Intructions¶
Adjust the root folders first
Setup environment¶
Google colab¶
In [ ]:
from google.colab import drive
drive.mount('/content/drive')
root_folder = "/content/drive/MyDrive/Licenta_INFO_selection"
Local folder¶
In [ ]:
import os
root_folder = "/Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_selection"
Data extraction and processing¶
Data extraction¶
Root data folder¶
In [2]:
import os
root_data_relative_path = "Data"
root_data_folder_path = os.path.join(root_folder, root_data_relative_path)
print("Root data folder path:", root_data_folder_path)
Root data folder path: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Data
Dukascopy data¶
Install plugin¶
In [ ]:
!pip install dukascopy-python
Download and save data¶
In [ ]:
import pandas as pd
import os
from datetime import datetime
import dukascopy_python as dp
from dukascopy_python.instruments import (
INSTRUMENT_FX_MAJORS_EUR_USD,
INSTRUMENT_FX_METALS_XAU_USD,
INSTRUMENT_IDX_AMERICA_E_SANDP_500
)
output_dir_Dukascopy = os.path.join(root_data_relative_path, "Dukascopy")
os.makedirs(output_dir_Dukascopy, exist_ok=True)
dukascopy_symbols = {
"EURUSD": INSTRUMENT_FX_MAJORS_EUR_USD,
"GOLD": INSTRUMENT_FX_METALS_XAU_USD,
"SP500": INSTRUMENT_IDX_AMERICA_E_SANDP_500
}
dukascopy_timeframes = {
"5min": dp.INTERVAL_MIN_5,
"hourly": dp.INTERVAL_HOUR_1,
}
start_date = datetime(2007, 1, 1)
end_date = datetime(2025, 7, 10)
dukascopy_data = {}
for label, instrument in dukascopy_symbols.items():
dukascopy_data[label] = {}
print(f"\nDownloading data for {label}")
for tf_label, (interval_value) in dukascopy_timeframes.items():
print(f" Timeframe: {tf_label}")
try:
df = dp.fetch(
instrument=instrument,
interval=interval_value,
offer_side=dp.OFFER_SIDE_BID,
start=start_date,
end=end_date
)
if not df.empty:
df = df.rename(columns={
"timestamp": "time",
"open": "open",
"high": "high",
"low": "low",
"close": "close",
"volume": "volume"
})
df = df.reset_index()
df["time"] = pd.to_datetime(df["timestamp"]).dt.strftime("%Y-%m-%d %H:%M")
df.set_index("time", inplace=True)
dukascopy_data[label][tf_label] = df
filename = f"{label}_{tf_label}.csv"
df.to_csv(os.path.join(output_dir_Dukascopy, filename))
else:
print(f" No data for {label} at {tf_label}")
except Exception as e:
print(f" Error downloading {label} at {tf_label}: {e}")
print("\nDownload and save complete.")
Alpaca data¶
Install plugin¶
In [ ]:
!pip install alpaca-py
Download and save data
In [ ]:
from alpaca.data.historical import StockHistoricalDataClient
from alpaca.data.requests import StockBarsRequest
from alpaca.data.timeframe import TimeFrame, TimeFrameUnit
from datetime import datetime
import pandas as pd
import os
output_dir_Alpaca = os.path.join(root_data_relative_path, "Alpaca")
os.makedirs(output_dir_Alpaca, exist_ok=True)
ALPACA_API_KEY = "PK2H3O5SAYN8TS1MONVR"
ALPACA_SECRET_KEY = "LSwqMQnADiZn47RIYA91KIiKg7K7B66YYEXPlTQP"
alpaca_stocks_tickers = ["AAPL", "MSFT", "GE", "BAC", "C"]
alpaca_timeframes = {
"daily": TimeFrame(1, TimeFrameUnit.Day),
"hourly": TimeFrame(1, TimeFrameUnit.Hour),
"5min": TimeFrame(5, TimeFrameUnit.Minute)
}
alpaca_start_date = datetime(2000, 1, 1)
alpaca_end_date = datetime(2025, 7, 10)
client = StockHistoricalDataClient(ALPACA_API_KEY, ALPACA_SECRET_KEY)
alpaca_stocks_data = {}
for ticker in alpaca_stocks_tickers:
alpaca_stocks_data[ticker] = {}
print(f"\n Downloading {ticker}")
for tf_label, tf_enum in alpaca_timeframes.items():
print(f"Timeframe: {tf_label}")
try:
req = StockBarsRequest(
symbol_or_symbols=ticker,
timeframe=tf_enum,
start=alpaca_start_date,
end=alpaca_end_date
)
bars = client.get_stock_bars(req)
df = bars.df
if not df.empty:
df = df.reset_index()
df["time"] = pd.to_datetime(df["timestamp"]).dt.strftime("%Y-%m-%d %H:%M")
df = df.rename(columns={
"open": "open",
"high": "high",
"low": "low",
"close": "close",
"volume": "volume"
})
alpaca_stocks_data[ticker][tf_label] = df
file_path = os.path.join(output_dir_Alpaca, f"{ticker}_{tf_label}.csv")
df.to_csv(file_path, index=False)
else:
print(f"No data for {ticker} [{tf_label}]")
except Exception as e:
print(f"Error downloading {ticker} [{tf_label}]: {e}")
print("\n Download and save complete.")
Binance data¶
Install plugin¶
In [ ]:
!pip install python-binance
Download and save data¶
In [ ]:
from binance.client import Client
from binance.exceptions import BinanceAPIException
import pandas as pd
import os
BINANCE_API_KEY = 'pENAEgy4xAygFaHCnUVsYnJRN9nsjIhvrG5IATVZMDapqXKTSJgsJC3bnqtdZNGy'
BINANCE_SECRET_KEY = 'BBO7CzGlNUgbqOhY27ofoVIK5dtM8dWLqKVMXYgkbpKEw3o4bdbvwySOcY8OxPaE'
client = Client(BINANCE_API_KEY, BINANCE_SECRET_KEY)
binance_coins = ["BTCUSDT"]
binance_timeframes = {
"daily": "1d",
"hourly": "1h",
"5min": "5m"
}
start_date = "8 Aug, 2017"
end_date = "10 Jul, 2025"
output_dir_Binance = os.path.join(root_data_relative_path, "Binance")
os.makedirs(output_dir_Binance, exist_ok=True)
binance_data = {}
for symbol in binance_coins:
binance_data[symbol] = {}
print(f"\n Downloading {symbol}")
for tf_label, tf_binance in binance_timeframes.items():
print(f" Timeframe: {tf_label}")
try:
klines = client.get_historical_klines(
symbol=symbol,
interval=tf_binance,
start_str=start_date,
end_str=end_date)
if not klines:
print(f" No data for {symbol} at {tf_label}")
continue
df = pd.DataFrame(klines, columns=[
"Open time", "Open", "High", "Low", "Close", "Volume",
"Close time", "Quote asset volume", "Number of trades",
"Taker buy base volume", "Taker buy quote volume", "Ignore"
])
df["time"] = pd.to_datetime(df["Open time"], unit='ms')
df["time"] = df["time"].dt.strftime("%Y-%m-%d %H:%M")
df.rename(columns={
"Open": "open",
"High": "high",
"Low": "low",
"Close": "close",
"Volume": "volume"
}, inplace=True)
filename = f"{symbol}_{tf_label}.csv"
df.to_csv(os.path.join(output_dir_Binance, filename), index=False)
binance_data[symbol][tf_label] = df
except BinanceAPIException as e:
print(f" Error: {e}")
print("\n Download and save complete.")
Yahoo data¶
Install plugin¶
In [ ]:
!pip install yfinance
Download and save data¶
In [ ]:
import yfinance as yf
import pandas as pd
import os
output_dir_Yahoo = os.path.join(root_data_relative_path, "Yahoo")
os.makedirs(output_dir_Yahoo, exist_ok=True)
yahoo_tickers = {
"AAPL": "Apple",
"MSFT": "Microsoft",
"GC=F": "Gold",
"^GSPC": "S&P 500",
"EURUSD=X": "EUR-USD",
"GE": "General Electric",
"BAC": "Bank of America",
"C": "Citigroup",
"BTC-USD": "Bitcoin"
}
yahoo_data_dict = {}
for symbol in yahoo_tickers:
print(f"Downloading data for {yahoo_tickers[symbol]} ({symbol})...")
df = yf.download(
symbol,
start="2000-01-01",
end="2025-07-10",
interval="1d",
auto_adjust=True,
progress=False)
if isinstance(df.columns, pd.MultiIndex):
df.columns = df.columns.get_level_values(0)
df = df.reset_index()
df = df.rename(columns={
"Date": "time",
"Open": "open",
"High": "high",
"Low": "low",
"Close": "close",
"Volume": "volume"
})
df["time"] = pd.to_datetime(df["time"]).dt.strftime("%Y-%m-%d %H:%M")
yahoo_data_dict[symbol] = df
safe_symbol = symbol.replace("=", "").replace("^", "")
file_path = os.path.join(output_dir_Yahoo, f"{safe_symbol}_daily.csv")
df.to_csv(file_path, index=False)
print(f"Data saved in: {output_dir_Yahoo}")
Install GARCH¶
In [ ]:
!pip install arch
!pip install tqdm
Extract GARCH and save the processed data¶
In [ ]:
import numpy as np
import pandas as pd
from pandas.tseries.offsets import DateOffset
from arch import arch_model
from tqdm import tqdm
import warnings
import os
import joblib
import pickle
alpaca_stocks_tickers = ["AAPL", "MSFT", "GE", "BAC", "C"]
alpaca_timeframes = ["daily", "hourly", "5min"]
dir_Alpaca = os.path.join(root_data_folder_path, "Alpaca")
def load_alpaca_data_from_csv(directory, ticker_list=alpaca_stocks_tickers, timeframes=alpaca_timeframes):
data_loaded = {}
for file in os.listdir(directory):
if file.endswith(".csv"):
try:
name_parts = file.replace(".csv", "").split("_")
ticker, tf = name_parts[0], name_parts[1]
if ticker_list and ticker not in ticker_list:
continue
if timeframes and tf not in timeframes:
continue
df = pd.read_csv(
os.path.join(directory, file),
index_col=1,
parse_dates=True,
date_format='mixed')
if ticker not in data_loaded:
data_loaded[ticker] = {}
df = df.sort_index()
data_loaded[ticker][tf] = df
except Exception as e:
print(f"Error loading {file}: {e}")
return data_loaded
alpaca_loaded_data = load_alpaca_data_from_csv(dir_Alpaca, alpaca_stocks_tickers, alpaca_timeframes)
print(alpaca_loaded_data["AAPL"]["daily"].head())
for ticker in alpaca_loaded_data:
print(f"{ticker}: {list(alpaca_loaded_data[ticker].keys())}")
binance_coins = ["BTCUSDT"]
binance_timeframes = ["daily", "hourly", "5min"]
dir_Binance = os.path.join(root_data_folder_path, "Binance")
def load_binance_data_from_csv(directory, coins=binance_coins, timeframes=binance_timeframes):
data = {}
for file in os.listdir(directory):
if file.endswith(".csv"):
try:
name_parts = file.replace(".csv", "").split("_")
ticker, tf = name_parts[0], name_parts[1]
if coins and ticker not in coins:
continue
if timeframes and tf not in timeframes:
continue
df = pd.read_csv(
os.path.join(directory, file),
index_col=12,
parse_dates=True,
date_format='mixed')
df = df.sort_index()
df.drop(columns=["Open time", "Close time"], inplace=True)
if ticker not in data:
data[ticker] = {}
data[ticker][tf] = df
except Exception as e:
print(f" Error loading {file}: {e}")
return data
binance_loaded_data = load_binance_data_from_csv(dir_Binance, binance_coins, binance_timeframes)
print(binance_loaded_data["BTCUSDT"]["daily"].head())
dir_Dukascopy = os.path.join(root_data_folder_path, "Dukascopy")
dukascopy_symbols = ["EURUSD", "GOLD", "SP500"]
dukascopy_timeframes = ["5min", "hourly","daily"]
def load_dukascopy_data(directory, symbols=None, timeframes=None):
data_loaded = {}
for file in os.listdir(directory):
if file.endswith(".csv"):
try:
name_parts = file.replace(".csv", "").split("_")
ticker, tf = name_parts[0], name_parts[1]
if symbols and ticker not in symbols:
continue
if timeframes and tf not in timeframes:
continue
df = pd.read_csv(os.path.join(directory, file), index_col=0, parse_dates=True)
if ticker not in data_loaded:
data_loaded[ticker] = {}
df = df.sort_index()
data_loaded[ticker][tf] = df
except Exception as e:
print(f"Error loading {file}: {e}")
return data_loaded
dukascopy_loaded_data = load_dukascopy_data(dir_Dukascopy, dukascopy_symbols, dukascopy_timeframes)
print(dukascopy_loaded_data["EURUSD"]["daily"].head())
all_data = {}
all_tickers = alpaca_stocks_tickers + binance_coins + dukascopy_symbols
selected_time_frames =["daily", "5min"]
for data_source in [alpaca_loaded_data, binance_loaded_data, dukascopy_loaded_data]:
for ticker, tf_dict in data_source.items():
if ticker not in all_data:
all_data[ticker] = {}
for tf_label, df in tf_dict.items():
all_data[ticker][tf_label] = df
cleaned_data_no_interpolation = {}
for ticker in all_tickers:
if ticker not in all_data:
continue
cleaned_data_no_interpolation[ticker] = {}
for tf in selected_time_frames:
if tf not in all_data[ticker]:
continue
df = all_data[ticker][tf].copy()
df.index = pd.to_datetime(df.index)
df.columns = [col.lower() for col in df.columns]
df = df[["open", "close", "high", "low", "volume"]]
df.index.name = "time"
df = df.sort_index()
if tf == "daily":
df = df[df["volume"] > 0]
df["scaled_log_return"] = np.log(df["close"] / df["close"].shift(1))
df = df.dropna()
cleaned_data_no_interpolation[ticker][tf] = df
print("Cleaned data without interpolation and computed scaled log returns for all selected timeframes.")
for ticker, tf_dict in cleaned_data_no_interpolation.items():
print(f"\nTicker: {ticker}")
for tf, df in tf_dict.items():
if not isinstance(df, pd.DataFrame) or df.empty:
print(f" Timeframe: {tf} – EMPTY or not a DataFrame")
continue
df = df.sort_index()
start_date = df.index.min()
end_date = df.index.max()
n_rows = len(df)
print(f" Timeframe: {tf}")
print(f" Start: {start_date}")
print(f" End: {end_date}")
print(f" Rows: {n_rows}")
cleaned_data_no_interpolation_with_variance = {}
missing_rv_stats = []
for ticker in all_tickers:
if ticker not in cleaned_data_no_interpolation:
continue
if "5min" not in cleaned_data_no_interpolation[ticker] or "daily" not in cleaned_data_no_interpolation[ticker]:
continue
df_5min = cleaned_data_no_interpolation[ticker]["5min"].copy()
df_daily = cleaned_data_no_interpolation[ticker]["daily"].copy()
#Problem specific to EURUSD
if ticker == "EURUSD":
select_date = "2012-01-12"
df_daily = df_daily[df_daily.index >= pd.to_datetime(start_date)]
if "scaled_log_return" not in df_5min.columns:
print(f"Skipping {ticker}: no scaled_log_return column in 5min data")
continue
df_5min.index = pd.to_datetime(df_5min.index)
df_5min["date"] = df_5min.index.date
daily_rv = df_5min.groupby("date")["scaled_log_return"].apply(lambda x: (x**2).sum()).rename("scaled_realized_variance")
df_daily.index = pd.to_datetime(df_daily.index)
df_daily["date"] = df_daily.index.date
df_daily = df_daily.merge(daily_rv, on="date", how="left")
df_daily = df_daily.set_index("date")
df_daily = df_daily.iloc[1:]
num_missing = df_daily["scaled_realized_variance"].isna().sum()
total_rows = len(df_daily)
missing_rv_stats.append({
"ticker": ticker,
"missing_rv_count": num_missing,
"total_rows": total_rows,
"missing_percentage": num_missing / total_rows * 100
})
#just 2 NAs get dropped here, one for gold, one for EURUSD
df_daily = df_daily.dropna()
cleaned_data_no_interpolation_with_variance[ticker] = {}
cleaned_data_no_interpolation_with_variance[ticker]["daily"] = df_daily
print("Realized variance added to daily data.")
missing_rv_df = pd.DataFrame(missing_rv_stats)
print(missing_rv_df)
lookback = DateOffset(years=1)
min_obs = 200
model_configs = [
("TGARCH", 1, 1)
]
data_with_garch_no_interpolation = {}
for ticker in tqdm(cleaned_data_no_interpolation_with_variance.keys(), desc="Processing tickers"):
if "daily" not in cleaned_data_no_interpolation_with_variance[ticker]:
continue
df_daily = cleaned_data_no_interpolation_with_variance[ticker]["daily"].copy()
if "scaled_log_return" not in df_daily.columns:
continue
df_daily.index = pd.to_datetime(df_daily.index)
df_daily = df_daily.sort_index().dropna()
scaled_log_returns = df_daily["scaled_log_return"]
start_eligible = df_daily.index[0] + lookback
eligible_dates = df_daily.index[df_daily.index >= start_eligible]
models_x = {f"x_{m}_{p}_{q}": dict(pred_var=[], bic=[], params=[], converged=[], warnings=[]) for m, p, q in model_configs}
models_y = {f"y_{m}_{p}_{q}": dict(pred_var=[], bic=[], params=[], converged=[], warnings=[]) for m, p, q in model_configs}
def fit_model_safe(model_func, model_type):
try:
with warnings.catch_warnings(record=True) as wlist:
warnings.simplefilter("always")
with np.errstate(all='ignore'):
model = model_func()
result = model.fit(disp="off", options={"maxiter": 1000})
warning_msg = "; ".join([str(w.message) for w in wlist]) if wlist else ""
forecast = result.forecast(horizon=1, reindex=False)
pred_var = forecast.variance.iloc[-1, 0]
converged = result.convergence_flag == 0
return pred_var, result.bic, result.params.to_dict(), converged, warning_msg
except Exception as e:
return np.nan, np.nan, {}, False, str(e)
for current_dt in eligible_dates:
# Backward only window
window_start = current_dt - lookback
window_data = scaled_log_returns.loc[window_start:current_dt - pd.Timedelta(days=1)]
window_data = window_data.replace([np.inf, -np.inf], np.nan).dropna()
if len(window_data) < min_obs or not np.isfinite(window_data).all():
for m in models_x.values():
m["pred_var"].append(np.nan)
m["bic"].append(np.nan)
m["params"].append({})
m["converged"].append(False)
m["warnings"].append("Invalid or insufficient window")
else:
for model_type, p, q in model_configs:
model_key = f"x_{model_type}_{p}_{q}"
def model_factory():
if model_type == "TGARCH":
return arch_model(window_data, vol='GARCH', p=p, q=q, dist='t')
elif model_type == "GARCH":
return arch_model(window_data, vol='GARCH', p=p, q=q, dist='normal')
elif model_type == "ARCH":
return arch_model(window_data, vol='ARCH', p=p, dist='normal')
elif model_type == "GJRGARCH":
return arch_model(window_data, vol='GARCH', p=p, o=1, q=q, dist='normal')
pred_var, bic, params, conv, warn = fit_model_safe(model_factory, model_type)
models_x[model_key]["pred_var"].append(pred_var)
models_x[model_key]["bic"].append(bic)
models_x[model_key]["params"].append(params)
models_x[model_key]["converged"].append(conv)
models_x[model_key]["warnings"].append(warn)
#Backward and forward window
fwd_end = current_dt + pd.Timedelta(days=27)
fwd_start = fwd_end - lookback
fwd_data = scaled_log_returns.loc[fwd_start:fwd_end]
fwd_data = fwd_data.replace([np.inf, -np.inf], np.nan).dropna()
if len(fwd_data) < min_obs or not np.isfinite(fwd_data).all():
for m in models_y.values():
m["pred_var"].append(np.nan)
m["bic"].append(np.nan)
m["params"].append({})
m["converged"].append(False)
m["warnings"].append("Invalid or insufficient window")
else:
for model_type, p, q in model_configs:
model_key = f"y_{model_type}_{p}_{q}"
def model_factory():
if model_type == "TGARCH":
return arch_model(fwd_data, vol='GARCH', p=p, q=q, dist='t')
elif model_type == "GARCH":
return arch_model(fwd_data, vol='GARCH', p=p, q=q, dist='normal')
elif model_type == "ARCH":
return arch_model(fwd_data, vol='ARCH', p=p, dist='normal')
elif model_type == "GJRGARCH":
return arch_model(fwd_data, vol='GARCH', p=p, o=1, q=q, dist='normal')
pred_var, bic, params, conv, warn = fit_model_safe(model_factory, model_type)
models_y[model_key]["pred_var"].append(pred_var)
models_y[model_key]["bic"].append(bic)
models_y[model_key]["params"].append(params)
models_y[model_key]["converged"].append(conv)
models_y[model_key]["warnings"].append(warn)
#Combine all into output dataframe
df_out = df_daily.loc[eligible_dates].copy()
def build_model_df(models_dict):
data = {}
for model_name, output in models_dict.items():
data[f"{model_name}_pred_variance"] = output["pred_var"]
data[f"{model_name}_BIC"] = output["bic"]
data[f"{model_name}_converged"] = output["converged"]
data[f"{model_name}_warnings"] = output["warnings"]
for param in set().union(*output["params"]):
data[f"{model_name}_param_{param}"] = [p.get(param, np.nan) for p in output["params"]]
return pd.DataFrame(data, index=df_out.index)
df_model_x = build_model_df(models_x)
df_model_y = build_model_df(models_y)
df_out = pd.concat([df_out, df_model_x, df_model_y], axis=1)
df_out = df_out.copy()
print(f"\n--- Missing forecast rate for {ticker} ---")
for model_key, model_dict in {**models_x, **models_y}.items():
pred_var_list = model_dict["pred_var"]
if len(pred_var_list) > 0:
nan_rate = np.isnan(pred_var_list).mean()
print(f"{ticker}: {nan_rate:.2%} NaNs in {model_key} forecast")
else:
print(f"{ticker}: No data processed for {model_key}")
data_with_garch_no_interpolation[ticker] = df_out
output_dir_Processed = os.path.join(root_data_folder_path, "Processed")
processed_dict = {}
standard_params = (
[f"alpha_{i}" for i in range(1, 3)] +
[f"beta_{i}" for i in range(1, 3)] +
["omega"] +
["mu"] +
["nu_tgarch"] +
[f"gamma_gjr_{i}" for i in range(1, 3)]
)
rows_with_large_variances = []
rows_only_with_large_variances = []
non_converged_rows = []
for ticker, df in data_with_garch_no_interpolation.items():
rows = []
for i, row in df.iterrows():
new_row = {
"date": i,
"open": row.get("open", np.nan),
"close": row.get("close", np.nan),
"high": row.get("high", np.nan),
"low": row.get("low", np.nan),
"lg_return": row.get("scaled_log_return", np.nan),
"volume": row.get("volume", np.nan),
"y_var": row.get("scaled_realized_variance", np.nan)
}
bics_x, pred_vars_x = {}, {}
bics_y, pred_vars_y = {}, {}
for model_type, p, q in model_configs:
base = f"{model_type}_{p}_{q}"
x_key = f"x_{base}"
y_key = f"y_{base}"
#X models
conv_x = row.get(f"{x_key}_converged", False)
pred_var_x = row.get(f"{x_key}_pred_variance", np.nan)
if conv_x and np.isfinite(pred_var_x):
bics_x[base] = row.get(f"{x_key}_BIC", np.nan)
pred_vars_x[base] = pred_var_x
new_row[f"{x_key.lower()}_var"] = pred_var_x if conv_x and np.isfinite(pred_var_x) else np.nan
#Y models
conv_y = row.get(f"{y_key}_converged", False)
pred_var_y = row.get(f"{y_key}_pred_variance", np.nan)
if conv_y and np.isfinite(pred_var_y):
bics_y[base] = row.get(f"{y_key}_BIC", np.nan)
pred_vars_y[base] = pred_var_y
new_row[f"{y_key.lower()}_var"] = pred_var_y if conv_y and np.isfinite(pred_var_y) else np.nan
for model_label, pred_vars in [("x", pred_vars_x), ("y", pred_vars_y)]:
converged_vars = list(pred_vars.values())
filtered_vars = [v for v in converged_vars if np.isfinite(v) and v < 1e10]
avg_var = np.mean(filtered_vars) if filtered_vars else 1e10
if any(v >= 1e10 for v in converged_vars):
rows_with_large_variances.append({"ticker": ticker, "date": i, "model_type": model_label})
if all(v >= 1e10 for v in converged_vars) and converged_vars:
rows_only_with_large_variances.append({"ticker": ticker, "date": i, "model_type": model_label})
if not pred_vars:
non_converged_rows.append({"ticker": ticker, "date": i, "model_type": model_label})
#Best model selection
bics = bics_x if model_label == "x" else bics_y
best_model = min(bics, key=bics.get) if bics else None
best_pred_var = pred_vars.get(best_model, avg_var)
new_row[f"{model_label}_best_model"] = best_model
new_row[f"{model_label}_best_pred_var"] = best_pred_var
for param in standard_params:
new_row[f"{model_label}_best_{param}"] = 0.0
if best_model:
param_prefix = f"{model_label}_{best_model}_param_"
for col in df.columns:
if col.startswith(param_prefix):
raw_param = col.split(param_prefix)[1]
value = row[col]
mapped = None
if raw_param.startswith("alpha["):
idx = int(raw_param[6:-1])
mapped = f"alpha_{idx}"
elif raw_param.startswith("beta["):
idx = int(raw_param[5:-1])
mapped = f"beta_{idx}"
elif raw_param == "omega":
mapped = "omega"
elif raw_param == "mu":
mapped = "mu"
elif raw_param.startswith("gamma["):
idx = int(raw_param[6:-1])
if "GJRGARCH" in best_model.upper():
mapped = f"gamma_gjr_{idx}"
elif raw_param == "nu":
mapped = "nu_tgarch"
if mapped and mapped in standard_params:
new_row[f"{model_label}_best_{mapped}"] = value
rows.append(new_row)
processed_dict[ticker] = pd.DataFrame(rows)
processed_dict[ticker].to_csv(
os.path.join(output_dir_Processed, f"processed_{ticker}.csv"),
index=False)
non_converged_df = pd.DataFrame(non_converged_rows)
processed_dict["AAPL"].info()
output_dir_Environment = os.path.join(root_folder, "Saved_objects")
os.makedirs(output_dir_Environment, exist_ok=True)
session_data_filename = os.path.join(output_dir_Environment, 'process_data_no_interpolation_session.pkl')
def is_picklable(obj):
try:
pickle.dumps(obj)
return True
except Exception:
return False
session_vars = {
k: v for k, v in globals().items()
if not k.startswith("__") and is_picklable(v)
}
#save session data
joblib.dump(session_vars, session_data_filename)
print(f"Session saved with {len(session_vars)} variables to:\n{session_data_filename}")
#load the saved session
session_data_filename = os.path.join(root_folder,"Saved_objects/process_data_no_interpolation_session.pkl")
session = joblib.load(session_data_filename)
globals().update(session)
Data processing¶
Processed data folder path¶
In [69]:
data_relative_path = "Data/Processed"
data_folder_path = os.path.join(root_folder, data_relative_path)
print("Data folder path:", data_folder_path)
Data folder path: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Data/Processed
Importing data and renaming columns¶
In [70]:
import pandas as pd
tickers = ["AAPL", "MSFT", "GE", "BAC", "C", "BTCUSDT", "EURUSD", "GOLD", "SP500"]
df_dict = {
ticker: pd.read_csv(f"{data_folder_path}/processed_{ticker}.csv", parse_dates=["date"])
for ticker in tickers
}
print("Data size:")
for ticker in tickers:
print(f"{ticker}: {df_dict[ticker].shape}")
df = df_dict[ticker]
df.rename(columns={
"y_lg_return": "lg_return",
"y_var": "rv"
}, inplace=True)
new_columns = {}
for col in df.columns:
if col.startswith("best_x_"):
new_columns[col] = col.replace("best_x_", "x_best_param_")
for col in df.columns:
if col.startswith("best_y_"):
new_columns[col] = col.replace("best_y_", "y_best_param_")
df.rename(columns=new_columns, inplace=True)
print("\nData types:")
df_dict["AAPL"].info()
Data size: AAPL: (2137, 32) MSFT: (2137, 32) GE: (2137, 32) BAC: (2137, 32) C: (2137, 32) BTCUSDT: (2518, 32) EURUSD: (3901, 32) GOLD: (5653, 32) SP500: (3585, 32) Data types: <class 'pandas.core.frame.DataFrame'> RangeIndex: 2137 entries, 0 to 2136 Data columns (total 32 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 date 2137 non-null datetime64[ns] 1 open 2137 non-null float64 2 close 2137 non-null float64 3 high 2137 non-null float64 4 low 2137 non-null float64 5 lg_return 2137 non-null float64 6 volume 2137 non-null float64 7 rv 2137 non-null float64 8 x_tgarch_1_1_var 2137 non-null float64 9 y_tgarch_1_1_var 2137 non-null float64 10 x_best_model 2137 non-null object 11 x_best_pred_var 2137 non-null float64 12 x_best_alpha_1 2137 non-null float64 13 x_best_alpha_2 2137 non-null float64 14 x_best_beta_1 2137 non-null float64 15 x_best_beta_2 2137 non-null float64 16 x_best_omega 2137 non-null float64 17 x_best_mu 2137 non-null float64 18 x_best_nu_tgarch 2137 non-null float64 19 x_best_gamma_gjr_1 2137 non-null float64 20 x_best_gamma_gjr_2 2137 non-null float64 21 y_best_model 2137 non-null object 22 y_best_pred_var 2137 non-null float64 23 y_best_alpha_1 2137 non-null float64 24 y_best_alpha_2 2137 non-null float64 25 y_best_beta_1 2137 non-null float64 26 y_best_beta_2 2137 non-null float64 27 y_best_omega 2137 non-null float64 28 y_best_mu 2137 non-null float64 29 y_best_nu_tgarch 2137 non-null float64 30 y_best_gamma_gjr_1 2137 non-null float64 31 y_best_gamma_gjr_2 2137 non-null float64 dtypes: datetime64[ns](1), float64(29), object(2) memory usage: 534.4+ KB
Dropping the "best model" columns which contain strings¶
In [71]:
for ticker in tickers:
df_dict[ticker] = df_dict[ticker].drop(["x_best_model", "y_best_model"], axis=1)
print("Dropped columns for: ", ticker)
Dropped columns for: AAPL Dropped columns for: MSFT Dropped columns for: GE Dropped columns for: BAC Dropped columns for: C Dropped columns for: BTCUSDT Dropped columns for: EURUSD Dropped columns for: GOLD Dropped columns for: SP500
Dropping some overlapping columns ONLY when working with a single GARCH¶
In [72]:
df_dict["EURUSD"].columns
for ticker in tickers:
df_dict[ticker] = df_dict[ticker].drop(["x_tgarch_1_1_var", "y_tgarch_1_1_var"], axis=1)
print("Dropped columns for: ", ticker)
Dropped columns for: AAPL Dropped columns for: MSFT Dropped columns for: GE Dropped columns for: BAC Dropped columns for: C Dropped columns for: BTCUSDT Dropped columns for: EURUSD Dropped columns for: GOLD Dropped columns for: SP500
Trasnfoming predicted var columns to log scale¶
In [73]:
import numpy as np
import pandas as pd
cols = ["x_best_pred_var"]
#cols = ["x_best_pred_var", "y_best_pred_var"]
#cols = ["x_best_pred_var", "y_best_pred_var", "rv"]
for ticker in tickers:
df = df_dict[ticker]
for c in cols:
if c in df:
df.loc[:, c] = np.log(df[c].astype(float).clip(lower=1e-8))
Helper function to remove constant columns¶
They might emerge for some GARCH parameter.
In [74]:
import pandas as pd
def remove_constant_columns_from_df(df, ticker=None):
constant_cols = [col for col in df.columns if df[col].nunique(dropna=False) == 1]
if constant_cols:
print(f"\n{ticker or 'DataFrame'} — Constant columns removed:")
for col in constant_cols:
print(f" - {col}")
return df.drop(columns=constant_cols)
for ticker in df_dict:
df_dict[ticker] = remove_constant_columns_from_df(df_dict[ticker], ticker=ticker)
AAPL — Constant columns removed: - x_best_alpha_2 - x_best_beta_2 - x_best_gamma_gjr_1 - x_best_gamma_gjr_2 - y_best_alpha_2 - y_best_beta_2 - y_best_gamma_gjr_1 - y_best_gamma_gjr_2 MSFT — Constant columns removed: - x_best_alpha_2 - x_best_beta_2 - x_best_gamma_gjr_1 - x_best_gamma_gjr_2 - y_best_alpha_2 - y_best_beta_2 - y_best_gamma_gjr_1 - y_best_gamma_gjr_2 GE — Constant columns removed: - x_best_alpha_2 - x_best_beta_2 - x_best_gamma_gjr_1 - x_best_gamma_gjr_2 - y_best_alpha_2 - y_best_beta_2 - y_best_gamma_gjr_1 - y_best_gamma_gjr_2 BAC — Constant columns removed: - x_best_alpha_2 - x_best_beta_2 - x_best_gamma_gjr_1 - x_best_gamma_gjr_2 - y_best_alpha_2 - y_best_beta_2 - y_best_gamma_gjr_1 - y_best_gamma_gjr_2 C — Constant columns removed: - x_best_alpha_2 - x_best_beta_2 - x_best_gamma_gjr_1 - x_best_gamma_gjr_2 - y_best_alpha_2 - y_best_beta_2 - y_best_gamma_gjr_1 - y_best_gamma_gjr_2 BTCUSDT — Constant columns removed: - x_best_alpha_2 - x_best_beta_2 - x_best_gamma_gjr_1 - x_best_gamma_gjr_2 - y_best_alpha_2 - y_best_beta_2 - y_best_gamma_gjr_1 - y_best_gamma_gjr_2 EURUSD — Constant columns removed: - x_best_alpha_2 - x_best_beta_2 - x_best_gamma_gjr_1 - x_best_gamma_gjr_2 - y_best_alpha_2 - y_best_beta_2 - y_best_gamma_gjr_1 - y_best_gamma_gjr_2 GOLD — Constant columns removed: - x_best_alpha_2 - x_best_beta_2 - x_best_gamma_gjr_1 - x_best_gamma_gjr_2 - y_best_alpha_2 - y_best_beta_2 - y_best_gamma_gjr_1 - y_best_gamma_gjr_2 SP500 — Constant columns removed: - x_best_alpha_2 - x_best_beta_2 - x_best_gamma_gjr_1 - x_best_gamma_gjr_2 - y_best_alpha_2 - y_best_beta_2 - y_best_gamma_gjr_1 - y_best_gamma_gjr_2
Helper function to check for very large values¶
We had some problems with EGARCH variacnes and parameters so we removed them from the data pipeline
In [75]:
import pandas as pd
def find_large_values(df_dict, threshold=1e6):
for ticker, df in df_dict.items():
print(f"\nChecking ticker: {ticker}")
for col in df.columns:
if pd.api.types.is_numeric_dtype(df[col]):
max_val = df[col].max(skipna=True)
min_val = df[col].min(skipna=True)
if abs(max_val) > threshold or abs(min_val) > threshold:
print(f"Column '{col}' has large values:")
print(f"Max: {max_val}")
print(f"Min: {min_val}")
else:
print(f"Column '{col}' OK!")
find_large_values(df_dict, threshold=1e10)
Checking ticker: AAPL Column 'open' OK! Column 'close' OK! Column 'high' OK! Column 'low' OK! Column 'lg_return' OK! Column 'volume' OK! Column 'rv' OK! Column 'x_best_pred_var' OK! Column 'x_best_alpha_1' OK! Column 'x_best_beta_1' OK! Column 'x_best_omega' OK! Column 'x_best_mu' OK! Column 'x_best_nu_tgarch' OK! Column 'y_best_pred_var' OK! Column 'y_best_alpha_1' OK! Column 'y_best_beta_1' OK! Column 'y_best_omega' OK! Column 'y_best_mu' OK! Column 'y_best_nu_tgarch' OK! Checking ticker: MSFT Column 'open' OK! Column 'close' OK! Column 'high' OK! Column 'low' OK! Column 'lg_return' OK! Column 'volume' OK! Column 'rv' OK! Column 'x_best_pred_var' OK! Column 'x_best_alpha_1' OK! Column 'x_best_beta_1' OK! Column 'x_best_omega' OK! Column 'x_best_mu' OK! Column 'x_best_nu_tgarch' OK! Column 'y_best_pred_var' OK! Column 'y_best_alpha_1' OK! Column 'y_best_beta_1' OK! Column 'y_best_omega' OK! Column 'y_best_mu' OK! Column 'y_best_nu_tgarch' OK! Checking ticker: GE Column 'open' OK! Column 'close' OK! Column 'high' OK! Column 'low' OK! Column 'lg_return' OK! Column 'volume' OK! Column 'rv' OK! Column 'x_best_pred_var' OK! Column 'x_best_alpha_1' OK! Column 'x_best_beta_1' OK! Column 'x_best_omega' OK! Column 'x_best_mu' OK! Column 'x_best_nu_tgarch' OK! Column 'y_best_pred_var' OK! Column 'y_best_alpha_1' OK! Column 'y_best_beta_1' OK! Column 'y_best_omega' OK! Column 'y_best_mu' OK! Column 'y_best_nu_tgarch' OK! Checking ticker: BAC Column 'open' OK! Column 'close' OK! Column 'high' OK! Column 'low' OK! Column 'lg_return' OK! Column 'volume' OK! Column 'rv' OK! Column 'x_best_pred_var' OK! Column 'x_best_alpha_1' OK! Column 'x_best_beta_1' OK! Column 'x_best_omega' OK! Column 'x_best_mu' OK! Column 'x_best_nu_tgarch' OK! Column 'y_best_pred_var' OK! Column 'y_best_alpha_1' OK! Column 'y_best_beta_1' OK! Column 'y_best_omega' OK! Column 'y_best_mu' OK! Column 'y_best_nu_tgarch' OK! Checking ticker: C Column 'open' OK! Column 'close' OK! Column 'high' OK! Column 'low' OK! Column 'lg_return' OK! Column 'volume' OK! Column 'rv' OK! Column 'x_best_pred_var' OK! Column 'x_best_alpha_1' OK! Column 'x_best_beta_1' OK! Column 'x_best_omega' OK! Column 'x_best_mu' OK! Column 'x_best_nu_tgarch' OK! Column 'y_best_pred_var' OK! Column 'y_best_alpha_1' OK! Column 'y_best_beta_1' OK! Column 'y_best_omega' OK! Column 'y_best_mu' OK! Column 'y_best_nu_tgarch' OK! Checking ticker: BTCUSDT Column 'open' OK! Column 'close' OK! Column 'high' OK! Column 'low' OK! Column 'lg_return' OK! Column 'volume' OK! Column 'rv' OK! Column 'x_best_pred_var' OK! Column 'x_best_alpha_1' OK! Column 'x_best_beta_1' OK! Column 'x_best_omega' OK! Column 'x_best_mu' OK! Column 'x_best_nu_tgarch' OK! Column 'y_best_pred_var' OK! Column 'y_best_alpha_1' OK! Column 'y_best_beta_1' OK! Column 'y_best_omega' OK! Column 'y_best_mu' OK! Column 'y_best_nu_tgarch' OK! Checking ticker: EURUSD Column 'open' OK! Column 'close' OK! Column 'high' OK! Column 'low' OK! Column 'lg_return' OK! Column 'volume' OK! Column 'rv' OK! Column 'x_best_pred_var' OK! Column 'x_best_alpha_1' OK! Column 'x_best_beta_1' OK! Column 'x_best_omega' OK! Column 'x_best_mu' OK! Column 'x_best_nu_tgarch' OK! Column 'y_best_pred_var' OK! Column 'y_best_alpha_1' OK! Column 'y_best_beta_1' OK! Column 'y_best_omega' OK! Column 'y_best_mu' OK! Column 'y_best_nu_tgarch' OK! Checking ticker: GOLD Column 'open' OK! Column 'close' OK! Column 'high' OK! Column 'low' OK! Column 'lg_return' OK! Column 'volume' OK! Column 'rv' OK! Column 'x_best_pred_var' OK! Column 'x_best_alpha_1' OK! Column 'x_best_beta_1' OK! Column 'x_best_omega' OK! Column 'x_best_mu' OK! Column 'x_best_nu_tgarch' OK! Column 'y_best_pred_var' OK! Column 'y_best_alpha_1' OK! Column 'y_best_beta_1' OK! Column 'y_best_omega' OK! Column 'y_best_mu' OK! Column 'y_best_nu_tgarch' OK! Checking ticker: SP500 Column 'open' OK! Column 'close' OK! Column 'high' OK! Column 'low' OK! Column 'lg_return' OK! Column 'volume' OK! Column 'rv' OK! Column 'x_best_pred_var' OK! Column 'x_best_alpha_1' OK! Column 'x_best_beta_1' OK! Column 'x_best_omega' OK! Column 'x_best_mu' OK! Column 'x_best_nu_tgarch' OK! Column 'y_best_pred_var' OK! Column 'y_best_alpha_1' OK! Column 'y_best_beta_1' OK! Column 'y_best_omega' OK! Column 'y_best_mu' OK! Column 'y_best_nu_tgarch' OK!
Saving the unstructured data for GARCH¶
In [76]:
import os
import pickle
objects_relative_path = "Saved_objects"
save_data_object_path = os.path.join(root_folder, objects_relative_path)
print("Object folder path:", save_data_object_path)
os.makedirs(save_data_object_path, exist_ok=True)
unstructured_dictionary_file_path = os.path.join(save_data_object_path, "unstructured_data_dict.pkl")
with open(unstructured_dictionary_file_path, "wb") as f:
pickle.dump(df_dict, f)
print(f"Unstructured data dictionary saved to: {unstructured_dictionary_file_path}")
Object folder path: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects Unstructured data dictionary saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/unstructured_data_dict.pkl
Put data in the right format¶
We have to separate the X features into time and others (price, variance, GARCH parameters) as these will need to be treated differently when it comes to normalization.
In [77]:
from typing import Union, Dict, Optional
import numpy as np
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
def df_to_raw_X_y_dictionary(data,
ticker="Unnamed ticker",
window_size=60,
horizon=60,
rv_y = True,
lg_return_y = False,
garch_var_x = True,
best_param_x=True,
best_param_y=True,
volume_x=True,
x_lg_return=False,
best_var_x = False,
best_var_y = False
):
assert rv_y or lg_return_y, "At least one of rv_y or lg_return_y must be True"
df_copy = data.copy()
if len(df_copy) < window_size + horizon:
print(f"Skipping {ticker}: Not enough data.")
return None
if "date" not in df_copy.columns or not pd.api.types.is_datetime64_any_dtype(df_copy["date"]):
raise ValueError(f"Data for {ticker} must include a 'date' column in datetime format.")
# Time-based features
df_copy["x_time_day_of_week"] = df_copy["date"].dt.dayofweek
df_copy["x_time_day_of_month"] = df_copy["date"].dt.day
df_copy["x_time_day_of_year"] = df_copy["date"].dt.dayofyear
df_copy["x_time_week_of_year"] = df_copy["date"].dt.isocalendar().week.astype(int)
df_copy["x_time_month_of_year"] = df_copy["date"].dt.month
df_copy["x_time_is_quarter_end"] = df_copy["date"].dt.is_quarter_end.astype(int)
df_copy["x_time_years_since_2000"] = df_copy["date"].dt.year - 2000
df_copy["x_time_days_since_2000"] = (df_copy["date"] - pd.Timestamp("2000-01-01")).dt.days
df_copy["x_time_months_since_2000"] = (df_copy["x_time_years_since_2000"] * df_copy["x_time_month_of_year"] - 1)
# Sin/cos cyclical re-codings for time features
df_copy["x_time_dow_sin"] = np.sin(2 * np.pi * df_copy["x_time_day_of_week"] / 7)
df_copy["x_time_dow_cos"] = np.cos(2 * np.pi * df_copy["x_time_day_of_week"] / 7)
df_copy["x_time_dom_sin"] = np.sin(2 * np.pi * df_copy["x_time_day_of_month"] / 31)
df_copy["x_time_dom_cos"] = np.cos(2 * np.pi * df_copy["x_time_day_of_month"] / 31)
df_copy["x_time_doy_sin"] = np.sin(2 * np.pi * df_copy["x_time_day_of_year"] / 365)
df_copy["x_time_doy_cos"] = np.cos(2 * np.pi * df_copy["x_time_day_of_year"] / 365)
df_copy["x_time_woy_sin"] = np.sin(2 * np.pi * df_copy["x_time_week_of_year"] / 52)
df_copy["x_time_woy_cos"] = np.cos(2 * np.pi * df_copy["x_time_week_of_year"] / 52)
df_copy["x_time_moy_sin"] = np.sin(2 * np.pi * df_copy["x_time_month_of_year"] / 12)
df_copy["x_time_moy_cos"] = np.cos(2 * np.pi * df_copy["x_time_month_of_year"] / 12)
# Normalize trend time features
trend_features = ["x_time_years_since_2000", "x_time_days_since_2000", "x_time_months_since_2000"]
df_copy[trend_features] = MinMaxScaler().fit_transform(df_copy[trend_features])
X_time_features = [
"x_time_dow_sin", "x_time_dow_cos", "x_time_dom_sin", "x_time_dom_cos",
"x_time_doy_sin", "x_time_doy_cos", "x_time_woy_sin", "x_time_woy_cos",
"x_time_moy_sin", "x_time_moy_cos", "x_time_is_quarter_end",
"x_time_years_since_2000", "x_time_days_since_2000", "x_time_months_since_2000"
]
if x_lg_return:
X_other_features = ["rv", "lg_return"]
else:
X_other_features = ["rv"]
if volume_x:
X_other_features.append("volume")
if garch_var_x:
X_other_features += [col for col in df_copy.columns if col.startswith("x_") and not col.startswith("x_best_") and col.endswith("_var")]
if best_param_x:
X_other_features += [col for col in df_copy.columns if col.startswith("x_best_") and not col.endswith("_var")]
if best_var_x:
X_other_features += [col for col in df_copy.columns if col.startswith("x_best_") and col.endswith("_var")]
y_targets=[]
if rv_y:
y_targets.append("rv")
if lg_return_y:
y_targets.append("lg_return")
if best_var_y:
y_targets += [col for col in df_copy.columns if col.startswith("y_best_") and col.endswith("_var")]
if best_param_y:
y_targets += [col for col in df_copy.columns if col.startswith("y_best_") and not col.endswith("_var")]
required_columns = X_time_features + X_other_features + y_targets
df_copy = df_copy.dropna(subset=required_columns).reset_index(drop=True)
X_other_all, X_time_all, y_all = [], [], []
for i in range(window_size, len(df_copy) - horizon + 1):
# Input: t-27:t-0
other_window = df_copy.loc[i - window_size:i - 1, X_other_features].values
time_window = df_copy.loc[i - window_size:i - 1, X_time_features].values
# Targets: t1:t+28
y_horizon = df_copy.loc[i:i + horizon - 1, y_targets].values
X_other_all.append(other_window)
X_time_all.append(time_window)
y_all.append(y_horizon)
X_other_all = np.asarray(X_other_all, dtype=np.float64)
X_time_all = np.asarray(X_time_all, dtype=np.float64)
y_all = np.asarray(y_all, dtype=np.float64)
X_other_columns = [f"{f}_t-{window_size - t - 1}" for t in range(window_size) for f in X_other_features]
X_time_columns = [f"{f}_t-{window_size - t - 1}" for t in range(window_size) for f in X_time_features]
y_columns = [f"{f}_t+{t+1}" for t in range(horizon) for f in y_targets]
return {
"X_other": X_other_all,
"X_time": X_time_all,
"y": y_all,
"X_other_columns": X_other_columns,
"X_time_columns": X_time_columns,
"y_columns": y_columns
}
Data format v1¶
In [78]:
structured_data_dict = {}
for ticker, df in df_dict.items():
ticker_data = df_to_raw_X_y_dictionary(df, ticker=ticker, lg_return_y=True,rv_y = True, garch_var_x= False, best_param_x=True, best_param_y=True, volume_x=True)
if ticker_data is not None:
structured_data_dict[ticker] = ticker_data
print("Structured data dictionary created")
Structured data dictionary created
Saving data to objects to be used later¶
In [79]:
import os
import pickle
objects_relative_path = "Saved_objects"
save_data_object_path = os.path.join(root_folder, objects_relative_path)
print("Object folder path:", save_data_object_path)
os.makedirs(save_data_object_path, exist_ok=True)
data_dictionary_file_path = os.path.join(save_data_object_path, "structured_data_dict.pkl")
with open(data_dictionary_file_path, "wb") as f:
pickle.dump(structured_data_dict, f)
print(f"Data dictionary saved to: {data_dictionary_file_path}")
Object folder path: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects Data dictionary saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/structured_data_dict.pkl
Extracting and saving some additional data compatible with Benchmark models¶
In [80]:
structured_data_dict_2 = {}
for ticker, df in df_dict.items():
ticker_data = df_to_raw_X_y_dictionary(df, ticker=ticker, lg_return_y=True,rv_y = True, garch_var_x= False, best_param_x=True, best_param_y=True, volume_x=False, )
if ticker_data is not None:
structured_data_dict_2[ticker] = ticker_data
print("Structured data dictionary 2 created")
data_dictionary_2_file_path = os.path.join(save_data_object_path, "structured_data_dict_2.pkl")
with open(data_dictionary_2_file_path, "wb") as f:
pickle.dump(structured_data_dict_2, f)
print(f"Data dictionary 2 saved to: {data_dictionary_2_file_path}")
Structured data dictionary 2 created Data dictionary 2 saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/structured_data_dict_2.pkl
Data v3¶
In [81]:
structured_data_dict_3 = {}
for ticker, df in df_dict.items():
ticker_data = df_to_raw_X_y_dictionary(df, ticker=ticker, lg_return_y=True,rv_y = True, garch_var_x= False, best_param_x=False, best_param_y=True, volume_x=False, )
if ticker_data is not None:
structured_data_dict_3[ticker] = ticker_data
print("Structured data dictionary 3 created")
data_dictionary_3_file_path = os.path.join(save_data_object_path, "structured_data_dict_3.pkl")
with open(data_dictionary_3_file_path, "wb") as f:
pickle.dump(structured_data_dict_3, f)
print(f"Data dictionary 3 saved to: {data_dictionary_3_file_path}")
Structured data dictionary 3 created Data dictionary 3 saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/structured_data_dict_3.pkl
Data v4¶
In [82]:
structured_data_dict_4 = {}
for ticker, df in df_dict.items():
ticker_data = df_to_raw_X_y_dictionary(df, ticker=ticker, lg_return_y=True,rv_y = True, garch_var_x= False, best_param_x=False, best_param_y=False, volume_x=False, )
if ticker_data is not None:
structured_data_dict_4[ticker] = ticker_data
print("Structured data dictionary 4 created")
data_dictionary_4_file_path = os.path.join(save_data_object_path, "structured_data_dict_4.pkl")
with open(data_dictionary_4_file_path, "wb") as f:
pickle.dump(structured_data_dict_4, f)
print(f"Data dictionary 4 saved to: {data_dictionary_4_file_path}")
Structured data dictionary 4 created Data dictionary 4 saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/structured_data_dict_4.pkl
Data v5¶
In [83]:
structured_data_dict_5 = {}
for ticker, df in df_dict.items():
ticker_data = df_to_raw_X_y_dictionary(df, ticker=ticker, lg_return_y=False, rv_y=True, garch_var_x=False, best_param_x=False, best_param_y=False, volume_x=False, x_lg_return=False)
if ticker_data is not None:
structured_data_dict_5[ticker] = ticker_data
print("Structured data dictionary 5 created")
data_dictionary_5_file_path = os.path.join(save_data_object_path, "structured_data_dict_5.pkl")
with open(data_dictionary_5_file_path, "wb") as f:
pickle.dump(structured_data_dict_5, f)
print(f"Data dictionary 5 saved to: {data_dictionary_5_file_path}")
Structured data dictionary 5 created Data dictionary 5 saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/structured_data_dict_5.pkl
Data v6¶
In [84]:
structured_data_dict_6 = {}
for ticker, df in df_dict.items():
ticker_data = df_to_raw_X_y_dictionary(df, ticker=ticker, lg_return_y=False, rv_y=True, garch_var_x=True, best_param_x=True, best_param_y=True, volume_x=False, x_lg_return=False)
if ticker_data is not None:
structured_data_dict_6[ticker] = ticker_data
print("Structured data dictionary 6 created")
data_dictionary_6_file_path = os.path.join(save_data_object_path, "structured_data_dict_6.pkl")
with open(data_dictionary_6_file_path, "wb") as f:
pickle.dump(structured_data_dict_6, f)
print(f"Data dictionary 6 saved to: {data_dictionary_6_file_path}")
Structured data dictionary 6 created Data dictionary 6 saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/structured_data_dict_6.pkl
Data v7¶
In [85]:
structured_data_dict_7 = {}
for ticker, df in df_dict.items():
ticker_data = df_to_raw_X_y_dictionary(df, ticker=ticker, lg_return_y=False, rv_y=True, garch_var_x=False, best_param_x=True, best_param_y=True, volume_x=False, x_lg_return=False)
if ticker_data is not None:
structured_data_dict_7[ticker] = ticker_data
print("Structured data dictionary 7 created")
data_dictionary_7_file_path = os.path.join(save_data_object_path, "structured_data_dict_7.pkl")
with open(data_dictionary_7_file_path, "wb") as f:
pickle.dump(structured_data_dict_7, f)
print(f"Data dictionary 7 saved to: {data_dictionary_7_file_path}")
Structured data dictionary 7 created Data dictionary 7 saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/structured_data_dict_7.pkl
Data v8¶
In [86]:
structured_data_dict_8 = {}
for ticker, df in df_dict.items():
ticker_data = df_to_raw_X_y_dictionary(df, ticker=ticker, lg_return_y=False, rv_y=True, garch_var_x=False, best_param_x=False, best_param_y=True, volume_x=False, x_lg_return=False)
if ticker_data is not None:
structured_data_dict_8[ticker] = ticker_data
print("Structured data dictionary 8 created")
data_dictionary_8_file_path = os.path.join(save_data_object_path, "structured_data_dict_8.pkl")
with open(data_dictionary_8_file_path, "wb") as f:
pickle.dump(structured_data_dict_8, f)
print(f"Data dictionary 8 saved to: {data_dictionary_8_file_path}")
Structured data dictionary 8 created Data dictionary 8 saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/structured_data_dict_8.pkl
Data v9¶
In [87]:
structured_data_dict_9 = {}
for ticker, df in df_dict.items():
ticker_data = df_to_raw_X_y_dictionary(df, ticker=ticker, lg_return_y=False, rv_y=True, garch_var_x=False, best_param_x=False, best_param_y=False, volume_x=False, x_lg_return=False, best_var_x=True, best_var_y=True)
if ticker_data is not None:
structured_data_dict_9[ticker] = ticker_data
print("Structured data dictionary 9 created")
data_dictionary_9_file_path = os.path.join(save_data_object_path, "structured_data_dict_9.pkl")
with open(data_dictionary_9_file_path, "wb") as f:
pickle.dump(structured_data_dict_9, f)
print(f"Data dictionary 9 saved to: {data_dictionary_9_file_path}")
Structured data dictionary 9 created Data dictionary 9 saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/structured_data_dict_9.pkl
Data v10¶
In [142]:
structured_data_dict_10 = {}
for ticker, df in df_dict.items():
ticker_data = df_to_raw_X_y_dictionary(df, ticker=ticker, lg_return_y=True, rv_y=True, garch_var_x=False, best_param_x=False, best_param_y=False, volume_x=False, x_lg_return=True, best_var_x=False, best_var_y=False)
if ticker_data is not None:
structured_data_dict_10[ticker] = ticker_data
print("Structured data dictionary 10 created")
data_dictionary_10_file_path = os.path.join(save_data_object_path, "structured_data_dict_10.pkl")
with open(data_dictionary_10_file_path, "wb") as f:
pickle.dump(structured_data_dict_10, f)
print(f"Data dictionary 10 saved to: {data_dictionary_10_file_path}")
Structured data dictionary 10 created Data dictionary 10 saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/structured_data_dict_10.pkl
Data v11¶
In [89]:
structured_data_dict_11 = {}
for ticker, df in df_dict.items():
ticker_data = df_to_raw_X_y_dictionary(df, ticker=ticker, lg_return_y=False, rv_y=True, garch_var_x=False, best_param_x=True, best_param_y=False, volume_x=False, x_lg_return=False, best_var_x=True, best_var_y=False)
if ticker_data is not None:
structured_data_dict_11[ticker] = ticker_data
print("Structured data dictionary 11 created")
data_dictionary_11_file_path = os.path.join(save_data_object_path, "structured_data_dict_11.pkl")
with open(data_dictionary_11_file_path, "wb") as f:
pickle.dump(structured_data_dict_11, f)
print(f"Data dictionary 11 saved to: {data_dictionary_11_file_path}")
Structured data dictionary 11 created Data dictionary 11 saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/structured_data_dict_11.pkl
Helper function to check data integrity¶
Needed becasue it is called when training and testing models
In [90]:
import numpy as np
def check_data_issues(X, label="Array"):
print(f"Checking {label}:")
print("Shape:", X.shape)
has_nan = np.isnan(X).any()
has_posinf = np.isposinf(X).any()
has_neginf = np.isneginf(X).any()
has_inf = np.isinf(X).any()
min_val = np.nanmin(X)
max_val = np.nanmax(X)
print(f" Contains NaN values: {has_nan}")
print(f" Contains +inf values: {has_posinf}")
print(f" Contains -inf values: {has_neginf}")
print(f" Contains any infinities: {has_inf}")
print(" Max value: ", max_val)
print(" Min value: ", min_val)
Checking some examples of the data to see that it looks good¶
In [91]:
aapl_X_price = structured_data_dict["AAPL"]["X_other"]
aapl_X_price_columns = structured_data_dict["AAPL"]["X_other_columns"]
aapl_X_time = structured_data_dict["AAPL"]["X_time"]
aapl_X_time_columns = structured_data_dict["AAPL"]["X_time_columns"]
aapl_y = structured_data_dict["AAPL"]["y"]
aapl_y_columns = structured_data_dict["AAPL"]["y_columns"]
print("\n --- AAPL X_price columns: ---")
for col_name in aapl_X_price_columns:
print(col_name)
print("\n --- AAPL X_time columns: ---")
for col_name in aapl_X_time_columns:
print(col_name)
print("\n ---AAPL y columns:---")
for col_name in aapl_y_columns:
print(col_name)
check_data_issues(aapl_X_price, label="aapl_price")
check_data_issues(aapl_X_time, label="aapl_time")
check_data_issues(aapl_y, label="aapl_y")
print("\n shape(X) for AAPL:")
print(aapl_X_price.shape)
print("\n X for AAPL:")
print(aapl_X_price)
print("\n shape(Time) for AAPL:")
print(aapl_X_time.shape)
print("\n Time for AAPL:")
print(aapl_X_time)
print("\n shape(Y) for AAPL:")
print(aapl_y.shape)
print("\n Y for AAPL:")
print(aapl_y)
--- AAPL X_price columns: ---
rv_t-59
volume_t-59
x_best_alpha_1_t-59
x_best_beta_1_t-59
x_best_omega_t-59
x_best_mu_t-59
x_best_nu_tgarch_t-59
rv_t-58
volume_t-58
x_best_alpha_1_t-58
x_best_beta_1_t-58
x_best_omega_t-58
x_best_mu_t-58
x_best_nu_tgarch_t-58
rv_t-57
volume_t-57
x_best_alpha_1_t-57
x_best_beta_1_t-57
x_best_omega_t-57
x_best_mu_t-57
x_best_nu_tgarch_t-57
rv_t-56
volume_t-56
x_best_alpha_1_t-56
x_best_beta_1_t-56
x_best_omega_t-56
x_best_mu_t-56
x_best_nu_tgarch_t-56
rv_t-55
volume_t-55
x_best_alpha_1_t-55
x_best_beta_1_t-55
x_best_omega_t-55
x_best_mu_t-55
x_best_nu_tgarch_t-55
rv_t-54
volume_t-54
x_best_alpha_1_t-54
x_best_beta_1_t-54
x_best_omega_t-54
x_best_mu_t-54
x_best_nu_tgarch_t-54
rv_t-53
volume_t-53
x_best_alpha_1_t-53
x_best_beta_1_t-53
x_best_omega_t-53
x_best_mu_t-53
x_best_nu_tgarch_t-53
rv_t-52
volume_t-52
x_best_alpha_1_t-52
x_best_beta_1_t-52
x_best_omega_t-52
x_best_mu_t-52
x_best_nu_tgarch_t-52
rv_t-51
volume_t-51
x_best_alpha_1_t-51
x_best_beta_1_t-51
x_best_omega_t-51
x_best_mu_t-51
x_best_nu_tgarch_t-51
rv_t-50
volume_t-50
x_best_alpha_1_t-50
x_best_beta_1_t-50
x_best_omega_t-50
x_best_mu_t-50
x_best_nu_tgarch_t-50
rv_t-49
volume_t-49
x_best_alpha_1_t-49
x_best_beta_1_t-49
x_best_omega_t-49
x_best_mu_t-49
x_best_nu_tgarch_t-49
rv_t-48
volume_t-48
x_best_alpha_1_t-48
x_best_beta_1_t-48
x_best_omega_t-48
x_best_mu_t-48
x_best_nu_tgarch_t-48
rv_t-47
volume_t-47
x_best_alpha_1_t-47
x_best_beta_1_t-47
x_best_omega_t-47
x_best_mu_t-47
x_best_nu_tgarch_t-47
rv_t-46
volume_t-46
x_best_alpha_1_t-46
x_best_beta_1_t-46
x_best_omega_t-46
x_best_mu_t-46
x_best_nu_tgarch_t-46
rv_t-45
volume_t-45
x_best_alpha_1_t-45
x_best_beta_1_t-45
x_best_omega_t-45
x_best_mu_t-45
x_best_nu_tgarch_t-45
rv_t-44
volume_t-44
x_best_alpha_1_t-44
x_best_beta_1_t-44
x_best_omega_t-44
x_best_mu_t-44
x_best_nu_tgarch_t-44
rv_t-43
volume_t-43
x_best_alpha_1_t-43
x_best_beta_1_t-43
x_best_omega_t-43
x_best_mu_t-43
x_best_nu_tgarch_t-43
rv_t-42
volume_t-42
x_best_alpha_1_t-42
x_best_beta_1_t-42
x_best_omega_t-42
x_best_mu_t-42
x_best_nu_tgarch_t-42
rv_t-41
volume_t-41
x_best_alpha_1_t-41
x_best_beta_1_t-41
x_best_omega_t-41
x_best_mu_t-41
x_best_nu_tgarch_t-41
rv_t-40
volume_t-40
x_best_alpha_1_t-40
x_best_beta_1_t-40
x_best_omega_t-40
x_best_mu_t-40
x_best_nu_tgarch_t-40
rv_t-39
volume_t-39
x_best_alpha_1_t-39
x_best_beta_1_t-39
x_best_omega_t-39
x_best_mu_t-39
x_best_nu_tgarch_t-39
rv_t-38
volume_t-38
x_best_alpha_1_t-38
x_best_beta_1_t-38
x_best_omega_t-38
x_best_mu_t-38
x_best_nu_tgarch_t-38
rv_t-37
volume_t-37
x_best_alpha_1_t-37
x_best_beta_1_t-37
x_best_omega_t-37
x_best_mu_t-37
x_best_nu_tgarch_t-37
rv_t-36
volume_t-36
x_best_alpha_1_t-36
x_best_beta_1_t-36
x_best_omega_t-36
x_best_mu_t-36
x_best_nu_tgarch_t-36
rv_t-35
volume_t-35
x_best_alpha_1_t-35
x_best_beta_1_t-35
x_best_omega_t-35
x_best_mu_t-35
x_best_nu_tgarch_t-35
rv_t-34
volume_t-34
x_best_alpha_1_t-34
x_best_beta_1_t-34
x_best_omega_t-34
x_best_mu_t-34
x_best_nu_tgarch_t-34
rv_t-33
volume_t-33
x_best_alpha_1_t-33
x_best_beta_1_t-33
x_best_omega_t-33
x_best_mu_t-33
x_best_nu_tgarch_t-33
rv_t-32
volume_t-32
x_best_alpha_1_t-32
x_best_beta_1_t-32
x_best_omega_t-32
x_best_mu_t-32
x_best_nu_tgarch_t-32
rv_t-31
volume_t-31
x_best_alpha_1_t-31
x_best_beta_1_t-31
x_best_omega_t-31
x_best_mu_t-31
x_best_nu_tgarch_t-31
rv_t-30
volume_t-30
x_best_alpha_1_t-30
x_best_beta_1_t-30
x_best_omega_t-30
x_best_mu_t-30
x_best_nu_tgarch_t-30
rv_t-29
volume_t-29
x_best_alpha_1_t-29
x_best_beta_1_t-29
x_best_omega_t-29
x_best_mu_t-29
x_best_nu_tgarch_t-29
rv_t-28
volume_t-28
x_best_alpha_1_t-28
x_best_beta_1_t-28
x_best_omega_t-28
x_best_mu_t-28
x_best_nu_tgarch_t-28
rv_t-27
volume_t-27
x_best_alpha_1_t-27
x_best_beta_1_t-27
x_best_omega_t-27
x_best_mu_t-27
x_best_nu_tgarch_t-27
rv_t-26
volume_t-26
x_best_alpha_1_t-26
x_best_beta_1_t-26
x_best_omega_t-26
x_best_mu_t-26
x_best_nu_tgarch_t-26
rv_t-25
volume_t-25
x_best_alpha_1_t-25
x_best_beta_1_t-25
x_best_omega_t-25
x_best_mu_t-25
x_best_nu_tgarch_t-25
rv_t-24
volume_t-24
x_best_alpha_1_t-24
x_best_beta_1_t-24
x_best_omega_t-24
x_best_mu_t-24
x_best_nu_tgarch_t-24
rv_t-23
volume_t-23
x_best_alpha_1_t-23
x_best_beta_1_t-23
x_best_omega_t-23
x_best_mu_t-23
x_best_nu_tgarch_t-23
rv_t-22
volume_t-22
x_best_alpha_1_t-22
x_best_beta_1_t-22
x_best_omega_t-22
x_best_mu_t-22
x_best_nu_tgarch_t-22
rv_t-21
volume_t-21
x_best_alpha_1_t-21
x_best_beta_1_t-21
x_best_omega_t-21
x_best_mu_t-21
x_best_nu_tgarch_t-21
rv_t-20
volume_t-20
x_best_alpha_1_t-20
x_best_beta_1_t-20
x_best_omega_t-20
x_best_mu_t-20
x_best_nu_tgarch_t-20
rv_t-19
volume_t-19
x_best_alpha_1_t-19
x_best_beta_1_t-19
x_best_omega_t-19
x_best_mu_t-19
x_best_nu_tgarch_t-19
rv_t-18
volume_t-18
x_best_alpha_1_t-18
x_best_beta_1_t-18
x_best_omega_t-18
x_best_mu_t-18
x_best_nu_tgarch_t-18
rv_t-17
volume_t-17
x_best_alpha_1_t-17
x_best_beta_1_t-17
x_best_omega_t-17
x_best_mu_t-17
x_best_nu_tgarch_t-17
rv_t-16
volume_t-16
x_best_alpha_1_t-16
x_best_beta_1_t-16
x_best_omega_t-16
x_best_mu_t-16
x_best_nu_tgarch_t-16
rv_t-15
volume_t-15
x_best_alpha_1_t-15
x_best_beta_1_t-15
x_best_omega_t-15
x_best_mu_t-15
x_best_nu_tgarch_t-15
rv_t-14
volume_t-14
x_best_alpha_1_t-14
x_best_beta_1_t-14
x_best_omega_t-14
x_best_mu_t-14
x_best_nu_tgarch_t-14
rv_t-13
volume_t-13
x_best_alpha_1_t-13
x_best_beta_1_t-13
x_best_omega_t-13
x_best_mu_t-13
x_best_nu_tgarch_t-13
rv_t-12
volume_t-12
x_best_alpha_1_t-12
x_best_beta_1_t-12
x_best_omega_t-12
x_best_mu_t-12
x_best_nu_tgarch_t-12
rv_t-11
volume_t-11
x_best_alpha_1_t-11
x_best_beta_1_t-11
x_best_omega_t-11
x_best_mu_t-11
x_best_nu_tgarch_t-11
rv_t-10
volume_t-10
x_best_alpha_1_t-10
x_best_beta_1_t-10
x_best_omega_t-10
x_best_mu_t-10
x_best_nu_tgarch_t-10
rv_t-9
volume_t-9
x_best_alpha_1_t-9
x_best_beta_1_t-9
x_best_omega_t-9
x_best_mu_t-9
x_best_nu_tgarch_t-9
rv_t-8
volume_t-8
x_best_alpha_1_t-8
x_best_beta_1_t-8
x_best_omega_t-8
x_best_mu_t-8
x_best_nu_tgarch_t-8
rv_t-7
volume_t-7
x_best_alpha_1_t-7
x_best_beta_1_t-7
x_best_omega_t-7
x_best_mu_t-7
x_best_nu_tgarch_t-7
rv_t-6
volume_t-6
x_best_alpha_1_t-6
x_best_beta_1_t-6
x_best_omega_t-6
x_best_mu_t-6
x_best_nu_tgarch_t-6
rv_t-5
volume_t-5
x_best_alpha_1_t-5
x_best_beta_1_t-5
x_best_omega_t-5
x_best_mu_t-5
x_best_nu_tgarch_t-5
rv_t-4
volume_t-4
x_best_alpha_1_t-4
x_best_beta_1_t-4
x_best_omega_t-4
x_best_mu_t-4
x_best_nu_tgarch_t-4
rv_t-3
volume_t-3
x_best_alpha_1_t-3
x_best_beta_1_t-3
x_best_omega_t-3
x_best_mu_t-3
x_best_nu_tgarch_t-3
rv_t-2
volume_t-2
x_best_alpha_1_t-2
x_best_beta_1_t-2
x_best_omega_t-2
x_best_mu_t-2
x_best_nu_tgarch_t-2
rv_t-1
volume_t-1
x_best_alpha_1_t-1
x_best_beta_1_t-1
x_best_omega_t-1
x_best_mu_t-1
x_best_nu_tgarch_t-1
rv_t-0
volume_t-0
x_best_alpha_1_t-0
x_best_beta_1_t-0
x_best_omega_t-0
x_best_mu_t-0
x_best_nu_tgarch_t-0
--- AAPL X_time columns: ---
x_time_dow_sin_t-59
x_time_dow_cos_t-59
x_time_dom_sin_t-59
x_time_dom_cos_t-59
x_time_doy_sin_t-59
x_time_doy_cos_t-59
x_time_woy_sin_t-59
x_time_woy_cos_t-59
x_time_moy_sin_t-59
x_time_moy_cos_t-59
x_time_is_quarter_end_t-59
x_time_years_since_2000_t-59
x_time_days_since_2000_t-59
x_time_months_since_2000_t-59
x_time_dow_sin_t-58
x_time_dow_cos_t-58
x_time_dom_sin_t-58
x_time_dom_cos_t-58
x_time_doy_sin_t-58
x_time_doy_cos_t-58
x_time_woy_sin_t-58
x_time_woy_cos_t-58
x_time_moy_sin_t-58
x_time_moy_cos_t-58
x_time_is_quarter_end_t-58
x_time_years_since_2000_t-58
x_time_days_since_2000_t-58
x_time_months_since_2000_t-58
x_time_dow_sin_t-57
x_time_dow_cos_t-57
x_time_dom_sin_t-57
x_time_dom_cos_t-57
x_time_doy_sin_t-57
x_time_doy_cos_t-57
x_time_woy_sin_t-57
x_time_woy_cos_t-57
x_time_moy_sin_t-57
x_time_moy_cos_t-57
x_time_is_quarter_end_t-57
x_time_years_since_2000_t-57
x_time_days_since_2000_t-57
x_time_months_since_2000_t-57
x_time_dow_sin_t-56
x_time_dow_cos_t-56
x_time_dom_sin_t-56
x_time_dom_cos_t-56
x_time_doy_sin_t-56
x_time_doy_cos_t-56
x_time_woy_sin_t-56
x_time_woy_cos_t-56
x_time_moy_sin_t-56
x_time_moy_cos_t-56
x_time_is_quarter_end_t-56
x_time_years_since_2000_t-56
x_time_days_since_2000_t-56
x_time_months_since_2000_t-56
x_time_dow_sin_t-55
x_time_dow_cos_t-55
x_time_dom_sin_t-55
x_time_dom_cos_t-55
x_time_doy_sin_t-55
x_time_doy_cos_t-55
x_time_woy_sin_t-55
x_time_woy_cos_t-55
x_time_moy_sin_t-55
x_time_moy_cos_t-55
x_time_is_quarter_end_t-55
x_time_years_since_2000_t-55
x_time_days_since_2000_t-55
x_time_months_since_2000_t-55
x_time_dow_sin_t-54
x_time_dow_cos_t-54
x_time_dom_sin_t-54
x_time_dom_cos_t-54
x_time_doy_sin_t-54
x_time_doy_cos_t-54
x_time_woy_sin_t-54
x_time_woy_cos_t-54
x_time_moy_sin_t-54
x_time_moy_cos_t-54
x_time_is_quarter_end_t-54
x_time_years_since_2000_t-54
x_time_days_since_2000_t-54
x_time_months_since_2000_t-54
x_time_dow_sin_t-53
x_time_dow_cos_t-53
x_time_dom_sin_t-53
x_time_dom_cos_t-53
x_time_doy_sin_t-53
x_time_doy_cos_t-53
x_time_woy_sin_t-53
x_time_woy_cos_t-53
x_time_moy_sin_t-53
x_time_moy_cos_t-53
x_time_is_quarter_end_t-53
x_time_years_since_2000_t-53
x_time_days_since_2000_t-53
x_time_months_since_2000_t-53
x_time_dow_sin_t-52
x_time_dow_cos_t-52
x_time_dom_sin_t-52
x_time_dom_cos_t-52
x_time_doy_sin_t-52
x_time_doy_cos_t-52
x_time_woy_sin_t-52
x_time_woy_cos_t-52
x_time_moy_sin_t-52
x_time_moy_cos_t-52
x_time_is_quarter_end_t-52
x_time_years_since_2000_t-52
x_time_days_since_2000_t-52
x_time_months_since_2000_t-52
x_time_dow_sin_t-51
x_time_dow_cos_t-51
x_time_dom_sin_t-51
x_time_dom_cos_t-51
x_time_doy_sin_t-51
x_time_doy_cos_t-51
x_time_woy_sin_t-51
x_time_woy_cos_t-51
x_time_moy_sin_t-51
x_time_moy_cos_t-51
x_time_is_quarter_end_t-51
x_time_years_since_2000_t-51
x_time_days_since_2000_t-51
x_time_months_since_2000_t-51
x_time_dow_sin_t-50
x_time_dow_cos_t-50
x_time_dom_sin_t-50
x_time_dom_cos_t-50
x_time_doy_sin_t-50
x_time_doy_cos_t-50
x_time_woy_sin_t-50
x_time_woy_cos_t-50
x_time_moy_sin_t-50
x_time_moy_cos_t-50
x_time_is_quarter_end_t-50
x_time_years_since_2000_t-50
x_time_days_since_2000_t-50
x_time_months_since_2000_t-50
x_time_dow_sin_t-49
x_time_dow_cos_t-49
x_time_dom_sin_t-49
x_time_dom_cos_t-49
x_time_doy_sin_t-49
x_time_doy_cos_t-49
x_time_woy_sin_t-49
x_time_woy_cos_t-49
x_time_moy_sin_t-49
x_time_moy_cos_t-49
x_time_is_quarter_end_t-49
x_time_years_since_2000_t-49
x_time_days_since_2000_t-49
x_time_months_since_2000_t-49
x_time_dow_sin_t-48
x_time_dow_cos_t-48
x_time_dom_sin_t-48
x_time_dom_cos_t-48
x_time_doy_sin_t-48
x_time_doy_cos_t-48
x_time_woy_sin_t-48
x_time_woy_cos_t-48
x_time_moy_sin_t-48
x_time_moy_cos_t-48
x_time_is_quarter_end_t-48
x_time_years_since_2000_t-48
x_time_days_since_2000_t-48
x_time_months_since_2000_t-48
x_time_dow_sin_t-47
x_time_dow_cos_t-47
x_time_dom_sin_t-47
x_time_dom_cos_t-47
x_time_doy_sin_t-47
x_time_doy_cos_t-47
x_time_woy_sin_t-47
x_time_woy_cos_t-47
x_time_moy_sin_t-47
x_time_moy_cos_t-47
x_time_is_quarter_end_t-47
x_time_years_since_2000_t-47
x_time_days_since_2000_t-47
x_time_months_since_2000_t-47
x_time_dow_sin_t-46
x_time_dow_cos_t-46
x_time_dom_sin_t-46
x_time_dom_cos_t-46
x_time_doy_sin_t-46
x_time_doy_cos_t-46
x_time_woy_sin_t-46
x_time_woy_cos_t-46
x_time_moy_sin_t-46
x_time_moy_cos_t-46
x_time_is_quarter_end_t-46
x_time_years_since_2000_t-46
x_time_days_since_2000_t-46
x_time_months_since_2000_t-46
x_time_dow_sin_t-45
x_time_dow_cos_t-45
x_time_dom_sin_t-45
x_time_dom_cos_t-45
x_time_doy_sin_t-45
x_time_doy_cos_t-45
x_time_woy_sin_t-45
x_time_woy_cos_t-45
x_time_moy_sin_t-45
x_time_moy_cos_t-45
x_time_is_quarter_end_t-45
x_time_years_since_2000_t-45
x_time_days_since_2000_t-45
x_time_months_since_2000_t-45
x_time_dow_sin_t-44
x_time_dow_cos_t-44
x_time_dom_sin_t-44
x_time_dom_cos_t-44
x_time_doy_sin_t-44
x_time_doy_cos_t-44
x_time_woy_sin_t-44
x_time_woy_cos_t-44
x_time_moy_sin_t-44
x_time_moy_cos_t-44
x_time_is_quarter_end_t-44
x_time_years_since_2000_t-44
x_time_days_since_2000_t-44
x_time_months_since_2000_t-44
x_time_dow_sin_t-43
x_time_dow_cos_t-43
x_time_dom_sin_t-43
x_time_dom_cos_t-43
x_time_doy_sin_t-43
x_time_doy_cos_t-43
x_time_woy_sin_t-43
x_time_woy_cos_t-43
x_time_moy_sin_t-43
x_time_moy_cos_t-43
x_time_is_quarter_end_t-43
x_time_years_since_2000_t-43
x_time_days_since_2000_t-43
x_time_months_since_2000_t-43
x_time_dow_sin_t-42
x_time_dow_cos_t-42
x_time_dom_sin_t-42
x_time_dom_cos_t-42
x_time_doy_sin_t-42
x_time_doy_cos_t-42
x_time_woy_sin_t-42
x_time_woy_cos_t-42
x_time_moy_sin_t-42
x_time_moy_cos_t-42
x_time_is_quarter_end_t-42
x_time_years_since_2000_t-42
x_time_days_since_2000_t-42
x_time_months_since_2000_t-42
x_time_dow_sin_t-41
x_time_dow_cos_t-41
x_time_dom_sin_t-41
x_time_dom_cos_t-41
x_time_doy_sin_t-41
x_time_doy_cos_t-41
x_time_woy_sin_t-41
x_time_woy_cos_t-41
x_time_moy_sin_t-41
x_time_moy_cos_t-41
x_time_is_quarter_end_t-41
x_time_years_since_2000_t-41
x_time_days_since_2000_t-41
x_time_months_since_2000_t-41
x_time_dow_sin_t-40
x_time_dow_cos_t-40
x_time_dom_sin_t-40
x_time_dom_cos_t-40
x_time_doy_sin_t-40
x_time_doy_cos_t-40
x_time_woy_sin_t-40
x_time_woy_cos_t-40
x_time_moy_sin_t-40
x_time_moy_cos_t-40
x_time_is_quarter_end_t-40
x_time_years_since_2000_t-40
x_time_days_since_2000_t-40
x_time_months_since_2000_t-40
x_time_dow_sin_t-39
x_time_dow_cos_t-39
x_time_dom_sin_t-39
x_time_dom_cos_t-39
x_time_doy_sin_t-39
x_time_doy_cos_t-39
x_time_woy_sin_t-39
x_time_woy_cos_t-39
x_time_moy_sin_t-39
x_time_moy_cos_t-39
x_time_is_quarter_end_t-39
x_time_years_since_2000_t-39
x_time_days_since_2000_t-39
x_time_months_since_2000_t-39
x_time_dow_sin_t-38
x_time_dow_cos_t-38
x_time_dom_sin_t-38
x_time_dom_cos_t-38
x_time_doy_sin_t-38
x_time_doy_cos_t-38
x_time_woy_sin_t-38
x_time_woy_cos_t-38
x_time_moy_sin_t-38
x_time_moy_cos_t-38
x_time_is_quarter_end_t-38
x_time_years_since_2000_t-38
x_time_days_since_2000_t-38
x_time_months_since_2000_t-38
x_time_dow_sin_t-37
x_time_dow_cos_t-37
x_time_dom_sin_t-37
x_time_dom_cos_t-37
x_time_doy_sin_t-37
x_time_doy_cos_t-37
x_time_woy_sin_t-37
x_time_woy_cos_t-37
x_time_moy_sin_t-37
x_time_moy_cos_t-37
x_time_is_quarter_end_t-37
x_time_years_since_2000_t-37
x_time_days_since_2000_t-37
x_time_months_since_2000_t-37
x_time_dow_sin_t-36
x_time_dow_cos_t-36
x_time_dom_sin_t-36
x_time_dom_cos_t-36
x_time_doy_sin_t-36
x_time_doy_cos_t-36
x_time_woy_sin_t-36
x_time_woy_cos_t-36
x_time_moy_sin_t-36
x_time_moy_cos_t-36
x_time_is_quarter_end_t-36
x_time_years_since_2000_t-36
x_time_days_since_2000_t-36
x_time_months_since_2000_t-36
x_time_dow_sin_t-35
x_time_dow_cos_t-35
x_time_dom_sin_t-35
x_time_dom_cos_t-35
x_time_doy_sin_t-35
x_time_doy_cos_t-35
x_time_woy_sin_t-35
x_time_woy_cos_t-35
x_time_moy_sin_t-35
x_time_moy_cos_t-35
x_time_is_quarter_end_t-35
x_time_years_since_2000_t-35
x_time_days_since_2000_t-35
x_time_months_since_2000_t-35
x_time_dow_sin_t-34
x_time_dow_cos_t-34
x_time_dom_sin_t-34
x_time_dom_cos_t-34
x_time_doy_sin_t-34
x_time_doy_cos_t-34
x_time_woy_sin_t-34
x_time_woy_cos_t-34
x_time_moy_sin_t-34
x_time_moy_cos_t-34
x_time_is_quarter_end_t-34
x_time_years_since_2000_t-34
x_time_days_since_2000_t-34
x_time_months_since_2000_t-34
x_time_dow_sin_t-33
x_time_dow_cos_t-33
x_time_dom_sin_t-33
x_time_dom_cos_t-33
x_time_doy_sin_t-33
x_time_doy_cos_t-33
x_time_woy_sin_t-33
x_time_woy_cos_t-33
x_time_moy_sin_t-33
x_time_moy_cos_t-33
x_time_is_quarter_end_t-33
x_time_years_since_2000_t-33
x_time_days_since_2000_t-33
x_time_months_since_2000_t-33
x_time_dow_sin_t-32
x_time_dow_cos_t-32
x_time_dom_sin_t-32
x_time_dom_cos_t-32
x_time_doy_sin_t-32
x_time_doy_cos_t-32
x_time_woy_sin_t-32
x_time_woy_cos_t-32
x_time_moy_sin_t-32
x_time_moy_cos_t-32
x_time_is_quarter_end_t-32
x_time_years_since_2000_t-32
x_time_days_since_2000_t-32
x_time_months_since_2000_t-32
x_time_dow_sin_t-31
x_time_dow_cos_t-31
x_time_dom_sin_t-31
x_time_dom_cos_t-31
x_time_doy_sin_t-31
x_time_doy_cos_t-31
x_time_woy_sin_t-31
x_time_woy_cos_t-31
x_time_moy_sin_t-31
x_time_moy_cos_t-31
x_time_is_quarter_end_t-31
x_time_years_since_2000_t-31
x_time_days_since_2000_t-31
x_time_months_since_2000_t-31
x_time_dow_sin_t-30
x_time_dow_cos_t-30
x_time_dom_sin_t-30
x_time_dom_cos_t-30
x_time_doy_sin_t-30
x_time_doy_cos_t-30
x_time_woy_sin_t-30
x_time_woy_cos_t-30
x_time_moy_sin_t-30
x_time_moy_cos_t-30
x_time_is_quarter_end_t-30
x_time_years_since_2000_t-30
x_time_days_since_2000_t-30
x_time_months_since_2000_t-30
x_time_dow_sin_t-29
x_time_dow_cos_t-29
x_time_dom_sin_t-29
x_time_dom_cos_t-29
x_time_doy_sin_t-29
x_time_doy_cos_t-29
x_time_woy_sin_t-29
x_time_woy_cos_t-29
x_time_moy_sin_t-29
x_time_moy_cos_t-29
x_time_is_quarter_end_t-29
x_time_years_since_2000_t-29
x_time_days_since_2000_t-29
x_time_months_since_2000_t-29
x_time_dow_sin_t-28
x_time_dow_cos_t-28
x_time_dom_sin_t-28
x_time_dom_cos_t-28
x_time_doy_sin_t-28
x_time_doy_cos_t-28
x_time_woy_sin_t-28
x_time_woy_cos_t-28
x_time_moy_sin_t-28
x_time_moy_cos_t-28
x_time_is_quarter_end_t-28
x_time_years_since_2000_t-28
x_time_days_since_2000_t-28
x_time_months_since_2000_t-28
x_time_dow_sin_t-27
x_time_dow_cos_t-27
x_time_dom_sin_t-27
x_time_dom_cos_t-27
x_time_doy_sin_t-27
x_time_doy_cos_t-27
x_time_woy_sin_t-27
x_time_woy_cos_t-27
x_time_moy_sin_t-27
x_time_moy_cos_t-27
x_time_is_quarter_end_t-27
x_time_years_since_2000_t-27
x_time_days_since_2000_t-27
x_time_months_since_2000_t-27
x_time_dow_sin_t-26
x_time_dow_cos_t-26
x_time_dom_sin_t-26
x_time_dom_cos_t-26
x_time_doy_sin_t-26
x_time_doy_cos_t-26
x_time_woy_sin_t-26
x_time_woy_cos_t-26
x_time_moy_sin_t-26
x_time_moy_cos_t-26
x_time_is_quarter_end_t-26
x_time_years_since_2000_t-26
x_time_days_since_2000_t-26
x_time_months_since_2000_t-26
x_time_dow_sin_t-25
x_time_dow_cos_t-25
x_time_dom_sin_t-25
x_time_dom_cos_t-25
x_time_doy_sin_t-25
x_time_doy_cos_t-25
x_time_woy_sin_t-25
x_time_woy_cos_t-25
x_time_moy_sin_t-25
x_time_moy_cos_t-25
x_time_is_quarter_end_t-25
x_time_years_since_2000_t-25
x_time_days_since_2000_t-25
x_time_months_since_2000_t-25
x_time_dow_sin_t-24
x_time_dow_cos_t-24
x_time_dom_sin_t-24
x_time_dom_cos_t-24
x_time_doy_sin_t-24
x_time_doy_cos_t-24
x_time_woy_sin_t-24
x_time_woy_cos_t-24
x_time_moy_sin_t-24
x_time_moy_cos_t-24
x_time_is_quarter_end_t-24
x_time_years_since_2000_t-24
x_time_days_since_2000_t-24
x_time_months_since_2000_t-24
x_time_dow_sin_t-23
x_time_dow_cos_t-23
x_time_dom_sin_t-23
x_time_dom_cos_t-23
x_time_doy_sin_t-23
x_time_doy_cos_t-23
x_time_woy_sin_t-23
x_time_woy_cos_t-23
x_time_moy_sin_t-23
x_time_moy_cos_t-23
x_time_is_quarter_end_t-23
x_time_years_since_2000_t-23
x_time_days_since_2000_t-23
x_time_months_since_2000_t-23
x_time_dow_sin_t-22
x_time_dow_cos_t-22
x_time_dom_sin_t-22
x_time_dom_cos_t-22
x_time_doy_sin_t-22
x_time_doy_cos_t-22
x_time_woy_sin_t-22
x_time_woy_cos_t-22
x_time_moy_sin_t-22
x_time_moy_cos_t-22
x_time_is_quarter_end_t-22
x_time_years_since_2000_t-22
x_time_days_since_2000_t-22
x_time_months_since_2000_t-22
x_time_dow_sin_t-21
x_time_dow_cos_t-21
x_time_dom_sin_t-21
x_time_dom_cos_t-21
x_time_doy_sin_t-21
x_time_doy_cos_t-21
x_time_woy_sin_t-21
x_time_woy_cos_t-21
x_time_moy_sin_t-21
x_time_moy_cos_t-21
x_time_is_quarter_end_t-21
x_time_years_since_2000_t-21
x_time_days_since_2000_t-21
x_time_months_since_2000_t-21
x_time_dow_sin_t-20
x_time_dow_cos_t-20
x_time_dom_sin_t-20
x_time_dom_cos_t-20
x_time_doy_sin_t-20
x_time_doy_cos_t-20
x_time_woy_sin_t-20
x_time_woy_cos_t-20
x_time_moy_sin_t-20
x_time_moy_cos_t-20
x_time_is_quarter_end_t-20
x_time_years_since_2000_t-20
x_time_days_since_2000_t-20
x_time_months_since_2000_t-20
x_time_dow_sin_t-19
x_time_dow_cos_t-19
x_time_dom_sin_t-19
x_time_dom_cos_t-19
x_time_doy_sin_t-19
x_time_doy_cos_t-19
x_time_woy_sin_t-19
x_time_woy_cos_t-19
x_time_moy_sin_t-19
x_time_moy_cos_t-19
x_time_is_quarter_end_t-19
x_time_years_since_2000_t-19
x_time_days_since_2000_t-19
x_time_months_since_2000_t-19
x_time_dow_sin_t-18
x_time_dow_cos_t-18
x_time_dom_sin_t-18
x_time_dom_cos_t-18
x_time_doy_sin_t-18
x_time_doy_cos_t-18
x_time_woy_sin_t-18
x_time_woy_cos_t-18
x_time_moy_sin_t-18
x_time_moy_cos_t-18
x_time_is_quarter_end_t-18
x_time_years_since_2000_t-18
x_time_days_since_2000_t-18
x_time_months_since_2000_t-18
x_time_dow_sin_t-17
x_time_dow_cos_t-17
x_time_dom_sin_t-17
x_time_dom_cos_t-17
x_time_doy_sin_t-17
x_time_doy_cos_t-17
x_time_woy_sin_t-17
x_time_woy_cos_t-17
x_time_moy_sin_t-17
x_time_moy_cos_t-17
x_time_is_quarter_end_t-17
x_time_years_since_2000_t-17
x_time_days_since_2000_t-17
x_time_months_since_2000_t-17
x_time_dow_sin_t-16
x_time_dow_cos_t-16
x_time_dom_sin_t-16
x_time_dom_cos_t-16
x_time_doy_sin_t-16
x_time_doy_cos_t-16
x_time_woy_sin_t-16
x_time_woy_cos_t-16
x_time_moy_sin_t-16
x_time_moy_cos_t-16
x_time_is_quarter_end_t-16
x_time_years_since_2000_t-16
x_time_days_since_2000_t-16
x_time_months_since_2000_t-16
x_time_dow_sin_t-15
x_time_dow_cos_t-15
x_time_dom_sin_t-15
x_time_dom_cos_t-15
x_time_doy_sin_t-15
x_time_doy_cos_t-15
x_time_woy_sin_t-15
x_time_woy_cos_t-15
x_time_moy_sin_t-15
x_time_moy_cos_t-15
x_time_is_quarter_end_t-15
x_time_years_since_2000_t-15
x_time_days_since_2000_t-15
x_time_months_since_2000_t-15
x_time_dow_sin_t-14
x_time_dow_cos_t-14
x_time_dom_sin_t-14
x_time_dom_cos_t-14
x_time_doy_sin_t-14
x_time_doy_cos_t-14
x_time_woy_sin_t-14
x_time_woy_cos_t-14
x_time_moy_sin_t-14
x_time_moy_cos_t-14
x_time_is_quarter_end_t-14
x_time_years_since_2000_t-14
x_time_days_since_2000_t-14
x_time_months_since_2000_t-14
x_time_dow_sin_t-13
x_time_dow_cos_t-13
x_time_dom_sin_t-13
x_time_dom_cos_t-13
x_time_doy_sin_t-13
x_time_doy_cos_t-13
x_time_woy_sin_t-13
x_time_woy_cos_t-13
x_time_moy_sin_t-13
x_time_moy_cos_t-13
x_time_is_quarter_end_t-13
x_time_years_since_2000_t-13
x_time_days_since_2000_t-13
x_time_months_since_2000_t-13
x_time_dow_sin_t-12
x_time_dow_cos_t-12
x_time_dom_sin_t-12
x_time_dom_cos_t-12
x_time_doy_sin_t-12
x_time_doy_cos_t-12
x_time_woy_sin_t-12
x_time_woy_cos_t-12
x_time_moy_sin_t-12
x_time_moy_cos_t-12
x_time_is_quarter_end_t-12
x_time_years_since_2000_t-12
x_time_days_since_2000_t-12
x_time_months_since_2000_t-12
x_time_dow_sin_t-11
x_time_dow_cos_t-11
x_time_dom_sin_t-11
x_time_dom_cos_t-11
x_time_doy_sin_t-11
x_time_doy_cos_t-11
x_time_woy_sin_t-11
x_time_woy_cos_t-11
x_time_moy_sin_t-11
x_time_moy_cos_t-11
x_time_is_quarter_end_t-11
x_time_years_since_2000_t-11
x_time_days_since_2000_t-11
x_time_months_since_2000_t-11
x_time_dow_sin_t-10
x_time_dow_cos_t-10
x_time_dom_sin_t-10
x_time_dom_cos_t-10
x_time_doy_sin_t-10
x_time_doy_cos_t-10
x_time_woy_sin_t-10
x_time_woy_cos_t-10
x_time_moy_sin_t-10
x_time_moy_cos_t-10
x_time_is_quarter_end_t-10
x_time_years_since_2000_t-10
x_time_days_since_2000_t-10
x_time_months_since_2000_t-10
x_time_dow_sin_t-9
x_time_dow_cos_t-9
x_time_dom_sin_t-9
x_time_dom_cos_t-9
x_time_doy_sin_t-9
x_time_doy_cos_t-9
x_time_woy_sin_t-9
x_time_woy_cos_t-9
x_time_moy_sin_t-9
x_time_moy_cos_t-9
x_time_is_quarter_end_t-9
x_time_years_since_2000_t-9
x_time_days_since_2000_t-9
x_time_months_since_2000_t-9
x_time_dow_sin_t-8
x_time_dow_cos_t-8
x_time_dom_sin_t-8
x_time_dom_cos_t-8
x_time_doy_sin_t-8
x_time_doy_cos_t-8
x_time_woy_sin_t-8
x_time_woy_cos_t-8
x_time_moy_sin_t-8
x_time_moy_cos_t-8
x_time_is_quarter_end_t-8
x_time_years_since_2000_t-8
x_time_days_since_2000_t-8
x_time_months_since_2000_t-8
x_time_dow_sin_t-7
x_time_dow_cos_t-7
x_time_dom_sin_t-7
x_time_dom_cos_t-7
x_time_doy_sin_t-7
x_time_doy_cos_t-7
x_time_woy_sin_t-7
x_time_woy_cos_t-7
x_time_moy_sin_t-7
x_time_moy_cos_t-7
x_time_is_quarter_end_t-7
x_time_years_since_2000_t-7
x_time_days_since_2000_t-7
x_time_months_since_2000_t-7
x_time_dow_sin_t-6
x_time_dow_cos_t-6
x_time_dom_sin_t-6
x_time_dom_cos_t-6
x_time_doy_sin_t-6
x_time_doy_cos_t-6
x_time_woy_sin_t-6
x_time_woy_cos_t-6
x_time_moy_sin_t-6
x_time_moy_cos_t-6
x_time_is_quarter_end_t-6
x_time_years_since_2000_t-6
x_time_days_since_2000_t-6
x_time_months_since_2000_t-6
x_time_dow_sin_t-5
x_time_dow_cos_t-5
x_time_dom_sin_t-5
x_time_dom_cos_t-5
x_time_doy_sin_t-5
x_time_doy_cos_t-5
x_time_woy_sin_t-5
x_time_woy_cos_t-5
x_time_moy_sin_t-5
x_time_moy_cos_t-5
x_time_is_quarter_end_t-5
x_time_years_since_2000_t-5
x_time_days_since_2000_t-5
x_time_months_since_2000_t-5
x_time_dow_sin_t-4
x_time_dow_cos_t-4
x_time_dom_sin_t-4
x_time_dom_cos_t-4
x_time_doy_sin_t-4
x_time_doy_cos_t-4
x_time_woy_sin_t-4
x_time_woy_cos_t-4
x_time_moy_sin_t-4
x_time_moy_cos_t-4
x_time_is_quarter_end_t-4
x_time_years_since_2000_t-4
x_time_days_since_2000_t-4
x_time_months_since_2000_t-4
x_time_dow_sin_t-3
x_time_dow_cos_t-3
x_time_dom_sin_t-3
x_time_dom_cos_t-3
x_time_doy_sin_t-3
x_time_doy_cos_t-3
x_time_woy_sin_t-3
x_time_woy_cos_t-3
x_time_moy_sin_t-3
x_time_moy_cos_t-3
x_time_is_quarter_end_t-3
x_time_years_since_2000_t-3
x_time_days_since_2000_t-3
x_time_months_since_2000_t-3
x_time_dow_sin_t-2
x_time_dow_cos_t-2
x_time_dom_sin_t-2
x_time_dom_cos_t-2
x_time_doy_sin_t-2
x_time_doy_cos_t-2
x_time_woy_sin_t-2
x_time_woy_cos_t-2
x_time_moy_sin_t-2
x_time_moy_cos_t-2
x_time_is_quarter_end_t-2
x_time_years_since_2000_t-2
x_time_days_since_2000_t-2
x_time_months_since_2000_t-2
x_time_dow_sin_t-1
x_time_dow_cos_t-1
x_time_dom_sin_t-1
x_time_dom_cos_t-1
x_time_doy_sin_t-1
x_time_doy_cos_t-1
x_time_woy_sin_t-1
x_time_woy_cos_t-1
x_time_moy_sin_t-1
x_time_moy_cos_t-1
x_time_is_quarter_end_t-1
x_time_years_since_2000_t-1
x_time_days_since_2000_t-1
x_time_months_since_2000_t-1
x_time_dow_sin_t-0
x_time_dow_cos_t-0
x_time_dom_sin_t-0
x_time_dom_cos_t-0
x_time_doy_sin_t-0
x_time_doy_cos_t-0
x_time_woy_sin_t-0
x_time_woy_cos_t-0
x_time_moy_sin_t-0
x_time_moy_cos_t-0
x_time_is_quarter_end_t-0
x_time_years_since_2000_t-0
x_time_days_since_2000_t-0
x_time_months_since_2000_t-0
---AAPL y columns:---
rv_t+1
lg_return_t+1
y_best_alpha_1_t+1
y_best_beta_1_t+1
y_best_omega_t+1
y_best_mu_t+1
y_best_nu_tgarch_t+1
rv_t+2
lg_return_t+2
y_best_alpha_1_t+2
y_best_beta_1_t+2
y_best_omega_t+2
y_best_mu_t+2
y_best_nu_tgarch_t+2
rv_t+3
lg_return_t+3
y_best_alpha_1_t+3
y_best_beta_1_t+3
y_best_omega_t+3
y_best_mu_t+3
y_best_nu_tgarch_t+3
rv_t+4
lg_return_t+4
y_best_alpha_1_t+4
y_best_beta_1_t+4
y_best_omega_t+4
y_best_mu_t+4
y_best_nu_tgarch_t+4
rv_t+5
lg_return_t+5
y_best_alpha_1_t+5
y_best_beta_1_t+5
y_best_omega_t+5
y_best_mu_t+5
y_best_nu_tgarch_t+5
rv_t+6
lg_return_t+6
y_best_alpha_1_t+6
y_best_beta_1_t+6
y_best_omega_t+6
y_best_mu_t+6
y_best_nu_tgarch_t+6
rv_t+7
lg_return_t+7
y_best_alpha_1_t+7
y_best_beta_1_t+7
y_best_omega_t+7
y_best_mu_t+7
y_best_nu_tgarch_t+7
rv_t+8
lg_return_t+8
y_best_alpha_1_t+8
y_best_beta_1_t+8
y_best_omega_t+8
y_best_mu_t+8
y_best_nu_tgarch_t+8
rv_t+9
lg_return_t+9
y_best_alpha_1_t+9
y_best_beta_1_t+9
y_best_omega_t+9
y_best_mu_t+9
y_best_nu_tgarch_t+9
rv_t+10
lg_return_t+10
y_best_alpha_1_t+10
y_best_beta_1_t+10
y_best_omega_t+10
y_best_mu_t+10
y_best_nu_tgarch_t+10
rv_t+11
lg_return_t+11
y_best_alpha_1_t+11
y_best_beta_1_t+11
y_best_omega_t+11
y_best_mu_t+11
y_best_nu_tgarch_t+11
rv_t+12
lg_return_t+12
y_best_alpha_1_t+12
y_best_beta_1_t+12
y_best_omega_t+12
y_best_mu_t+12
y_best_nu_tgarch_t+12
rv_t+13
lg_return_t+13
y_best_alpha_1_t+13
y_best_beta_1_t+13
y_best_omega_t+13
y_best_mu_t+13
y_best_nu_tgarch_t+13
rv_t+14
lg_return_t+14
y_best_alpha_1_t+14
y_best_beta_1_t+14
y_best_omega_t+14
y_best_mu_t+14
y_best_nu_tgarch_t+14
rv_t+15
lg_return_t+15
y_best_alpha_1_t+15
y_best_beta_1_t+15
y_best_omega_t+15
y_best_mu_t+15
y_best_nu_tgarch_t+15
rv_t+16
lg_return_t+16
y_best_alpha_1_t+16
y_best_beta_1_t+16
y_best_omega_t+16
y_best_mu_t+16
y_best_nu_tgarch_t+16
rv_t+17
lg_return_t+17
y_best_alpha_1_t+17
y_best_beta_1_t+17
y_best_omega_t+17
y_best_mu_t+17
y_best_nu_tgarch_t+17
rv_t+18
lg_return_t+18
y_best_alpha_1_t+18
y_best_beta_1_t+18
y_best_omega_t+18
y_best_mu_t+18
y_best_nu_tgarch_t+18
rv_t+19
lg_return_t+19
y_best_alpha_1_t+19
y_best_beta_1_t+19
y_best_omega_t+19
y_best_mu_t+19
y_best_nu_tgarch_t+19
rv_t+20
lg_return_t+20
y_best_alpha_1_t+20
y_best_beta_1_t+20
y_best_omega_t+20
y_best_mu_t+20
y_best_nu_tgarch_t+20
rv_t+21
lg_return_t+21
y_best_alpha_1_t+21
y_best_beta_1_t+21
y_best_omega_t+21
y_best_mu_t+21
y_best_nu_tgarch_t+21
rv_t+22
lg_return_t+22
y_best_alpha_1_t+22
y_best_beta_1_t+22
y_best_omega_t+22
y_best_mu_t+22
y_best_nu_tgarch_t+22
rv_t+23
lg_return_t+23
y_best_alpha_1_t+23
y_best_beta_1_t+23
y_best_omega_t+23
y_best_mu_t+23
y_best_nu_tgarch_t+23
rv_t+24
lg_return_t+24
y_best_alpha_1_t+24
y_best_beta_1_t+24
y_best_omega_t+24
y_best_mu_t+24
y_best_nu_tgarch_t+24
rv_t+25
lg_return_t+25
y_best_alpha_1_t+25
y_best_beta_1_t+25
y_best_omega_t+25
y_best_mu_t+25
y_best_nu_tgarch_t+25
rv_t+26
lg_return_t+26
y_best_alpha_1_t+26
y_best_beta_1_t+26
y_best_omega_t+26
y_best_mu_t+26
y_best_nu_tgarch_t+26
rv_t+27
lg_return_t+27
y_best_alpha_1_t+27
y_best_beta_1_t+27
y_best_omega_t+27
y_best_mu_t+27
y_best_nu_tgarch_t+27
rv_t+28
lg_return_t+28
y_best_alpha_1_t+28
y_best_beta_1_t+28
y_best_omega_t+28
y_best_mu_t+28
y_best_nu_tgarch_t+28
rv_t+29
lg_return_t+29
y_best_alpha_1_t+29
y_best_beta_1_t+29
y_best_omega_t+29
y_best_mu_t+29
y_best_nu_tgarch_t+29
rv_t+30
lg_return_t+30
y_best_alpha_1_t+30
y_best_beta_1_t+30
y_best_omega_t+30
y_best_mu_t+30
y_best_nu_tgarch_t+30
rv_t+31
lg_return_t+31
y_best_alpha_1_t+31
y_best_beta_1_t+31
y_best_omega_t+31
y_best_mu_t+31
y_best_nu_tgarch_t+31
rv_t+32
lg_return_t+32
y_best_alpha_1_t+32
y_best_beta_1_t+32
y_best_omega_t+32
y_best_mu_t+32
y_best_nu_tgarch_t+32
rv_t+33
lg_return_t+33
y_best_alpha_1_t+33
y_best_beta_1_t+33
y_best_omega_t+33
y_best_mu_t+33
y_best_nu_tgarch_t+33
rv_t+34
lg_return_t+34
y_best_alpha_1_t+34
y_best_beta_1_t+34
y_best_omega_t+34
y_best_mu_t+34
y_best_nu_tgarch_t+34
rv_t+35
lg_return_t+35
y_best_alpha_1_t+35
y_best_beta_1_t+35
y_best_omega_t+35
y_best_mu_t+35
y_best_nu_tgarch_t+35
rv_t+36
lg_return_t+36
y_best_alpha_1_t+36
y_best_beta_1_t+36
y_best_omega_t+36
y_best_mu_t+36
y_best_nu_tgarch_t+36
rv_t+37
lg_return_t+37
y_best_alpha_1_t+37
y_best_beta_1_t+37
y_best_omega_t+37
y_best_mu_t+37
y_best_nu_tgarch_t+37
rv_t+38
lg_return_t+38
y_best_alpha_1_t+38
y_best_beta_1_t+38
y_best_omega_t+38
y_best_mu_t+38
y_best_nu_tgarch_t+38
rv_t+39
lg_return_t+39
y_best_alpha_1_t+39
y_best_beta_1_t+39
y_best_omega_t+39
y_best_mu_t+39
y_best_nu_tgarch_t+39
rv_t+40
lg_return_t+40
y_best_alpha_1_t+40
y_best_beta_1_t+40
y_best_omega_t+40
y_best_mu_t+40
y_best_nu_tgarch_t+40
rv_t+41
lg_return_t+41
y_best_alpha_1_t+41
y_best_beta_1_t+41
y_best_omega_t+41
y_best_mu_t+41
y_best_nu_tgarch_t+41
rv_t+42
lg_return_t+42
y_best_alpha_1_t+42
y_best_beta_1_t+42
y_best_omega_t+42
y_best_mu_t+42
y_best_nu_tgarch_t+42
rv_t+43
lg_return_t+43
y_best_alpha_1_t+43
y_best_beta_1_t+43
y_best_omega_t+43
y_best_mu_t+43
y_best_nu_tgarch_t+43
rv_t+44
lg_return_t+44
y_best_alpha_1_t+44
y_best_beta_1_t+44
y_best_omega_t+44
y_best_mu_t+44
y_best_nu_tgarch_t+44
rv_t+45
lg_return_t+45
y_best_alpha_1_t+45
y_best_beta_1_t+45
y_best_omega_t+45
y_best_mu_t+45
y_best_nu_tgarch_t+45
rv_t+46
lg_return_t+46
y_best_alpha_1_t+46
y_best_beta_1_t+46
y_best_omega_t+46
y_best_mu_t+46
y_best_nu_tgarch_t+46
rv_t+47
lg_return_t+47
y_best_alpha_1_t+47
y_best_beta_1_t+47
y_best_omega_t+47
y_best_mu_t+47
y_best_nu_tgarch_t+47
rv_t+48
lg_return_t+48
y_best_alpha_1_t+48
y_best_beta_1_t+48
y_best_omega_t+48
y_best_mu_t+48
y_best_nu_tgarch_t+48
rv_t+49
lg_return_t+49
y_best_alpha_1_t+49
y_best_beta_1_t+49
y_best_omega_t+49
y_best_mu_t+49
y_best_nu_tgarch_t+49
rv_t+50
lg_return_t+50
y_best_alpha_1_t+50
y_best_beta_1_t+50
y_best_omega_t+50
y_best_mu_t+50
y_best_nu_tgarch_t+50
rv_t+51
lg_return_t+51
y_best_alpha_1_t+51
y_best_beta_1_t+51
y_best_omega_t+51
y_best_mu_t+51
y_best_nu_tgarch_t+51
rv_t+52
lg_return_t+52
y_best_alpha_1_t+52
y_best_beta_1_t+52
y_best_omega_t+52
y_best_mu_t+52
y_best_nu_tgarch_t+52
rv_t+53
lg_return_t+53
y_best_alpha_1_t+53
y_best_beta_1_t+53
y_best_omega_t+53
y_best_mu_t+53
y_best_nu_tgarch_t+53
rv_t+54
lg_return_t+54
y_best_alpha_1_t+54
y_best_beta_1_t+54
y_best_omega_t+54
y_best_mu_t+54
y_best_nu_tgarch_t+54
rv_t+55
lg_return_t+55
y_best_alpha_1_t+55
y_best_beta_1_t+55
y_best_omega_t+55
y_best_mu_t+55
y_best_nu_tgarch_t+55
rv_t+56
lg_return_t+56
y_best_alpha_1_t+56
y_best_beta_1_t+56
y_best_omega_t+56
y_best_mu_t+56
y_best_nu_tgarch_t+56
rv_t+57
lg_return_t+57
y_best_alpha_1_t+57
y_best_beta_1_t+57
y_best_omega_t+57
y_best_mu_t+57
y_best_nu_tgarch_t+57
rv_t+58
lg_return_t+58
y_best_alpha_1_t+58
y_best_beta_1_t+58
y_best_omega_t+58
y_best_mu_t+58
y_best_nu_tgarch_t+58
rv_t+59
lg_return_t+59
y_best_alpha_1_t+59
y_best_beta_1_t+59
y_best_omega_t+59
y_best_mu_t+59
y_best_nu_tgarch_t+59
rv_t+60
lg_return_t+60
y_best_alpha_1_t+60
y_best_beta_1_t+60
y_best_omega_t+60
y_best_mu_t+60
y_best_nu_tgarch_t+60
Checking aapl_price:
Shape: (2018, 60, 7)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 357020901.0
Min value: -0.166803569186452
Checking aapl_time:
Shape: (2018, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.0
Min value: -1.0
Checking aapl_y:
Shape: (2018, 60, 7)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 18807.929648208163
Min value: -135.29444775864093
shape(X) for AAPL:
(2018, 60, 7)
X for AAPL:
[[[6.76504706e-01 3.39139590e+07 1.64639200e-01 ... 2.22700829e-01
9.08427206e-02 3.42649277e+00]
[1.05204846e+00 3.62626110e+07 1.65286883e-01 ... 2.57627872e-01
1.02183271e-01 3.41951773e+00]
[6.63962853e-01 2.67453480e+07 1.63020134e-01 ... 2.56669021e-01
1.08512804e-01 3.44750609e+00]
...
[5.64998964e-01 2.25292560e+07 2.01842972e-01 ... 3.08889996e-01
1.21153897e-01 2.95237762e+00]
[3.58117505e-01 2.25897220e+07 2.06529393e-01 ... 3.19430141e-01
1.15773904e-01 2.86080882e+00]
[4.32127322e-01 2.15473710e+07 2.02593361e-01 ... 2.98567078e-01
1.12065441e-01 2.87163649e+00]]
[[1.05204846e+00 3.62626110e+07 1.65286883e-01 ... 2.57627872e-01
1.02183271e-01 3.41951773e+00]
[6.63962853e-01 2.67453480e+07 1.63020134e-01 ... 2.56669021e-01
1.08512804e-01 3.44750609e+00]
[8.86629800e-01 2.97761640e+07 1.63411349e-01 ... 2.43772670e-01
1.09771755e-01 3.42531078e+00]
...
[3.58117505e-01 2.25897220e+07 2.06529393e-01 ... 3.19430141e-01
1.15773904e-01 2.86080882e+00]
[4.32127322e-01 2.15473710e+07 2.02593361e-01 ... 2.98567078e-01
1.12065441e-01 2.87163649e+00]
[4.41486834e-01 2.08974510e+07 2.06604778e-01 ... 2.95875266e-01
1.11179457e-01 2.85269742e+00]]
[[6.63962853e-01 2.67453480e+07 1.63020134e-01 ... 2.56669021e-01
1.08512804e-01 3.44750609e+00]
[8.86629800e-01 2.97761640e+07 1.63411349e-01 ... 2.43772670e-01
1.09771755e-01 3.42531078e+00]
[9.15033653e-01 2.91556670e+07 1.62962724e-01 ... 2.40149969e-01
1.12142138e-01 3.40853826e+00]
...
[4.32127322e-01 2.15473710e+07 2.02593361e-01 ... 2.98567078e-01
1.12065441e-01 2.87163649e+00]
[4.41486834e-01 2.08974510e+07 2.06604778e-01 ... 2.95875266e-01
1.11179457e-01 2.85269742e+00]
[1.36512504e+00 2.90532960e+07 2.04656453e-01 ... 2.93698805e-01
1.13288622e-01 2.86650287e+00]]
...
[[5.06469051e+00 4.96307250e+07 1.16562350e-01 ... 1.83034562e+00
1.23134418e-01 4.59207242e+00]
[2.68596134e+00 3.94352940e+07 1.14335186e-01 ... 1.82517375e+00
1.17654233e-01 4.63767933e+00]
[3.22404246e+00 3.98319690e+07 1.13544083e-01 ... 1.81937648e+00
1.13756999e-01 4.63165513e+00]
...
[3.08638235e+01 1.25908914e+08 4.71686893e-02 ... 2.25135409e-01
1.90740443e-01 3.31304220e+00]
[8.46587393e+01 1.60466286e+08 2.71731667e-01 ... 2.59116206e+00
1.98313673e-01 3.31323607e+00]
[2.67920976e+01 1.20853216e+08 2.89938370e-01 ... 2.53524775e+00
1.95548084e-01 3.35768112e+00]]
[[2.68596134e+00 3.94352940e+07 1.14335186e-01 ... 1.82517375e+00
1.17654233e-01 4.63767933e+00]
[3.22404246e+00 3.98319690e+07 1.13544083e-01 ... 1.81937648e+00
1.13756999e-01 4.63165513e+00]
[3.34604999e+00 7.17590520e+07 1.09884175e-01 ... 1.82818521e+00
1.20829087e-01 4.70491215e+00]
...
[8.46587393e+01 1.60466286e+08 2.71731667e-01 ... 2.59116206e+00
1.98313673e-01 3.31323607e+00]
[2.67920976e+01 1.20853216e+08 2.89938370e-01 ... 2.53524775e+00
1.95548084e-01 3.35768112e+00]
[1.12407504e+02 1.84386792e+08 3.23695156e-01 ... 2.19907159e+00
2.02372852e-01 3.32020312e+00]]
[[3.22404246e+00 3.98319690e+07 1.13544083e-01 ... 1.81937648e+00
1.13756999e-01 4.63165513e+00]
[3.34604999e+00 7.17590520e+07 1.09884175e-01 ... 1.82818521e+00
1.20829087e-01 4.70491215e+00]
[1.80367858e+00 6.84883010e+07 1.35468567e-01 ... 1.87419340e+00
1.22216999e-01 4.45644413e+00]
...
[2.67920976e+01 1.20853216e+08 2.89938370e-01 ... 2.53524775e+00
1.95548084e-01 3.35768112e+00]
[1.12407504e+02 1.84386792e+08 3.23695156e-01 ... 2.19907159e+00
2.02372852e-01 3.32020312e+00]
[3.09586134e+01 1.21879981e+08 2.61815546e-01 ... 9.34619600e-01
2.16608185e-01 3.20569311e+00]]]
shape(Time) for AAPL:
(2018, 60, 14)
Time for AAPL:
[[[-4.33883739e-01 -9.00968868e-01 9.37752132e-01 ... 0.00000000e+00
0.00000000e+00 0.00000000e+00]
[ 0.00000000e+00 1.00000000e+00 9.68077119e-01 ... 0.00000000e+00
9.65872505e-04 0.00000000e+00]
[ 7.81831482e-01 6.23489802e-01 8.97804540e-01 ... 0.00000000e+00
1.28783001e-03 0.00000000e+00]
...
[ 4.33883739e-01 -9.00968868e-01 -2.01298520e-01 ... 0.00000000e+00
2.67224726e-02 1.25461255e-01]
[-4.33883739e-01 -9.00968868e-01 -2.44929360e-16 ... 0.00000000e+00
2.70444301e-02 1.25461255e-01]
[ 0.00000000e+00 1.00000000e+00 5.71268215e-01 ... 0.00000000e+00
2.80103026e-02 1.88191882e-01]]
[[ 0.00000000e+00 1.00000000e+00 9.68077119e-01 ... 0.00000000e+00
9.65872505e-04 0.00000000e+00]
[ 7.81831482e-01 6.23489802e-01 8.97804540e-01 ... 0.00000000e+00
1.28783001e-03 0.00000000e+00]
[ 9.74927912e-01 -2.22520934e-01 7.90775737e-01 ... 0.00000000e+00
1.60978751e-03 0.00000000e+00]
...
[-4.33883739e-01 -9.00968868e-01 -2.44929360e-16 ... 0.00000000e+00
2.70444301e-02 1.25461255e-01]
[ 0.00000000e+00 1.00000000e+00 5.71268215e-01 ... 0.00000000e+00
2.80103026e-02 1.88191882e-01]
[ 7.81831482e-01 6.23489802e-01 7.24792787e-01 ... 0.00000000e+00
2.83322601e-02 1.88191882e-01]]
[[ 7.81831482e-01 6.23489802e-01 8.97804540e-01 ... 0.00000000e+00
1.28783001e-03 0.00000000e+00]
[ 9.74927912e-01 -2.22520934e-01 7.90775737e-01 ... 0.00000000e+00
1.60978751e-03 0.00000000e+00]
[ 4.33883739e-01 -9.00968868e-01 6.51372483e-01 ... 0.00000000e+00
1.93174501e-03 0.00000000e+00]
...
[ 0.00000000e+00 1.00000000e+00 5.71268215e-01 ... 0.00000000e+00
2.80103026e-02 1.88191882e-01]
[ 7.81831482e-01 6.23489802e-01 7.24792787e-01 ... 0.00000000e+00
2.83322601e-02 1.88191882e-01]
[ 9.74927912e-01 -2.22520934e-01 8.48644257e-01 ... 0.00000000e+00
2.86542176e-02 1.88191882e-01]]
...
[[ 0.00000000e+00 1.00000000e+00 4.85301963e-01 ... 1.00000000e+00
9.43013522e-01 2.95202952e-02]
[ 7.81831482e-01 6.23489802e-01 2.99363123e-01 ... 1.00000000e+00
9.43335480e-01 2.95202952e-02]
[ 9.74927912e-01 -2.22520934e-01 1.01168322e-01 ... 1.00000000e+00
9.43657437e-01 2.95202952e-02]
...
[-4.33883739e-01 -9.00968868e-01 7.24792787e-01 ... 1.00000000e+00
9.69092080e-01 3.06273063e-01]
[ 0.00000000e+00 1.00000000e+00 9.88468324e-01 ... 1.00000000e+00
9.70057952e-01 3.06273063e-01]
[ 7.81831482e-01 6.23489802e-01 9.98716507e-01 ... 1.00000000e+00
9.70379910e-01 3.06273063e-01]]
[[ 7.81831482e-01 6.23489802e-01 2.99363123e-01 ... 1.00000000e+00
9.43335480e-01 2.95202952e-02]
[ 9.74927912e-01 -2.22520934e-01 1.01168322e-01 ... 1.00000000e+00
9.43657437e-01 2.95202952e-02]
[ 4.33883739e-01 -9.00968868e-01 -1.01168322e-01 ... 1.00000000e+00
9.43979395e-01 2.95202952e-02]
...
[ 0.00000000e+00 1.00000000e+00 9.88468324e-01 ... 1.00000000e+00
9.70057952e-01 3.06273063e-01]
[ 7.81831482e-01 6.23489802e-01 9.98716507e-01 ... 1.00000000e+00
9.70379910e-01 3.06273063e-01]
[ 9.74927912e-01 -2.22520934e-01 9.68077119e-01 ... 1.00000000e+00
9.70701867e-01 3.06273063e-01]]
[[ 9.74927912e-01 -2.22520934e-01 1.01168322e-01 ... 1.00000000e+00
9.43657437e-01 2.95202952e-02]
[ 4.33883739e-01 -9.00968868e-01 -1.01168322e-01 ... 1.00000000e+00
9.43979395e-01 2.95202952e-02]
[-4.33883739e-01 -9.00968868e-01 -2.99363123e-01 ... 1.00000000e+00
9.44301352e-01 2.95202952e-02]
...
[ 7.81831482e-01 6.23489802e-01 9.98716507e-01 ... 1.00000000e+00
9.70379910e-01 3.06273063e-01]
[ 9.74927912e-01 -2.22520934e-01 9.68077119e-01 ... 1.00000000e+00
9.70701867e-01 3.06273063e-01]
[ 4.33883739e-01 -9.00968868e-01 8.97804540e-01 ... 1.00000000e+00
9.71023825e-01 3.06273063e-01]]]
shape(Y) for AAPL:
(2018, 60, 7)
Y for AAPL:
[[[ 4.41486834e-01 7.41848308e-01 1.88166974e-01 ... 3.01510467e-01
1.13948224e-01 3.07411399e+00]
[ 1.36512504e+00 -5.19409735e-01 1.83240115e-01 ... 2.95823997e-01
1.16869956e-01 3.08190592e+00]
[ 8.66251987e-01 -2.50278217e-01 1.78770944e-01 ... 2.81516914e-01
1.16380525e-01 3.16053146e+00]
...
[ 1.41220190e+00 -3.14960890e-01 2.08036115e-01 ... 3.14029331e-01
1.15914948e-01 2.88032185e+00]
[ 1.56907991e+00 -1.44364448e+00 1.99235095e-01 ... 3.16129285e-01
1.21747294e-01 2.92337593e+00]
[ 3.81369644e+00 1.45050202e+00 1.96403318e-01 ... 3.07975012e-01
1.24363615e-01 2.92378402e+00]]
[[ 1.36512504e+00 -5.19409735e-01 1.83240115e-01 ... 2.95823997e-01
1.16869956e-01 3.08190592e+00]
[ 8.66251987e-01 -2.50278217e-01 1.78770944e-01 ... 2.81516914e-01
1.16380525e-01 3.16053146e+00]
[ 6.32251338e-01 -2.22996608e-01 1.80024678e-01 ... 2.88854204e-01
1.08825694e-01 3.08464777e+00]
...
[ 1.56907991e+00 -1.44364448e+00 1.99235095e-01 ... 3.16129285e-01
1.21747294e-01 2.92337593e+00]
[ 3.81369644e+00 1.45050202e+00 1.96403318e-01 ... 3.07975012e-01
1.24363615e-01 2.92378402e+00]
[ 1.95125755e+00 -1.48529552e+00 1.97948067e-01 ... 3.16598413e-01
1.29260535e-01 2.89330494e+00]]
[[ 8.66251987e-01 -2.50278217e-01 1.78770944e-01 ... 2.81516914e-01
1.16380525e-01 3.16053146e+00]
[ 6.32251338e-01 -2.22996608e-01 1.80024678e-01 ... 2.88854204e-01
1.08825694e-01 3.08464777e+00]
[ 4.93781630e-01 -1.18669519e-01 1.81208451e-01 ... 2.99632060e-01
1.23042324e-01 2.98320687e+00]
...
[ 3.81369644e+00 1.45050202e+00 1.96403318e-01 ... 3.07975012e-01
1.24363615e-01 2.92378402e+00]
[ 1.95125755e+00 -1.48529552e+00 1.97948067e-01 ... 3.16598413e-01
1.29260535e-01 2.89330494e+00]
[ 1.69170531e+00 2.36357427e-01 2.04112876e-01 ... 3.53462987e-01
1.26904646e-01 2.92913158e+00]]
...
[[ 1.12407504e+02 1.42617411e+01 2.46518753e-01 ... 7.49091848e-01
2.34520318e-01 3.04392012e+00]
[ 3.09586134e+01 -4.33186128e+00 2.56066556e-01 ... 7.74816906e-01
2.41687578e-01 2.95369362e+00]
[ 1.62449203e+01 3.97921608e+00 2.51671100e-01 ... 8.06168063e-01
2.39529877e-01 2.99872318e+00]
...
[ 2.76021631e+00 2.19872751e+00 1.97787490e-01 ... 7.40407270e-01
1.82203879e-01 3.04172446e+00]
[ 1.76195374e+00 5.21140173e-01 1.88880007e-01 ... 7.02716465e-01
1.79924591e-01 3.15133418e+00]
[ 2.61004026e+00 -1.70015902e+00 1.88020982e-01 ... 6.67213086e-01
1.75567247e-01 3.25255026e+00]]
[[ 3.09586134e+01 -4.33186128e+00 2.56066556e-01 ... 7.74816906e-01
2.41687578e-01 2.95369362e+00]
[ 1.62449203e+01 3.97921608e+00 2.51671100e-01 ... 8.06168063e-01
2.39529877e-01 2.99872318e+00]
[ 5.49327509e+01 2.18143275e+00 2.68561502e-01 ... 8.83130950e-01
2.43072282e-01 2.99819484e+00]
...
[ 1.76195374e+00 5.21140173e-01 1.88880007e-01 ... 7.02716465e-01
1.79924591e-01 3.15133418e+00]
[ 2.61004026e+00 -1.70015902e+00 1.88020982e-01 ... 6.67213086e-01
1.75567247e-01 3.25255026e+00]
[ 1.93572910e+00 2.85741501e-02 1.88020982e-01 ... 6.67213086e-01
1.75567247e-01 3.25255026e+00]]
[[ 1.62449203e+01 3.97921608e+00 2.51671100e-01 ... 8.06168063e-01
2.39529877e-01 2.99872318e+00]
[ 5.49327509e+01 2.18143275e+00 2.68561502e-01 ... 8.83130950e-01
2.43072282e-01 2.99819484e+00]
[ 6.46699860e+00 -1.87812046e-01 2.66459692e-01 ... 8.44436372e-01
2.50342693e-01 2.84838905e+00]
...
[ 2.61004026e+00 -1.70015902e+00 1.88020982e-01 ... 6.67213086e-01
1.75567247e-01 3.25255026e+00]
[ 1.93572910e+00 2.85741501e-02 1.88020982e-01 ... 6.67213086e-01
1.75567247e-01 3.25255026e+00]
[ 1.71835643e+00 5.36627193e-01 1.88020982e-01 ... 6.67213086e-01
1.75567247e-01 3.25255026e+00]]]
Some aditional tests to check how the data is ouputed with just on target variable¶
In [92]:
additional_test_data = df_to_raw_X_y_dictionary(df_dict["AAPL"], ticker="AAPL", rv_y = True, garch_var_x= False, best_param_x=False, best_param_y=False)
y_additional_test_data = additional_test_data["y"]
check_data_issues(y_additional_test_data, "AAPL additional test data")
Checking AAPL additional test data: Shape: (2018, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 18807.929648208163 Min value: 0.250100463008116
Model evaluation metrics¶
Helper functions¶
Some helper function that can compute relevant metrics¶
In [93]:
import numpy as np
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
def evaluate_predictions(
y_true,
y_pred,
horizons=[1, 3, 5, 10, 20, -1],
epsilon=1e-12,
qlike_mode="ratio",
qlike_floor="auto",
qlike_calibrate=True
):
results = {}
assert y_true.ndim == 2 and y_pred.ndim == 2
assert y_true.shape == y_pred.shape
T = y_true.shape[1]
def _auto_floor(arr):
arr = np.asarray(arr, dtype=np.float64)
pos = arr[np.isfinite(arr) & (arr > 0)]
if pos.size == 0:
return float(epsilon)
base = np.percentile(pos, 5.0)
return float(max(epsilon, 1e-6 * base))
for h in horizons:
label = f"{h} day(s)" if h > 0 else "full horizon"
h_slice = h if h > 0 else T
if h_slice > T:
continue
yt = y_true[:, :h_slice].astype(np.float64).ravel()
yp = y_pred[:, :h_slice].astype(np.float64).ravel()
mask = np.isfinite(yt) & np.isfinite(yp)
yt = yt[mask]
yp = yp[mask]
if yt.size == 0:
results[f"{label} MAE"] = np.nan
results[f"{label} RMSE"] = np.nan
results[f"{label} R2"] = np.nan
results[f"{label} Pearson r"] = np.nan
results[f"{label} QLIKE"] = np.nan
continue
mae = float(mean_absolute_error(yt, yp))
rmse = float(np.sqrt(mean_squared_error(yt, yp)))
r2 = float(r2_score(yt, yp))
ytm = yt - yt.mean()
ypm = yp - yp.mean()
denom = np.sqrt((ytm**2).mean() * (ypm**2).mean())
pearson_r = float((ytm*ypm).mean() / denom) if denom > epsilon else np.nan
if qlike_floor == "auto":
floor = _auto_floor(yt)
else:
floor = max(float(qlike_floor), float(epsilon))
yt_pos = np.clip(yt, floor, None)
yp_pos = np.clip(yp, floor, None)
if qlike_calibrate:
c = float(np.mean(yt_pos / yp_pos))
yp_pos = np.clip(yp_pos * c, floor, None)
if qlike_mode == "ratio":
r = yt_pos / yp_pos
qlike = float(np.mean(r - np.log(r) - 1.0))
elif qlike_mode == "log":
qlike = float(np.mean(np.log(yp_pos) + (yt_pos / yp_pos)))
else:
raise ValueError("qlike_mode must be 'ratio' or 'log'")
results[f"{label} MAE"] = mae
results[f"{label} RMSE"] = rmse
results[f"{label} R2"] = r2
results[f"{label} Pearson r"] = pearson_r
results[f"{label} QLIKE"] = qlike
return results
Import external libraries¶
In [94]:
import sys
external_libraries_relative_path = 'External_libraries'
time_series_library_relative_path = 'Time-Series-Library'
external_libraries_path = os.path.join(root_folder, external_libraries_relative_path)
time_series_library_path = os.path.join(external_libraries_path, time_series_library_relative_path)
print("Times-Series-Library repo path:", time_series_library_path)
sys.path.append(time_series_library_path)
Times-Series-Library repo path: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/External_libraries/Time-Series-Library
Install some dependencies¶
In [95]:
!pip install reformer-pytorch
Requirement already satisfied: reformer-pytorch in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (1.4.4) Requirement already satisfied: axial-positional-embedding>=0.1.0 in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from reformer-pytorch) (0.3.12) Requirement already satisfied: einops in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from reformer-pytorch) (0.8.1) Requirement already satisfied: local-attention in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from reformer-pytorch) (1.11.2) Requirement already satisfied: product-key-memory in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from reformer-pytorch) (0.2.11) Requirement already satisfied: torch in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from reformer-pytorch) (2.8.0) Requirement already satisfied: filelock in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from torch->reformer-pytorch) (3.19.1) Requirement already satisfied: typing-extensions>=4.10.0 in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from torch->reformer-pytorch) (4.12.2) Requirement already satisfied: sympy>=1.13.3 in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from torch->reformer-pytorch) (1.14.0) Requirement already satisfied: networkx in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from torch->reformer-pytorch) (3.4.2) Requirement already satisfied: jinja2 in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from torch->reformer-pytorch) (3.1.6) Requirement already satisfied: fsspec in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from torch->reformer-pytorch) (2025.7.0) Requirement already satisfied: mpmath<1.4,>=1.1.0 in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from sympy>=1.13.3->torch->reformer-pytorch) (1.3.0) Requirement already satisfied: MarkupSafe>=2.0 in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from jinja2->torch->reformer-pytorch) (3.0.2) Requirement already satisfied: hyper-connections>=0.1.8 in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from local-attention->reformer-pytorch) (0.2.1) Requirement already satisfied: colt5-attention>=0.10.14 in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from product-key-memory->reformer-pytorch) (0.11.1) Requirement already satisfied: packaging in /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages (from colt5-attention>=0.10.14->product-key-memory->reformer-pytorch) (25.0)
Some helper functions to process 3D tensors¶
In [96]:
from sklearn.preprocessing import MinMaxScaler, StandardScaler
import numpy as np
def combine_3D_inputs(X1, X2):
return np.concatenate([X1, X2], axis=2)
def scale_3d_array(array, type="standard"):
if array.ndim != 3:
raise ValueError("Input array must be 3D (batch, time, features)")
B, T, F = array.shape
reshaped = array.reshape(-1, F)
if type == "min_max":
scaler = MinMaxScaler(clip=False)
elif type == "standard":
scaler = StandardScaler()
else:
raise ValueError("Unknown scaling type. Use 'min_max' or 'standard'.")
scaled = scaler.fit_transform(reshaped).reshape(B, T, F)
return scaled, scaler
def apply_scaler_to_3d_array(array, scaler):
if array is None:
return None
B, T, F = array.shape
reshaped = array.reshape(-1, F)
scaled = scaler.transform(reshaped).reshape(B, T, F)
return scaled
def inverse_scale_3d(array, scaler):
if array is None:
return None
B, T, F = array.shape
reshaped = array.reshape(-1, F)
inv = scaler.inverse_transform(reshaped).reshape(B, T, F)
return inv
def flatten_3d_tensor_by_feature_first(array):
if array.ndim != 3:
raise ValueError("Expected a 3D array of shape (batch, time, features)")
return array.transpose(0, 2, 1).reshape(array.shape[0], -1)
def flatten_3d_tensor_standard(array):
if array.ndim != 3:
raise ValueError("Expected a 3D array of shape (batch_size, time_steps, features)")
return array.reshape(array.shape[0], -1)
def unflatten_3d_tensor_standard(array, time_steps, features):
if array.ndim != 2:
raise ValueError("Expected a 2D array (batch_size, time_steps * features)")
batch_size = array.shape[0]
expected_dim = time_steps * features
if array.shape[1] != expected_dim:
raise ValueError(f"Expected second dimension to be {expected_dim}, got {array.shape[1]}")
return array.reshape(batch_size, time_steps, features)
def unflatten_3d_tensor_by_feature_first(array, time_steps, features):
if array.ndim != 2:
raise ValueError("Expected a 2D array (batch_size, features * time_steps)")
batch_size = array.shape[0]
expected_dim = features * time_steps
if array.shape[1] != expected_dim:
raise ValueError(f"Expected second dimension to be {expected_dim}, got {array.shape[1]}")
reshaped = array.reshape(batch_size, features, time_steps)
return reshaped.transpose(0, 2, 1)
Some functions to do checking on the measures computed with keras backbone¶
In [97]:
import numpy as np
def probe_scaled_and_unscaled(model, X_test, y_test_scaled, scaler_y, task_idx=None):
pred_scaled = model.model_.predict(X_test, verbose=0) if hasattr(model, "model_") and model.model_ is not None else model.predict(X_test)
if pred_scaled.ndim == 2: # (N,H)
pred_scaled = pred_scaled[..., None]
if y_test_scaled.ndim == 2:
y_test_scaled = y_test_scaled[..., None]
N, H, F = y_test_scaled.shape
assert pred_scaled.shape == (N, H, F), f"Shape mismatch: pred {pred_scaled.shape} vs true {y_test_scaled.shape}"
if task_idx is not None:
ps = pred_scaled[..., task_idx:task_idx+1]
ys = y_test_scaled[..., task_idx:task_idx+1]
mse_s = np.mean((ps - ys) ** 2)
rmse_s = float(np.sqrt(mse_s))
mae_s = float(np.mean(np.abs(ps - ys)))
if hasattr(scaler_y, "data_range_"):
scale = float(scaler_y.data_range_[task_idx])
shift = float(scaler_y.data_min_[task_idx])
pu = ps * scale + shift
tu = ys * scale + shift
rmse_u_exp = rmse_s * scale
mae_u_exp = mae_s * scale
elif hasattr(scaler_y, "scale_"):
scale = float(scaler_y.scale_[task_idx])
shift = float(scaler_y.mean_[task_idx])
pu = ps * scale + shift
tu = ys * scale + shift
rmse_u_exp = rmse_s * scale
mae_u_exp = mae_s * scale
else:
raise ValueError("Unsupported scaler type; expected MinMaxScaler or StandardScaler.")
rmse_u = float(np.sqrt(np.mean((pu - tu) ** 2)))
mae_u = float(np.mean(np.abs(pu - tu)))
return {
"rmse_scaled": rmse_s,
"mae_scaled": mae_s,
"rmse_unscaled_expected": rmse_u_exp,
"mae_unscaled_expected": mae_u_exp,
"rmse_unscaled": rmse_u,
"mae_unscaled": mae_u,
}
diff_s = pred_scaled - y_test_scaled
rmse_s = float(np.sqrt(np.mean(diff_s ** 2)))
mae_s = float(np.mean(np.abs(diff_s)))
if hasattr(scaler_y, "data_range_"):
scales = scaler_y.data_range_.reshape(1, 1, F)
shifts = scaler_y.data_min_.reshape(1, 1, F)
diff_u_exp = diff_s * scales
elif hasattr(scaler_y, "scale_"):
scales = scaler_y.scale_.reshape(1, 1, F)
shifts = scaler_y.mean_.reshape(1, 1, F)
diff_u_exp = diff_s * scales
else:
raise ValueError("Unsupported scaler type; expected MinMaxScaler or StandardScaler.")
rmse_u_exp = float(np.sqrt(np.mean(diff_u_exp ** 2)))
mae_u_exp = float(np.mean(np.abs(diff_u_exp)))
pred_u = scaler_y.inverse_transform(pred_scaled.reshape(-1, F)).reshape(N, H, F)
true_u = scaler_y.inverse_transform(y_test_scaled.reshape(-1, F)).reshape(N, H, F)
rmse_u = float(np.sqrt(np.mean((pred_u - true_u) ** 2)))
mae_u = float(np.mean(np.abs(pred_u - true_u)))
return {
"rmse_scaled": rmse_s,
"mae_scaled": mae_s,
"rmse_unscaled_expected": rmse_u_exp,
"mae_unscaled_expected": mae_u_exp,
"rmse_unscaled": rmse_u,
"mae_unscaled": mae_u,
}
Main function for training and evaluating models¶
We had to spearate the "actual features" X, from the "time" X because of the scaling logic.
In [98]:
import pickle
import os
import numpy as np
from sklearn.model_selection import KFold, TimeSeriesSplit
from sklearn.metrics import mean_squared_error
from sklearn.base import clone
from sklearn.model_selection import ParameterGrid
from scipy.stats import t
import torch
import random
def train_and_evaluate_model(
X_price,
X_time,
y,
model_type,
*,
target_mode="log_var_ratio",
baseline_feature_idx=0,
baseline_window=10,
baseline_eps=1e-12,
merge_price_time=True,
use_nested_cv=False,
outer_folds=5,
inner_folds=3,
param_grid=None,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
random_state=42,
save_model_path=None,
checkpoint_path=None,
verbose=True,
epochs=50,
batch_size=32,
lr=3e-5,
flatten=True,
no_tasks=1,
trainable_parts=False,
hidden_layers=3,
time_horizon=1,
single_holdout=False,
val_frac=0.10,
debug_scaling=False,
dropout=0.00,
l2_weight=0.00,
patience=10,
min_epochs=50,
min_delta=1e-4,
hidden_dim=16,
knots=8,
spline_power=3,
warmup_aux_epochs=15,
joint_epochs=15,
d_model=64,
d_ff=254,
n_heads=4,
e_layers=2,
y_scale_type="standard",
cv_strategy="time",
metric_for_early_stop="mse_model",
esv_frac=None,
deterministic_torch=False,
final_refit_holdout_frac=0.20,
):
def _reset_seeds(seed, deterministic=False):
random.seed(seed); np.random.seed(seed); torch.manual_seed(seed)
if torch.cuda.is_available(): torch.cuda.manual_seed_all(seed)
if deterministic:
try:
torch.use_deterministic_algorithms(True)
torch.backends.cudnn.benchmark = False
except Exception:
pass
def _cv_splitter(kind, n_splits):
if kind == "time":
return TimeSeriesSplit(n_splits=n_splits)
return KFold(n_splits=n_splits, shuffle=False)
def has_sklearn_api(m):
return hasattr(m, "get_params") and hasattr(m, "set_params")
def safe_clone(m):
if has_sklearn_api(m):
try:
return clone(m)
except Exception:
pass
params = m.get_params() if hasattr(m, "get_params") else {}
return m.__class__(**params)
assert len(y.shape) == 3, "y must be a 3D array (batch, time, features)"
y = y[:, 0:time_horizon, :]
if no_tasks < y.shape[2]:
y = y[:, :, :no_tasks]
if merge_price_time and normalize_X and not normalize_Time:
if verbose:
print("merge_price_time=True with normalize_X=True but normalize_Time=False -> enabling normalize_Time=True for scale alignment.")
normalize_Time = True
def _resolve_mode_and_loss(target_mode, normalize_y):
if target_mode == "log_var_ratio":
loss_type = "mse"; target_is_logvar = False
elif target_mode == "log":
loss_type = "gauss_nll_var"; target_is_logvar = True
if normalize_y:
print("normalize_y=True with NLL(log) → forcing normalize_y=False.")
normalize_y = False
elif target_mode == "identity":
loss_type = "mse"; target_is_logvar = False
elif target_mode == "log_mse":
loss_type = "mse"; target_is_logvar = True
else:
raise ValueError(f"Unknown target_mode: {target_mode}")
return loss_type, target_is_logvar, normalize_y
loss_type, target_is_logvar, normalize_y = _resolve_mode_and_loss(target_mode, normalize_y)
print(f"[mode={target_mode}] loss_type={loss_type}, target_is_logvar={target_is_logvar}, normalize_y={normalize_y}")
EPS_MIN = 1e-12
FLOOR_Y = max(EPS_MIN, baseline_eps)
esv_frac = val_frac if esv_frac is None else esv_frac
def _build_baseline_var_from_Xp(Xp, y_like, feat_idx=0, window=20, eps=1e-12):
B, Ty, Fy = y_like.shape
if Xp.shape[1] < window:
raise ValueError(f"X_price time_steps={Xp.shape[1]} < baseline_window={window}")
x = Xp[:, -window:, feat_idx]
rv = np.mean(np.square(x), axis=1)
rv = np.clip(rv, eps, None)
return np.tile(rv[:, None, None], (1, Ty, Fy))
def _transform_target(y_raw, base3d=None):
if target_mode == "log_var_ratio":
if base3d is None:
raise ValueError("log_var_ratio requires a baseline (base3d).")
y_log = np.log(np.clip(y_raw, FLOOR_Y, None))
b_log = np.log(np.clip(base3d, FLOOR_Y, None))
return (y_log - b_log), {"mode": "log_var_ratio", "base": base3d}
elif target_mode in ("log", "log_mse"):
return np.log(np.clip(y_raw, FLOOR_Y, None)), {"mode": "log"}
elif target_mode == "identity":
return y_raw, {"mode": "identity"}
else:
raise ValueError(f"Unknown target_mode: {target_mode}")
def _inverse_transform_target(y_model_space, ctx):
m = ctx["mode"]
if m == "log_var_ratio":
return ctx["base"] * np.exp(y_model_space)
elif m == "log":
return np.exp(y_model_space)
elif m == "identity":
return y_model_space
else:
raise ValueError(f"Unknown inverse mode: {m}")
def _fit_apply_scalers(Xp_core, Xt_core, y_core,
Xp_val, Xt_val, y_val,
Xp_test, Xt_test, y_test,
do_norm_X=True, do_norm_Time=False, do_norm_y=True,
y_scaler_kind="standard"):
X_price_scaler = None; X_time_scaler = None; y_scaler = None
if do_norm_X:
_, X_price_scaler = scale_3d_array(Xp_core, type="standard")
Xp_core_s = apply_scaler_to_3d_array(Xp_core, X_price_scaler)
Xp_val_s = apply_scaler_to_3d_array(Xp_val, X_price_scaler)
Xp_test_s = apply_scaler_to_3d_array(Xp_test, X_price_scaler)
else:
Xp_core_s, Xp_val_s, Xp_test_s = Xp_core, Xp_val, Xp_test
if do_norm_Time and Xt_core is not None:
_, X_time_scaler = scale_3d_array(Xt_core, type="standard")
Xt_core_s = apply_scaler_to_3d_array(Xt_core, X_time_scaler)
Xt_val_s = apply_scaler_to_3d_array(Xt_val, X_time_scaler)
Xt_test_s = apply_scaler_to_3d_array(Xt_test, X_time_scaler)
else:
Xt_core_s, Xt_val_s, Xt_test_s = Xt_core, Xt_val, Xt_test
if do_norm_y:
_, y_scaler = scale_3d_array(y_core, type=y_scaler_kind)
y_core_s = apply_scaler_to_3d_array(y_core, y_scaler)
y_val_s = apply_scaler_to_3d_array(y_val, y_scaler)
y_test_s = apply_scaler_to_3d_array(y_test, y_scaler)
else:
y_core_s, y_val_s, y_test_s = y_core, y_val, y_test
return (Xp_core_s, Xt_core_s, y_core_s,
Xp_val_s, Xt_val_s, y_val_s,
Xp_test_s, Xt_test_s, y_test_s,
X_price_scaler, X_time_scaler, y_scaler)
def _prep_for_model(Xp, Xt, y_, merge_PT, do_flatten):
X = combine_3D_inputs(Xp, Xt) if (merge_PT and Xt is not None) else Xp
if do_flatten:
X = flatten_3d_tensor_standard(X)
y_ = flatten_3d_tensor_by_feature_first(y_)
return X, y_
def _build_task0_true_pred(y_true_3d, y_pred_3d):
return np.stack([y_true_3d[:, :, 0], y_pred_3d[:, :, 0]], axis=-1)
def _peek_mag(name, arr):
if not debug_scaling or arr is None: return
r = arr.reshape(-1, arr.shape[-1]); top5 = np.sort(np.abs(r).max(axis=0))[-5:]
print(f"{name}: max|x| per feature (top 5): {top5}")
def _flat_dim(x3d):
return 0 if x3d is None else (x3d.shape[1] * x3d.shape[2])
def _feat_dim(x3d):
return 0 if x3d is None else x3d.shape[2]
if model_type == "Simple_MLP":
input_dimension = (X_price.shape[1] * X_price.shape[2] + (_flat_dim(X_time) if merge_price_time else 0))
model = SimpleMLPWrapper(input_dim=input_dimension,
output_dim=y.shape[1] * y.shape[2],
lr=lr, epochs=epochs, batch_size=batch_size,
verbose=verbose, hidden_layers=hidden_layers, no_tasks=no_tasks,
dropout=dropout, l2_weight=l2_weight,
patience=patience, min_epochs=min_epochs, min_delta=min_delta,
hidden_dim=hidden_dim, loss_type=loss_type, target_is_logvar=target_is_logvar)
elif model_type == "Simple_LSTM":
input_dimension = X_price.shape[2] + ((_feat_dim(X_time)) if merge_price_time else 0)
model = SimpleLSTMWrapper(input_dim=input_dimension, epochs=epochs,
verbose=verbose, batch_size=batch_size, lr=lr,
no_tasks=no_tasks, hidden_layers=hidden_layers,
output_dim=y.shape[2], pred_len=y.shape[1],
dropout=dropout, l2_weight=l2_weight,
patience=patience, min_epochs=min_epochs, min_delta=min_delta,
hidden_dim=hidden_dim, loss_type=loss_type, target_is_logvar=target_is_logvar)
elif model_type == "ITransformer":
input_dimension = X_price.shape[2] + ((_feat_dim(X_time)) if merge_price_time else 0)
model = ITransformerWrapper(input_len=X_price.shape[1], input_dim=input_dimension,
output_len=y.shape[1], output_dim=y.shape[2],
epochs=epochs, batch_size=batch_size, lr=lr,
verbose=verbose, no_tasks=no_tasks, dropout=dropout,
l2_weight=l2_weight, patience=patience,
min_epochs=min_epochs, min_delta=min_delta, hidden_dim=hidden_dim,
hidden_layers=hidden_layers,
d_model=d_model, d_ff=d_ff, n_heads=n_heads, e_layers=e_layers,
loss_type=loss_type, target_is_logvar=target_is_logvar)
elif model_type == "TimesNet":
input_dimension = X_price.shape[2] + ((_feat_dim(X_time)) if merge_price_time else 0)
model = TimesNetWrapper(input_len=X_price.shape[1], input_dim=input_dimension,
output_len=y.shape[1], output_dim=y.shape[2],
epochs=epochs, batch_size=batch_size, lr=lr,
verbose=verbose, no_tasks=no_tasks, dropout=dropout,
l2_weight=l2_weight, patience=patience,
min_epochs=min_epochs, min_delta=min_delta, hidden_dim=hidden_dim,
hidden_layers=hidden_layers,
d_model=d_model, d_ff=d_ff, n_heads=n_heads, e_layers=e_layers,
loss_type=loss_type, target_is_logvar=target_is_logvar)
elif model_type == "Custom_KAN":
input_dimension = (X_price.shape[1] * X_price.shape[2] + (_flat_dim(X_time) if merge_price_time else 0))
model = TorchHierarchicalKANWrapper(input_dim=input_dimension,
output_dim=y.shape[1] * y.shape[2],
lr=lr, epochs=epochs, batch_size=batch_size,
verbose=verbose, no_tasks=no_tasks,
knots=8, spline_power=3, dropout=dropout, l2_weight=l2_weight,
patience=patience, min_epochs=min_epochs, min_delta=min_delta,
hidden_dim=hidden_dim, warmup_aux_epochs=warmup_aux_epochs,
joint_epochs=joint_epochs, hidden_layers=hidden_layers,
loss_type=loss_type, target_is_logvar=target_is_logvar)
elif model_type == "Simple_KAN":
input_dimension = (X_price.shape[1] * X_price.shape[2] + (_flat_dim(X_time) if merge_price_time else 0))
model = SimpleKANWrapper(input_dim=input_dimension,
output_dim=y.shape[1] * y.shape[2],
lr=lr, epochs=epochs, batch_size=batch_size,
verbose=verbose, hidden_layers=hidden_layers,
no_tasks=no_tasks, knots=knots, spline_power=spline_power,
dropout=dropout, l2_weight=l2_weight,
patience=patience, min_epochs=min_epochs, min_delta=min_delta,
hidden_dim=hidden_dim, loss_type=loss_type, target_is_logvar=target_is_logvar)
elif model_type == "Custom_KAN_LSTM":
input_dimension = (X_price.shape[1] * X_price.shape[2] + (_flat_dim(X_time) if merge_price_time else 0))
model = TKANHierMTLWrapper(input_dim=input_dimension,
output_dim=y.shape[2],
pred_len=y.shape[1],
lr=lr, epochs=epochs, batch_size=batch_size,
verbose=verbose, hidden_layers=hidden_layers,
no_tasks=no_tasks, knots=knots, spline_power=spline_power,
dropout=dropout, l2_weight=l2_weight,
patience=patience, min_epochs=min_epochs, min_delta=min_delta,
hidden_dim=hidden_dim, loss_type=loss_type, target_is_logvar=target_is_logvar)
elif model_type == "LSTM_KAN":
input_dimension = X_price.shape[2] + ((_feat_dim(X_time)) if merge_price_time else 0)
model = TKANSeq2SeqWrapper(input_dim=input_dimension, epochs=epochs,
verbose=verbose, batch_size=batch_size, lr=lr,
no_tasks=no_tasks, hidden_layers=hidden_layers,
output_dim=y.shape[2], pred_len=y.shape[1],
knots=8, spline_power=3, dropout=dropout, l2_weight=l2_weight,
patience=patience, min_epochs=min_epochs, min_delta=min_delta,
hidden_dim=hidden_dim, loss_type=loss_type, target_is_logvar=target_is_logvar)
else:
raise ValueError("Model type not defined")
results = {}; nested_results = {}
best_model_final = None; best_params_final = None
X_price_train_scaler = None; X_time_train_scaler = None
scaler_y = None; y_task0_true_pred = None
_reset_seeds(random_state, deterministic=deterministic_torch)
batch_size_y, time_steps_y, num_features_y = y.shape
print("Batch size for y:", batch_size_y)
print("Time steps for y:", time_steps_y)
print("Features for y:", num_features_y)
def _make_train_val_from_train(Xp_tr_, Xt_tr_, y_tr_, frac=0.10):
n = len(Xp_tr_); v = max(1, int(round(frac * n)))
return (Xp_tr_[:-v], (Xt_tr_[:-v] if Xt_tr_ is not None else None), y_tr_[:-v],
Xp_tr_[-v:], (Xt_tr_[-v:] if Xt_tr_ is not None else None), y_tr_[-v:])
def _score_block(y_pred_block, y_true_block, ctx_block, scaler_block, is_scaled, metric):
if flatten:
yp3 = unflatten_3d_tensor_by_feature_first(y_pred_block, time_steps_y, num_features_y)
yt3 = unflatten_3d_tensor_by_feature_first(y_true_block, time_steps_y, num_features_y)
else:
yp3 = y_pred_block; yt3 = y_true_block
if metric == "nll":
if target_mode != "log":
raise ValueError("metric_for_early_stop='nll' requires target_mode='log'")
z = yp3; v = np.exp(yt3)
with np.errstate(over='ignore'):
nll = 0.5*(v*np.exp(-z)+z)
return float(np.mean(nll))
if metric == "mse_scaled":
return mean_squared_error(yt3.reshape(-1), yp3.reshape(-1))
if metric == "mse_model":
if is_scaled:
yp_m = inverse_scale_3d(yp3, scaler_block)
yt_m = inverse_scale_3d(yt3, scaler_block)
else:
yp_m = yp3; yt_m = yt3
yp_inv = _inverse_transform_target(yp_m, ctx_block)
yt_inv = _inverse_transform_target(yt_m, ctx_block)
return mean_squared_error(yt_inv.reshape(-1), yp_inv.reshape(-1))
raise ValueError(f"Unknown metric_for_early_stop: {metric}")
if not use_nested_cv and not single_holdout:
print("\nStarting training without CV:")
split_idx = int(0.8 * len(X_price))
Xp_tr, Xp_te = X_price[:split_idx], X_price[split_idx:]
Xt_tr, Xt_te = (X_time[:split_idx], X_time[split_idx:]) if X_time is not None else (None, None)
y_tr_raw, y_te_raw = y[:split_idx], y[split_idx:]
Xp_core, Xt_core, y_core_raw, Xp_val, Xt_val, y_val_raw = _make_train_val_from_train(Xp_tr, Xt_tr, y_tr_raw, frac=val_frac)
base_core = base_val = base_te = None
if target_mode == "log_var_ratio":
base_core = _build_baseline_var_from_Xp(Xp_core, y_core_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
base_val = _build_baseline_var_from_Xp(Xp_val, y_val_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
base_te = _build_baseline_var_from_Xp(Xp_te, y_te_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
y_core, ctx_core = _transform_target(y_core_raw, base_core)
y_val, ctx_val = _transform_target(y_val_raw, base_val)
y_te, ctx_te = _transform_target(y_te_raw, base_te)
(Xp_core_s, Xt_core_s, y_core_s,
Xp_val_s, Xt_val_s, y_val_s,
Xp_te_s, Xt_te_s, y_te_s,
X_price_train_scaler, X_time_train_scaler, scaler_y) = _fit_apply_scalers(
Xp_core, Xt_core, y_core, Xp_val, Xt_val, y_val, Xp_te, Xt_te, y_te,
do_norm_X=normalize_X, do_norm_Time=normalize_Time, do_norm_y=normalize_y,
y_scaler_kind=y_scale_type
)
if verbose:
_peek_mag("Xp_core_s", Xp_core_s); _peek_mag("Xp_val_s", Xp_val_s)
if Xt_core_s is not None: _peek_mag("Xt_core_s", Xt_core_s)
if Xt_val_s is not None: _peek_mag("Xt_val_s", Xt_val_s)
check_data_issues(Xp_core_s, label="X_price_train_core")
if Xt_core_s is not None: check_data_issues(Xt_core_s, label="X_time_train_core")
check_data_issues(y_core_s, label=f"y_train_core ({target_mode} scaled)")
check_data_issues(Xp_val_s, label="X_price_val")
if Xt_val_s is not None: check_data_issues(Xt_val_s, label="X_time_val")
check_data_issues(y_val_s, label=f"y_val ({target_mode} scaled)")
check_data_issues(Xp_te_s, label="X_price_test")
if Xt_te_s is not None: check_data_issues(Xt_te_s, label="X_time_test")
check_data_issues(y_te_s, label=f"y_test ({target_mode} scaled)")
X_train, y_train = _prep_for_model(Xp_core_s, Xt_core_s, y_core_s, merge_price_time, flatten)
X_val__, y_val__ = _prep_for_model(Xp_val_s, Xt_val_s, y_val_s, merge_price_time, flatten)
X_test, y_test_scaled = _prep_for_model(Xp_te_s, Xt_te_s, y_te_s, merge_price_time, flatten)
model.fit(X_train, y_train, X_val__, y_val__)
if hasattr(model, 'get_params'):
used_params = model.get_params()
print("\nParameters used in the single-fit model:")
for k, v in used_params.items():
print(f"{k}: {v:.8f}" if isinstance(v, float) else f"{k}: {v}")
y_pred = model.predict(X_test)
if flatten:
y_pred_3d = unflatten_3d_tensor_by_feature_first(y_pred, time_steps_y, num_features_y)
y_test_scaled_3d = unflatten_3d_tensor_by_feature_first(y_test_scaled, time_steps_y, num_features_y)
else:
y_pred_3d = y_pred; y_test_scaled_3d = y_test_scaled
if normalize_y:
y_pred_model_space = inverse_scale_3d(y_pred_3d, scaler_y)
y_true_model_space = inverse_scale_3d(y_test_scaled_3d, scaler_y)
else:
y_pred_model_space = y_pred_3d
y_true_model_space = y_test_scaled_3d
y_pred_inv = _inverse_transform_target(y_pred_model_space, ctx_te)
y_true_inv = _inverse_transform_target(y_true_model_space, ctx_te)
results.clear()
for i in range(no_tasks):
results[f"Task {i+1}"] = evaluate_predictions(y_true_inv[:, :, i], y_pred_inv[:, :, i])
if verbose:
print("\nResults for regular train/test evaluation (without CV):")
for task_name, task_result in results.items():
print(f"\n--- {task_name} ---")
for metric_name, metric_value in task_result.items():
print(f"{metric_name:<35}: {metric_value:.8f}" if isinstance(metric_value, float) else f"{metric_name:<35}: {metric_value}")
best_model_final = model
best_params_final = model.get_params() if hasattr(model, 'get_params') else None
y_task0_true_pred = _build_task0_true_pred(y_true_inv, y_pred_inv)
if save_model_path and best_model_final is not None:
os.makedirs(os.path.dirname(save_model_path), exist_ok=True)
save_dict = {
"model": best_model_final,
"results": results,
"nested_results": nested_results,
"best_params": best_params_final,
"X_price_scaler": X_price_train_scaler if normalize_X else None,
"X_time_scaler": X_time_train_scaler if normalize_Time else None,
"y_scaler": scaler_y if normalize_y else None,
"target_mode": target_mode,
"baseline_feature_idx": baseline_feature_idx,
"baseline_window": baseline_window,
}
with open(save_model_path, "wb") as f:
pickle.dump(save_dict, f)
print(f"\nBest model (no-CV) saved to: {save_model_path}")
print(f"\nSaved y_true min={np.nanmin(y_task0_true_pred[:,:,0]):.6g}, max={np.nanmax(y_task0_true_pred[:,:,0]):.6g}")
print(f"Saved y_pred min={np.nanmin(y_task0_true_pred[:,:,1]):.6g}, max={np.nanmax(y_task0_true_pred[:,:,1]):.6g}")
return results, nested_results, best_model_final, best_params_final, y_task0_true_pred
if single_holdout and not use_nested_cv:
print("\nSingle holdout: one train/test split, param search on a single train/val (early-stop) split.")
split_idx = int(0.8 * len(X_price))
Xp_tr, Xp_te = X_price[:split_idx], X_price[split_idx:]
Xt_tr, Xt_te = (X_time[:split_idx], X_time[split_idx:]) if X_time is not None else (None, None)
y_tr_raw, y_te_raw = y[:split_idx], y[split_idx:]
Xp_core, Xt_core, y_core_raw, Xp_val, Xt_val, y_val_raw = _make_train_val_from_train(Xp_tr, Xt_tr, y_tr_raw, frac=val_frac)
base_core = base_val = base_te = None
if target_mode == "log_var_ratio":
base_core = _build_baseline_var_from_Xp(Xp_core, y_core_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
base_val = _build_baseline_var_from_Xp(Xp_val, y_val_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
base_te = _build_baseline_var_from_Xp(Xp_te, y_te_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
y_core, ctx_core = _transform_target(y_core_raw, base_core)
y_val, ctx_val = _transform_target(y_val_raw, base_val)
y_te, ctx_te = _transform_target(y_te_raw, base_te)
(Xp_core_s, Xt_core_s, y_core_s,
Xp_val_s, Xt_val_s, y_val_s,
Xp_te_s, Xt_te_s, y_te_s,
X_price_train_scaler, X_time_train_scaler, scaler_y) = _fit_apply_scalers(
Xp_core, Xt_core, y_core, Xp_val, Xt_val, y_val, Xp_te, Xt_te, y_te,
do_norm_X=normalize_X, do_norm_Time=normalize_Time, do_norm_y=normalize_y,
y_scaler_kind=y_scale_type
)
X_train, y_train = _prep_for_model(Xp_core_s, Xt_core_s, y_core_s, merge_price_time, flatten)
X_val__, y_val__ = _prep_for_model(Xp_val_s, Xt_val_s, y_val_s, merge_price_time, flatten)
best_val_loss = float("inf")
best_grid_params = None
if param_grid is None or len(param_grid) == 0:
_reset_seeds(random_state, deterministic=deterministic_torch)
tmp_model = safe_clone(model)
tmp_model.fit(X_train, y_train, X_val__, y_val__)
best_grid_params = getattr(tmp_model, "get_params", lambda: None)()
else:
for param_set in ParameterGrid(param_grid):
_reset_seeds(random_state, deterministic=deterministic_torch)
model_inner = safe_clone(model)
if hasattr(model_inner, "set_params") and param_set:
model_inner.set_params(**param_set)
try:
model_inner.fit(X_train, y_train, X_val__, y_val__)
yv_pred = model_inner.predict(X_val__)
val_metric = _score_block(yv_pred, y_val__, ctx_val, scaler_y, normalize_y, metric_for_early_stop)
if val_metric < best_val_loss:
best_val_loss = val_metric
best_grid_params = param_set.copy()
except Exception as e:
if verbose:
print(f"Skipped param set {param_set} due to error: {e}")
continue
if best_grid_params and len(best_grid_params) > 0:
print("\nBest parameters found in single holdout (grid):")
for k, v in best_grid_params.items():
print(f" {k}: {v}")
else:
print("\nSingle holdout: no grid provided; proceeding to refit with current model params.")
print("\nRefitting with selected hyperparameters on (train_core + train_val), new early-stop split from that union…")
Xp_tr_full = np.concatenate([Xp_core, Xp_val], axis=0)
Xt_tr_full = (np.concatenate([Xt_core, Xt_val], axis=0) if Xt_core is not None else None)
y_tr_full = np.concatenate([y_core, y_val], axis=0)
Xp_core_r, Xt_core_r, y_core_r, Xp_esv_r, Xt_esv_r, y_esv_r = _make_train_val_from_train(Xp_tr_full, Xt_tr_full, y_tr_full, frac=esv_frac)
(Xp_core_sr, Xt_core_sr, y_core_sr,
Xp_esv_sr, Xt_esv_sr, y_esv_sr,
Xp_te_sr, Xt_te_sr, y_te_sr,
X_price_train_scaler, X_time_train_scaler, scaler_y) = _fit_apply_scalers(
Xp_core_r, Xt_core_r, y_core_r,
Xp_esv_r, Xt_esv_r, y_esv_r,
Xp_te, Xt_te, y_te,
do_norm_X=normalize_X, do_norm_Time=normalize_Time, do_norm_y=normalize_y,
y_scaler_kind=y_scale_type
)
X_tr_refit, y_tr_refit = _prep_for_model(Xp_core_sr, Xt_core_sr, y_core_sr, merge_price_time, flatten)
X_esv_refit, y_esv_refit = _prep_for_model(Xp_esv_sr, Xt_esv_sr, y_esv_sr, merge_price_time, flatten)
X_te_refit, y_te_scaled = _prep_for_model(Xp_te_sr, Xt_te_sr, y_te_sr, merge_price_time, flatten)
best_model_refit = safe_clone(model)
if best_grid_params and hasattr(best_model_refit, "set_params"):
best_model_refit.set_params(**best_grid_params)
_reset_seeds(random_state, deterministic=deterministic_torch)
best_model_refit.fit(X_tr_refit, y_tr_refit, X_esv_refit, y_esv_refit)
y_pred_refit = best_model_refit.predict(X_te_refit)
if flatten:
y_pred_refit_3d = unflatten_3d_tensor_by_feature_first(y_pred_refit, time_steps_y, num_features_y)
y_test_scaled_3d = unflatten_3d_tensor_by_feature_first(y_te_scaled, time_steps_y, num_features_y)
else:
y_pred_refit_3d = y_pred_refit; y_test_scaled_3d = y_te_scaled
if normalize_y:
y_pred_model = inverse_scale_3d(y_pred_refit_3d, scaler_y)
y_true_model = inverse_scale_3d(y_test_scaled_3d, scaler_y)
else:
y_pred_model = y_pred_refit_3d; y_true_model = y_test_scaled_3d
y_pred_inv = _inverse_transform_target(y_pred_model, ctx_te)
y_true_inv = _inverse_transform_target(y_true_model, ctx_te)
results.clear()
for i in range(no_tasks):
results[f"Task {i+1}"] = evaluate_predictions(y_true_inv[:, :, i], y_pred_inv[:, :, i])
if verbose:
print("\nResults for the refit model (single holdout):")
for task_name, task_result in results.items():
print(f"\n--- {task_name} ---")
for metric_name, metric_value in task_result.items():
print(f"{metric_name:<35}: {metric_value:.8f}" if isinstance(metric_value, float) else f"{metric_name:<35}: {metric_value}")
best_model_final = best_model_refit
best_params_final = best_model_final.get_params() if hasattr(best_model_final, "get_params") else (best_grid_params or {})
y_task0_true_pred = _build_task0_true_pred(y_true_inv, y_pred_inv)
print(f"\nSaved y_true min={np.nanmin(y_task0_true_pred[:,:,0]):.6g}, max={np.nanmax(y_task0_true_pred[:,:,0]):.6g}")
print(f"Saved y_pred min={np.nanmin(y_task0_true_pred[:,:,1]):.6g}, max={np.nanmax(y_task0_true_pred[:,:,1]):.6g}")
if save_model_path and best_model_final is not None:
os.makedirs(os.path.dirname(save_model_path), exist_ok=True)
save_dict = {
"model": best_model_final,
"results": results,
"nested_results": nested_results,
"best_params": best_params_final,
"X_price_scaler": X_price_train_scaler if normalize_X else None,
"X_time_scaler": X_time_train_scaler if normalize_Time else None,
"y_scaler": scaler_y if normalize_y else None,
"target_mode": target_mode,
"baseline_feature_idx": baseline_feature_idx,
"baseline_window": baseline_window,
}
with open(save_model_path, "wb") as f:
pickle.dump(save_dict, f)
print(f"\nBest single-holdout (refit) model saved to: {save_model_path}")
return results, nested_results, best_model_final, best_params_final, y_task0_true_pred
if verbose:
print("\nNested cross-validation with grid search:")
outer_cv = _cv_splitter(cv_strategy, outer_folds)
nested_scores = {i: [] for i in range(no_tasks)}
best_score = float("inf")
best_params_final = None; best_model_final = None
for fold_idx, (train_idx, test_idx) in enumerate(outer_cv.split(X_price)):
if verbose:
print(f"Outer fold {fold_idx + 1}:")
Xp_tr_full, Xp_te = X_price[train_idx], X_price[test_idx]
if X_time is not None:
Xt_tr_full, Xt_te = X_time[train_idx], X_time[test_idx]
else:
Xt_tr_full, Xt_te = None, None
y_tr_full_raw, y_te_raw = y[train_idx], y[test_idx]
inner_cv = _cv_splitter(cv_strategy, inner_folds)
best_val_loss = float("inf"); best_params = None
search_grid = ParameterGrid(param_grid) if (param_grid is not None and len(param_grid) > 0) else [dict()]
for param_set in search_grid:
fold_val_losses = []
for inner_fold_idx, (inner_tr_idx, inner_val_idx) in enumerate(inner_cv.split(Xp_tr_full)):
Xp_inner_tr, Xp_inner_val = Xp_tr_full[inner_tr_idx], Xp_tr_full[inner_val_idx]
if Xt_tr_full is not None:
Xt_inner_tr, Xt_inner_val = Xt_tr_full[inner_tr_idx], Xt_tr_full[inner_val_idx]
else:
Xt_inner_tr, Xt_inner_val = None, None
y_inner_tr_raw, y_inner_val_raw = y_tr_full_raw[inner_tr_idx], y_tr_full_raw[inner_val_idx]
Xp_core, Xt_core, y_core_raw, Xp_esv, Xt_esv, y_esv_raw = _make_train_val_from_train(Xp_inner_tr, Xt_inner_tr, y_inner_tr_raw, frac=esv_frac)
base_core = base_esv = base_val = None
if target_mode == "log_var_ratio":
base_core = _build_baseline_var_from_Xp(Xp_core, y_core_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
base_esv = _build_baseline_var_from_Xp(Xp_esv, y_esv_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
base_val = _build_baseline_var_from_Xp(Xp_inner_val, y_inner_val_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
y_core, ctx_core = _transform_target(y_core_raw, base_core)
y_esv, ctx_esv = _transform_target(y_esv_raw, base_esv)
y_val, ctx_val = _transform_target(y_inner_val_raw, base_val)
(Xp_core_s, Xt_core_s, y_core_s,
Xp_esv_s, Xt_esv_s, y_esv_s,
Xp_val_s, Xt_val_s, y_val_s,
_, _, y_scaler_inner) = _fit_apply_scalers(
Xp_core, Xt_core, y_core, Xp_esv, Xt_esv, y_esv, Xp_inner_val, Xt_inner_val, y_val,
do_norm_X=normalize_X, do_norm_Time=normalize_Time, do_norm_y=normalize_y,
y_scaler_kind=y_scale_type
)
Xi_tr, yi_tr = _prep_for_model(Xp_core_s, Xt_core_s, y_core_s, merge_price_time, flatten)
Xi_esv, yi_esv = _prep_for_model(Xp_esv_s, Xt_esv_s, y_esv_s, merge_price_time, flatten)
Xi_val, yi_val = _prep_for_model(Xp_val_s, Xt_val_s, y_val_s, merge_price_time, flatten)
_reset_seeds(random_state, deterministic=deterministic_torch)
model_inner = safe_clone(model)
if hasattr(model_inner, "set_params") and param_set:
model_inner.set_params(**param_set)
try:
if verbose:
print(f"\nSize of the X data for training in the inner fold {inner_fold_idx + 1}: {Xi_tr.shape}")
print(f"Size of the Y data for training in the inner fold {inner_fold_idx + 1}: {yi_tr.shape}")
model_inner.fit(Xi_tr, yi_tr, Xi_esv, yi_esv)
y_val_pred = model_inner.predict(Xi_val)
val_metric = _score_block(y_val_pred, yi_val, ctx_val, y_scaler_inner, normalize_y, metric_for_early_stop)
fold_val_losses.append(val_metric)
except Exception as e:
if verbose:
print(f"Skipped param set {param_set} in inner fold {inner_fold_idx + 1} due to error: {e}")
fold_val_losses = [np.inf]; break
avg_val_loss = np.mean(fold_val_losses)
if avg_val_loss < best_val_loss:
best_val_loss = avg_val_loss; best_params = param_set
if avg_val_loss < best_score:
best_score = avg_val_loss; best_params_final = param_set
if best_params is None:
continue
Xp_core, Xt_core, y_core_raw, Xp_esv, Xt_esv, y_esv_raw = _make_train_val_from_train(Xp_tr_full, Xt_tr_full, y_tr_full_raw, frac=esv_frac)
base_core = base_esv = base_te = None
if target_mode == "log_var_ratio":
base_core = _build_baseline_var_from_Xp(Xp_core, y_core_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
base_esv = _build_baseline_var_from_Xp(Xp_esv, y_esv_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
base_te = _build_baseline_var_from_Xp(Xp_te, y_te_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
y_core, ctx_core = _transform_target(y_core_raw, base_core)
y_esv, ctx_esv = _transform_target(y_esv_raw, base_esv)
y_te, ctx_te = _transform_target(y_te_raw, base_te)
(Xp_core_s, Xt_core_s, y_core_s,
Xp_esv_s, Xt_esv_s, y_esv_s,
Xp_te_s, Xt_te_s, y_te_s,
X_price_train_scaler, X_time_train_scaler, scaler_y) = _fit_apply_scalers(
Xp_core, Xt_core, y_core, Xp_esv, Xt_esv, y_esv, Xp_te, Xt_te, y_te,
do_norm_X=normalize_X, do_norm_Time=normalize_Time, do_norm_y=normalize_y,
y_scaler_kind=y_scale_type
)
X_train, y_train = _prep_for_model(Xp_core_s, Xt_core_s, y_core_s, merge_price_time, flatten)
X_val__, y_val__ = _prep_for_model(Xp_esv_s, Xt_esv_s, y_esv_s, merge_price_time, flatten)
X_test, y_test_scaled = _prep_for_model(Xp_te_s, Xt_te_s, y_te_s, merge_price_time, flatten)
if verbose:
print(f"\nSize of the X data for training in the outer fold: {X_train.shape}")
print(f"Size of the Y data for training in the outer fold: {y_train.shape}")
_reset_seeds(random_state, deterministic=deterministic_torch)
model_fold = safe_clone(model)
if hasattr(model_fold, "set_params") and best_params:
model_fold.set_params(**best_params)
model_fold.fit(X_train, y_train, X_val__, y_val__)
y_pred = model_fold.predict(X_test)
if flatten:
y_pred_3d = unflatten_3d_tensor_by_feature_first(y_pred, time_steps_y, num_features_y)
y_test_scaled_3d = unflatten_3d_tensor_by_feature_first(y_test_scaled, time_steps_y, num_features_y)
else:
y_pred_3d = y_pred; y_test_scaled_3d = y_test_scaled
if normalize_y:
y_pred_model = inverse_scale_3d(y_pred_3d, scaler_y)
y_true_model = inverse_scale_3d(y_test_scaled_3d, scaler_y)
else:
y_pred_model = y_pred_3d; y_true_model = y_test_scaled_3d
y_pred_inv = _inverse_transform_target(y_pred_model, ctx_te)
y_true_inv = _inverse_transform_target(y_true_model, ctx_te)
for i in range(no_tasks):
task_y_true = y_true_inv[:, :, i]
task_y_pred = y_pred_inv[:, :, i]
metrics = evaluate_predictions(task_y_true, task_y_pred)
nested_scores[i].append(metrics)
if verbose:
print(f"\nOuter Fold {fold_idx + 1} — Task {i + 1} Metrics:")
for metric_name, metric_value in metrics.items():
print(f" {metric_name:<35}: {metric_value:.4f}" if isinstance(metric_value, float) else f" {metric_name:<35}: {metric_value}")
if any(len(v) > 0 for v in nested_scores.values()):
for task_idx, folds in nested_scores.items():
if len(folds) == 0:
continue
for metric in folds[0]:
values = np.array([fold[metric] for fold in folds], dtype=float)
mean_val = values.mean()
ci95 = t.interval(0.95, df=len(values)-1, loc=mean_val, scale=values.std(ddof=1)/np.sqrt(len(values)))
nested_results[f"Task {task_idx+1} — Nested {metric}"] = mean_val
nested_results[f"Task {task_idx+1} — Nested {metric} 95% CI"] = ci95
if verbose:
print("\nNested CV Average Metrics with 95% CI (per task):")
for k in sorted(nested_results):
v = nested_results[k]
if isinstance(v, tuple):
print(f"{k:<55}: ({v[0]:.4f}, {v[1]:.4f})")
elif isinstance(v, float):
print(f"{k:<55}: {v:.4f}")
else:
print(f"{k:<55}: {v}")
if verbose and best_params_final is not None:
print("\nBest parameters found via nested CV:")
for k, v in best_params_final.items():
print(f" {k}: {v}")
print("\nRefitting the final model with the best parameters found in the nested CV:")
if best_params_final is not None:
if final_refit_holdout_frac < 0 or final_refit_holdout_frac >= 1:
raise ValueError("final_refit_holdout_frac must be in [0, 1).")
split_idx = int(round((1 - final_refit_holdout_frac) * len(X_price)))
if split_idx == len(X_price):
Xp_tr, Xp_te = X_price, X_price[:0]
Xt_tr, Xt_te = (X_time, X_time[:0]) if X_time is not None else (None, None)
y_tr_raw, y_te_raw = y, y[:0]
else:
Xp_tr, Xp_te = X_price[:split_idx], X_price[split_idx:]
Xt_tr, Xt_te = (X_time[:split_idx], X_time[split_idx:]) if X_time is not None else (None, None)
y_tr_raw, y_te_raw = y[:split_idx], y[split_idx:]
Xp_core, Xt_core, y_core_raw, Xp_esv, Xt_esv, y_esv_raw = _make_train_val_from_train(Xp_tr, Xt_tr, y_tr_raw, frac=esv_frac)
base_core = base_esv = base_te = None
if target_mode == "log_var_ratio":
base_core = _build_baseline_var_from_Xp(Xp_core, y_core_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
base_esv = _build_baseline_var_from_Xp(Xp_esv, y_esv_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps)
base_te = _build_baseline_var_from_Xp(Xp_te, y_te_raw, feat_idx=baseline_feature_idx, window=baseline_window, eps=baseline_eps) if len(y_te_raw) else None
y_core, ctx_core = _transform_target(y_core_raw, base_core)
y_esv, ctx_esv = _transform_target(y_esv_raw, base_esv)
y_te, ctx_te = _transform_target(y_te_raw, base_te) if len(y_te_raw) else (y_esv, ctx_esv)
(Xp_core_s, Xt_core_s, y_core_s,
Xp_esv_s, Xt_esv_s, y_esv_s,
Xp_te_s, Xt_te_s, y_te_s,
X_price_train_scaler, X_time_train_scaler, scaler_y) = _fit_apply_scalers(
Xp_core, Xt_core, y_core, Xp_esv, Xt_esv, y_esv, Xp_te, Xt_te, y_te,
do_norm_X=normalize_X, do_norm_Time=normalize_Time, do_norm_y=normalize_y,
y_scaler_kind=y_scale_type
)
X_train_final, y_train_final = _prep_for_model(Xp_core_s, Xt_core_s, y_core_s, merge_price_time, flatten)
X_val_final, y_val_final = _prep_for_model(Xp_esv_s, Xt_esv_s, y_esv_s, merge_price_time, flatten)
X_test_final, y_test_final_s = _prep_for_model(Xp_te_s, Xt_te_s, y_te_s, merge_price_time, flatten) if len(y_te_raw) else (None, None)
best_model_final = safe_clone(model)
if hasattr(best_model_final, "set_params"):
best_model_final.set_params(**best_params_final)
print(f"\nSize of the X data for training: {X_train_final.shape}")
print(f"Size of the Y data for training: {y_train_final.shape}")
_reset_seeds(random_state, deterministic=deterministic_torch)
best_model_final.fit(X_train_final, y_train_final, X_val_final, y_val_final)
if X_test_final is not None:
y_pred_final = best_model_final.predict(X_test_final)
if flatten:
y_pred_final_3d = unflatten_3d_tensor_by_feature_first(y_pred_final, time_steps_y, num_features_y)
y_test_final_s_3d = unflatten_3d_tensor_by_feature_first(y_test_final_s, time_steps_y, num_features_y)
else:
y_pred_final_3d = y_pred_final; y_test_final_s_3d = y_test_final_s
if normalize_y:
y_pred_final_model = inverse_scale_3d(y_pred_final_3d, scaler_y)
y_true_final_model = inverse_scale_3d(y_test_final_s_3d, scaler_y)
else:
y_pred_final_model = y_pred_final_3d; y_true_final_model = y_test_final_s_3d
y_pred_final_inv = _inverse_transform_target(y_pred_final_model, ctx_te)
y_true_final_inv = _inverse_transform_target(y_true_final_model, ctx_te)
if verbose:
print("\nResults after refitting the final model:")
for i in range(no_tasks):
final_metrics = evaluate_predictions(y_true_final_inv[:, :, i], y_pred_final_inv[:, :, i])
print(f"\nTask {i + 1} Final Metrics:")
for metric_name, metric_value in final_metrics.items():
print(f" {metric_name:<40}: {metric_value:.4f}" if isinstance(metric_value, float) else f" {metric_name:<40}: {metric_value}")
y_task0_true_pred = _build_task0_true_pred(y_true_final_inv, y_pred_final_inv)
print(f"\nSaved y_true min={np.nanmin(y_task0_true_pred[:,:,0]):.6g}, max={np.nanmax(y_task0_true_pred[:,:,0]):.6g}")
print(f"Saved y_pred min={np.nanmin(y_task0_true_pred[:,:,1]):.6g}, max={np.nanmax(y_task0_true_pred[:,:,1]):.6g}")
else:
y_task0_true_pred = None
print("\nFinal refit performed on all data; no holdout set to report metrics.")
if save_model_path and best_model_final is not None:
os.makedirs(os.path.dirname(save_model_path), exist_ok=True)
save_dict = {
"model": best_model_final,
"results": results,
"nested_results": nested_results,
"best_params": best_model_final.get_params() if hasattr(best_model_final, "get_params") else best_params_final,
"X_price_scaler": X_price_train_scaler if normalize_X else None,
"X_time_scaler": X_time_train_scaler if normalize_Time else None,
"y_scaler": scaler_y if normalize_y else None,
"target_mode": target_mode,
"baseline_feature_idx": baseline_feature_idx,
"baseline_window": baseline_window,
}
with open(save_model_path, "wb") as f:
pickle.dump(save_dict, f)
print(f"\nFinal nested-CV model saved to: {save_model_path}")
return results, nested_results, best_model_final, best_params_final, y_task0_true_pred
Loading data¶
The objects containing the data can be loaded so most of the previous sections are no longer necessary.
Sumarining helper¶
In [3]:
import numpy as np
import pandas as pd
def summarize_features(arr, col_names=None, name="array"):
if arr is None:
print(f"[{name}] is None")
return None
arr = np.asarray(arr)
if arr.ndim == 3:
B, T, F = arr.shape
flat = arr.reshape(-1, F)
elif arr.ndim == 2:
S, F = arr.shape
flat = arr
else:
raise ValueError(f"[{name}] expected 2D or 3D array; got shape {arr.shape}")
n_nan = np.isnan(flat).sum()
n_posinf = np.isposinf(flat).sum()
n_neginf = np.isneginf(flat).sum()
n_inf = n_posinf + n_neginf
print(f"[{name}] shape={arr.shape} | NaN: {n_nan} | +Inf: {n_posinf} | -Inf: {n_neginf} | Any Inf: {n_inf}")
safe = np.where(np.isfinite(flat), flat, np.nan)
mins = np.nanmin(safe, axis=0)
maxs = np.nanmax(safe, axis=0)
means = np.nanmean(safe, axis=0)
stds = np.nanstd(safe, axis=0)
p1 = np.nanpercentile(safe, 1, axis=0)
p50 = np.nanpercentile(safe, 50, axis=0)
p99 = np.nanpercentile(safe, 99, axis=0)
if col_names is None or len(col_names) != safe.shape[1]:
col_names = [f"f{i}" for i in range(safe.shape[1])]
df = pd.DataFrame({
"feature": col_names,
"min": mins,
"p01": p1,
"median": p50,
"p99": p99,
"max": maxs,
"mean": means,
"std": stds,
})
df["abs_max"] = np.maximum(np.abs(df["min"]), np.abs(df["max"]))
df = df.sort_values("abs_max", ascending=False).drop(columns=["abs_max"]).reset_index(drop=True)
return df
Loading regular data¶
In [4]:
import os
import pickle
objects_relative_path = "Saved_objects"
load_data_object_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict.pkl")
with open(load_data_object_file_path, "rb") as f:
structured_data_dict = pickle.load(f)
print("Data dictionary loaded successfully.")
aapl_X_price = structured_data_dict["AAPL"].get("X_other")
aapl_X_time = structured_data_dict["AAPL"].get("X_time")
aapl_y = structured_data_dict["AAPL"].get("y")
eur_X_price = structured_data_dict["EURUSD"].get("X_other")
eur_X_time = structured_data_dict["EURUSD"].get("X_time")
eur_y = structured_data_dict["EURUSD"].get("y")
eur_y_columns = structured_data_dict["EURUSD"].get("y_columns")
print(eur_y_columns)
print(eur_y_columns)
Data dictionary loaded successfully. ['rv_t+1', 'lg_return_t+1', 'y_best_alpha_1_t+1', 'y_best_beta_1_t+1', 'y_best_omega_t+1', 'y_best_mu_t+1', 'y_best_nu_tgarch_t+1', 'rv_t+2', 'lg_return_t+2', 'y_best_alpha_1_t+2', 'y_best_beta_1_t+2', 'y_best_omega_t+2', 'y_best_mu_t+2', 'y_best_nu_tgarch_t+2', 'rv_t+3', 'lg_return_t+3', 'y_best_alpha_1_t+3', 'y_best_beta_1_t+3', 'y_best_omega_t+3', 'y_best_mu_t+3', 'y_best_nu_tgarch_t+3', 'rv_t+4', 'lg_return_t+4', 'y_best_alpha_1_t+4', 'y_best_beta_1_t+4', 'y_best_omega_t+4', 'y_best_mu_t+4', 'y_best_nu_tgarch_t+4', 'rv_t+5', 'lg_return_t+5', 'y_best_alpha_1_t+5', 'y_best_beta_1_t+5', 'y_best_omega_t+5', 'y_best_mu_t+5', 'y_best_nu_tgarch_t+5', 'rv_t+6', 'lg_return_t+6', 'y_best_alpha_1_t+6', 'y_best_beta_1_t+6', 'y_best_omega_t+6', 'y_best_mu_t+6', 'y_best_nu_tgarch_t+6', 'rv_t+7', 'lg_return_t+7', 'y_best_alpha_1_t+7', 'y_best_beta_1_t+7', 'y_best_omega_t+7', 'y_best_mu_t+7', 'y_best_nu_tgarch_t+7', 'rv_t+8', 'lg_return_t+8', 'y_best_alpha_1_t+8', 'y_best_beta_1_t+8', 'y_best_omega_t+8', 'y_best_mu_t+8', 'y_best_nu_tgarch_t+8', 'rv_t+9', 'lg_return_t+9', 'y_best_alpha_1_t+9', 'y_best_beta_1_t+9', 'y_best_omega_t+9', 'y_best_mu_t+9', 'y_best_nu_tgarch_t+9', 'rv_t+10', 'lg_return_t+10', 'y_best_alpha_1_t+10', 'y_best_beta_1_t+10', 'y_best_omega_t+10', 'y_best_mu_t+10', 'y_best_nu_tgarch_t+10', 'rv_t+11', 'lg_return_t+11', 'y_best_alpha_1_t+11', 'y_best_beta_1_t+11', 'y_best_omega_t+11', 'y_best_mu_t+11', 'y_best_nu_tgarch_t+11', 'rv_t+12', 'lg_return_t+12', 'y_best_alpha_1_t+12', 'y_best_beta_1_t+12', 'y_best_omega_t+12', 'y_best_mu_t+12', 'y_best_nu_tgarch_t+12', 'rv_t+13', 'lg_return_t+13', 'y_best_alpha_1_t+13', 'y_best_beta_1_t+13', 'y_best_omega_t+13', 'y_best_mu_t+13', 'y_best_nu_tgarch_t+13', 'rv_t+14', 'lg_return_t+14', 'y_best_alpha_1_t+14', 'y_best_beta_1_t+14', 'y_best_omega_t+14', 'y_best_mu_t+14', 'y_best_nu_tgarch_t+14', 'rv_t+15', 'lg_return_t+15', 'y_best_alpha_1_t+15', 'y_best_beta_1_t+15', 'y_best_omega_t+15', 'y_best_mu_t+15', 'y_best_nu_tgarch_t+15', 'rv_t+16', 'lg_return_t+16', 'y_best_alpha_1_t+16', 'y_best_beta_1_t+16', 'y_best_omega_t+16', 'y_best_mu_t+16', 'y_best_nu_tgarch_t+16', 'rv_t+17', 'lg_return_t+17', 'y_best_alpha_1_t+17', 'y_best_beta_1_t+17', 'y_best_omega_t+17', 'y_best_mu_t+17', 'y_best_nu_tgarch_t+17', 'rv_t+18', 'lg_return_t+18', 'y_best_alpha_1_t+18', 'y_best_beta_1_t+18', 'y_best_omega_t+18', 'y_best_mu_t+18', 'y_best_nu_tgarch_t+18', 'rv_t+19', 'lg_return_t+19', 'y_best_alpha_1_t+19', 'y_best_beta_1_t+19', 'y_best_omega_t+19', 'y_best_mu_t+19', 'y_best_nu_tgarch_t+19', 'rv_t+20', 'lg_return_t+20', 'y_best_alpha_1_t+20', 'y_best_beta_1_t+20', 'y_best_omega_t+20', 'y_best_mu_t+20', 'y_best_nu_tgarch_t+20', 'rv_t+21', 'lg_return_t+21', 'y_best_alpha_1_t+21', 'y_best_beta_1_t+21', 'y_best_omega_t+21', 'y_best_mu_t+21', 'y_best_nu_tgarch_t+21', 'rv_t+22', 'lg_return_t+22', 'y_best_alpha_1_t+22', 'y_best_beta_1_t+22', 'y_best_omega_t+22', 'y_best_mu_t+22', 'y_best_nu_tgarch_t+22', 'rv_t+23', 'lg_return_t+23', 'y_best_alpha_1_t+23', 'y_best_beta_1_t+23', 'y_best_omega_t+23', 'y_best_mu_t+23', 'y_best_nu_tgarch_t+23', 'rv_t+24', 'lg_return_t+24', 'y_best_alpha_1_t+24', 'y_best_beta_1_t+24', 'y_best_omega_t+24', 'y_best_mu_t+24', 'y_best_nu_tgarch_t+24', 'rv_t+25', 'lg_return_t+25', 'y_best_alpha_1_t+25', 'y_best_beta_1_t+25', 'y_best_omega_t+25', 'y_best_mu_t+25', 'y_best_nu_tgarch_t+25', 'rv_t+26', 'lg_return_t+26', 'y_best_alpha_1_t+26', 'y_best_beta_1_t+26', 'y_best_omega_t+26', 'y_best_mu_t+26', 'y_best_nu_tgarch_t+26', 'rv_t+27', 'lg_return_t+27', 'y_best_alpha_1_t+27', 'y_best_beta_1_t+27', 'y_best_omega_t+27', 'y_best_mu_t+27', 'y_best_nu_tgarch_t+27', 'rv_t+28', 'lg_return_t+28', 'y_best_alpha_1_t+28', 'y_best_beta_1_t+28', 'y_best_omega_t+28', 'y_best_mu_t+28', 'y_best_nu_tgarch_t+28', 'rv_t+29', 'lg_return_t+29', 'y_best_alpha_1_t+29', 'y_best_beta_1_t+29', 'y_best_omega_t+29', 'y_best_mu_t+29', 'y_best_nu_tgarch_t+29', 'rv_t+30', 'lg_return_t+30', 'y_best_alpha_1_t+30', 'y_best_beta_1_t+30', 'y_best_omega_t+30', 'y_best_mu_t+30', 'y_best_nu_tgarch_t+30', 'rv_t+31', 'lg_return_t+31', 'y_best_alpha_1_t+31', 'y_best_beta_1_t+31', 'y_best_omega_t+31', 'y_best_mu_t+31', 'y_best_nu_tgarch_t+31', 'rv_t+32', 'lg_return_t+32', 'y_best_alpha_1_t+32', 'y_best_beta_1_t+32', 'y_best_omega_t+32', 'y_best_mu_t+32', 'y_best_nu_tgarch_t+32', 'rv_t+33', 'lg_return_t+33', 'y_best_alpha_1_t+33', 'y_best_beta_1_t+33', 'y_best_omega_t+33', 'y_best_mu_t+33', 'y_best_nu_tgarch_t+33', 'rv_t+34', 'lg_return_t+34', 'y_best_alpha_1_t+34', 'y_best_beta_1_t+34', 'y_best_omega_t+34', 'y_best_mu_t+34', 'y_best_nu_tgarch_t+34', 'rv_t+35', 'lg_return_t+35', 'y_best_alpha_1_t+35', 'y_best_beta_1_t+35', 'y_best_omega_t+35', 'y_best_mu_t+35', 'y_best_nu_tgarch_t+35', 'rv_t+36', 'lg_return_t+36', 'y_best_alpha_1_t+36', 'y_best_beta_1_t+36', 'y_best_omega_t+36', 'y_best_mu_t+36', 'y_best_nu_tgarch_t+36', 'rv_t+37', 'lg_return_t+37', 'y_best_alpha_1_t+37', 'y_best_beta_1_t+37', 'y_best_omega_t+37', 'y_best_mu_t+37', 'y_best_nu_tgarch_t+37', 'rv_t+38', 'lg_return_t+38', 'y_best_alpha_1_t+38', 'y_best_beta_1_t+38', 'y_best_omega_t+38', 'y_best_mu_t+38', 'y_best_nu_tgarch_t+38', 'rv_t+39', 'lg_return_t+39', 'y_best_alpha_1_t+39', 'y_best_beta_1_t+39', 'y_best_omega_t+39', 'y_best_mu_t+39', 'y_best_nu_tgarch_t+39', 'rv_t+40', 'lg_return_t+40', 'y_best_alpha_1_t+40', 'y_best_beta_1_t+40', 'y_best_omega_t+40', 'y_best_mu_t+40', 'y_best_nu_tgarch_t+40', 'rv_t+41', 'lg_return_t+41', 'y_best_alpha_1_t+41', 'y_best_beta_1_t+41', 'y_best_omega_t+41', 'y_best_mu_t+41', 'y_best_nu_tgarch_t+41', 'rv_t+42', 'lg_return_t+42', 'y_best_alpha_1_t+42', 'y_best_beta_1_t+42', 'y_best_omega_t+42', 'y_best_mu_t+42', 'y_best_nu_tgarch_t+42', 'rv_t+43', 'lg_return_t+43', 'y_best_alpha_1_t+43', 'y_best_beta_1_t+43', 'y_best_omega_t+43', 'y_best_mu_t+43', 'y_best_nu_tgarch_t+43', 'rv_t+44', 'lg_return_t+44', 'y_best_alpha_1_t+44', 'y_best_beta_1_t+44', 'y_best_omega_t+44', 'y_best_mu_t+44', 'y_best_nu_tgarch_t+44', 'rv_t+45', 'lg_return_t+45', 'y_best_alpha_1_t+45', 'y_best_beta_1_t+45', 'y_best_omega_t+45', 'y_best_mu_t+45', 'y_best_nu_tgarch_t+45', 'rv_t+46', 'lg_return_t+46', 'y_best_alpha_1_t+46', 'y_best_beta_1_t+46', 'y_best_omega_t+46', 'y_best_mu_t+46', 'y_best_nu_tgarch_t+46', 'rv_t+47', 'lg_return_t+47', 'y_best_alpha_1_t+47', 'y_best_beta_1_t+47', 'y_best_omega_t+47', 'y_best_mu_t+47', 'y_best_nu_tgarch_t+47', 'rv_t+48', 'lg_return_t+48', 'y_best_alpha_1_t+48', 'y_best_beta_1_t+48', 'y_best_omega_t+48', 'y_best_mu_t+48', 'y_best_nu_tgarch_t+48', 'rv_t+49', 'lg_return_t+49', 'y_best_alpha_1_t+49', 'y_best_beta_1_t+49', 'y_best_omega_t+49', 'y_best_mu_t+49', 'y_best_nu_tgarch_t+49', 'rv_t+50', 'lg_return_t+50', 'y_best_alpha_1_t+50', 'y_best_beta_1_t+50', 'y_best_omega_t+50', 'y_best_mu_t+50', 'y_best_nu_tgarch_t+50', 'rv_t+51', 'lg_return_t+51', 'y_best_alpha_1_t+51', 'y_best_beta_1_t+51', 'y_best_omega_t+51', 'y_best_mu_t+51', 'y_best_nu_tgarch_t+51', 'rv_t+52', 'lg_return_t+52', 'y_best_alpha_1_t+52', 'y_best_beta_1_t+52', 'y_best_omega_t+52', 'y_best_mu_t+52', 'y_best_nu_tgarch_t+52', 'rv_t+53', 'lg_return_t+53', 'y_best_alpha_1_t+53', 'y_best_beta_1_t+53', 'y_best_omega_t+53', 'y_best_mu_t+53', 'y_best_nu_tgarch_t+53', 'rv_t+54', 'lg_return_t+54', 'y_best_alpha_1_t+54', 'y_best_beta_1_t+54', 'y_best_omega_t+54', 'y_best_mu_t+54', 'y_best_nu_tgarch_t+54', 'rv_t+55', 'lg_return_t+55', 'y_best_alpha_1_t+55', 'y_best_beta_1_t+55', 'y_best_omega_t+55', 'y_best_mu_t+55', 'y_best_nu_tgarch_t+55', 'rv_t+56', 'lg_return_t+56', 'y_best_alpha_1_t+56', 'y_best_beta_1_t+56', 'y_best_omega_t+56', 'y_best_mu_t+56', 'y_best_nu_tgarch_t+56', 'rv_t+57', 'lg_return_t+57', 'y_best_alpha_1_t+57', 'y_best_beta_1_t+57', 'y_best_omega_t+57', 'y_best_mu_t+57', 'y_best_nu_tgarch_t+57', 'rv_t+58', 'lg_return_t+58', 'y_best_alpha_1_t+58', 'y_best_beta_1_t+58', 'y_best_omega_t+58', 'y_best_mu_t+58', 'y_best_nu_tgarch_t+58', 'rv_t+59', 'lg_return_t+59', 'y_best_alpha_1_t+59', 'y_best_beta_1_t+59', 'y_best_omega_t+59', 'y_best_mu_t+59', 'y_best_nu_tgarch_t+59', 'rv_t+60', 'lg_return_t+60', 'y_best_alpha_1_t+60', 'y_best_beta_1_t+60', 'y_best_omega_t+60', 'y_best_mu_t+60', 'y_best_nu_tgarch_t+60'] ['rv_t+1', 'lg_return_t+1', 'y_best_alpha_1_t+1', 'y_best_beta_1_t+1', 'y_best_omega_t+1', 'y_best_mu_t+1', 'y_best_nu_tgarch_t+1', 'rv_t+2', 'lg_return_t+2', 'y_best_alpha_1_t+2', 'y_best_beta_1_t+2', 'y_best_omega_t+2', 'y_best_mu_t+2', 'y_best_nu_tgarch_t+2', 'rv_t+3', 'lg_return_t+3', 'y_best_alpha_1_t+3', 'y_best_beta_1_t+3', 'y_best_omega_t+3', 'y_best_mu_t+3', 'y_best_nu_tgarch_t+3', 'rv_t+4', 'lg_return_t+4', 'y_best_alpha_1_t+4', 'y_best_beta_1_t+4', 'y_best_omega_t+4', 'y_best_mu_t+4', 'y_best_nu_tgarch_t+4', 'rv_t+5', 'lg_return_t+5', 'y_best_alpha_1_t+5', 'y_best_beta_1_t+5', 'y_best_omega_t+5', 'y_best_mu_t+5', 'y_best_nu_tgarch_t+5', 'rv_t+6', 'lg_return_t+6', 'y_best_alpha_1_t+6', 'y_best_beta_1_t+6', 'y_best_omega_t+6', 'y_best_mu_t+6', 'y_best_nu_tgarch_t+6', 'rv_t+7', 'lg_return_t+7', 'y_best_alpha_1_t+7', 'y_best_beta_1_t+7', 'y_best_omega_t+7', 'y_best_mu_t+7', 'y_best_nu_tgarch_t+7', 'rv_t+8', 'lg_return_t+8', 'y_best_alpha_1_t+8', 'y_best_beta_1_t+8', 'y_best_omega_t+8', 'y_best_mu_t+8', 'y_best_nu_tgarch_t+8', 'rv_t+9', 'lg_return_t+9', 'y_best_alpha_1_t+9', 'y_best_beta_1_t+9', 'y_best_omega_t+9', 'y_best_mu_t+9', 'y_best_nu_tgarch_t+9', 'rv_t+10', 'lg_return_t+10', 'y_best_alpha_1_t+10', 'y_best_beta_1_t+10', 'y_best_omega_t+10', 'y_best_mu_t+10', 'y_best_nu_tgarch_t+10', 'rv_t+11', 'lg_return_t+11', 'y_best_alpha_1_t+11', 'y_best_beta_1_t+11', 'y_best_omega_t+11', 'y_best_mu_t+11', 'y_best_nu_tgarch_t+11', 'rv_t+12', 'lg_return_t+12', 'y_best_alpha_1_t+12', 'y_best_beta_1_t+12', 'y_best_omega_t+12', 'y_best_mu_t+12', 'y_best_nu_tgarch_t+12', 'rv_t+13', 'lg_return_t+13', 'y_best_alpha_1_t+13', 'y_best_beta_1_t+13', 'y_best_omega_t+13', 'y_best_mu_t+13', 'y_best_nu_tgarch_t+13', 'rv_t+14', 'lg_return_t+14', 'y_best_alpha_1_t+14', 'y_best_beta_1_t+14', 'y_best_omega_t+14', 'y_best_mu_t+14', 'y_best_nu_tgarch_t+14', 'rv_t+15', 'lg_return_t+15', 'y_best_alpha_1_t+15', 'y_best_beta_1_t+15', 'y_best_omega_t+15', 'y_best_mu_t+15', 'y_best_nu_tgarch_t+15', 'rv_t+16', 'lg_return_t+16', 'y_best_alpha_1_t+16', 'y_best_beta_1_t+16', 'y_best_omega_t+16', 'y_best_mu_t+16', 'y_best_nu_tgarch_t+16', 'rv_t+17', 'lg_return_t+17', 'y_best_alpha_1_t+17', 'y_best_beta_1_t+17', 'y_best_omega_t+17', 'y_best_mu_t+17', 'y_best_nu_tgarch_t+17', 'rv_t+18', 'lg_return_t+18', 'y_best_alpha_1_t+18', 'y_best_beta_1_t+18', 'y_best_omega_t+18', 'y_best_mu_t+18', 'y_best_nu_tgarch_t+18', 'rv_t+19', 'lg_return_t+19', 'y_best_alpha_1_t+19', 'y_best_beta_1_t+19', 'y_best_omega_t+19', 'y_best_mu_t+19', 'y_best_nu_tgarch_t+19', 'rv_t+20', 'lg_return_t+20', 'y_best_alpha_1_t+20', 'y_best_beta_1_t+20', 'y_best_omega_t+20', 'y_best_mu_t+20', 'y_best_nu_tgarch_t+20', 'rv_t+21', 'lg_return_t+21', 'y_best_alpha_1_t+21', 'y_best_beta_1_t+21', 'y_best_omega_t+21', 'y_best_mu_t+21', 'y_best_nu_tgarch_t+21', 'rv_t+22', 'lg_return_t+22', 'y_best_alpha_1_t+22', 'y_best_beta_1_t+22', 'y_best_omega_t+22', 'y_best_mu_t+22', 'y_best_nu_tgarch_t+22', 'rv_t+23', 'lg_return_t+23', 'y_best_alpha_1_t+23', 'y_best_beta_1_t+23', 'y_best_omega_t+23', 'y_best_mu_t+23', 'y_best_nu_tgarch_t+23', 'rv_t+24', 'lg_return_t+24', 'y_best_alpha_1_t+24', 'y_best_beta_1_t+24', 'y_best_omega_t+24', 'y_best_mu_t+24', 'y_best_nu_tgarch_t+24', 'rv_t+25', 'lg_return_t+25', 'y_best_alpha_1_t+25', 'y_best_beta_1_t+25', 'y_best_omega_t+25', 'y_best_mu_t+25', 'y_best_nu_tgarch_t+25', 'rv_t+26', 'lg_return_t+26', 'y_best_alpha_1_t+26', 'y_best_beta_1_t+26', 'y_best_omega_t+26', 'y_best_mu_t+26', 'y_best_nu_tgarch_t+26', 'rv_t+27', 'lg_return_t+27', 'y_best_alpha_1_t+27', 'y_best_beta_1_t+27', 'y_best_omega_t+27', 'y_best_mu_t+27', 'y_best_nu_tgarch_t+27', 'rv_t+28', 'lg_return_t+28', 'y_best_alpha_1_t+28', 'y_best_beta_1_t+28', 'y_best_omega_t+28', 'y_best_mu_t+28', 'y_best_nu_tgarch_t+28', 'rv_t+29', 'lg_return_t+29', 'y_best_alpha_1_t+29', 'y_best_beta_1_t+29', 'y_best_omega_t+29', 'y_best_mu_t+29', 'y_best_nu_tgarch_t+29', 'rv_t+30', 'lg_return_t+30', 'y_best_alpha_1_t+30', 'y_best_beta_1_t+30', 'y_best_omega_t+30', 'y_best_mu_t+30', 'y_best_nu_tgarch_t+30', 'rv_t+31', 'lg_return_t+31', 'y_best_alpha_1_t+31', 'y_best_beta_1_t+31', 'y_best_omega_t+31', 'y_best_mu_t+31', 'y_best_nu_tgarch_t+31', 'rv_t+32', 'lg_return_t+32', 'y_best_alpha_1_t+32', 'y_best_beta_1_t+32', 'y_best_omega_t+32', 'y_best_mu_t+32', 'y_best_nu_tgarch_t+32', 'rv_t+33', 'lg_return_t+33', 'y_best_alpha_1_t+33', 'y_best_beta_1_t+33', 'y_best_omega_t+33', 'y_best_mu_t+33', 'y_best_nu_tgarch_t+33', 'rv_t+34', 'lg_return_t+34', 'y_best_alpha_1_t+34', 'y_best_beta_1_t+34', 'y_best_omega_t+34', 'y_best_mu_t+34', 'y_best_nu_tgarch_t+34', 'rv_t+35', 'lg_return_t+35', 'y_best_alpha_1_t+35', 'y_best_beta_1_t+35', 'y_best_omega_t+35', 'y_best_mu_t+35', 'y_best_nu_tgarch_t+35', 'rv_t+36', 'lg_return_t+36', 'y_best_alpha_1_t+36', 'y_best_beta_1_t+36', 'y_best_omega_t+36', 'y_best_mu_t+36', 'y_best_nu_tgarch_t+36', 'rv_t+37', 'lg_return_t+37', 'y_best_alpha_1_t+37', 'y_best_beta_1_t+37', 'y_best_omega_t+37', 'y_best_mu_t+37', 'y_best_nu_tgarch_t+37', 'rv_t+38', 'lg_return_t+38', 'y_best_alpha_1_t+38', 'y_best_beta_1_t+38', 'y_best_omega_t+38', 'y_best_mu_t+38', 'y_best_nu_tgarch_t+38', 'rv_t+39', 'lg_return_t+39', 'y_best_alpha_1_t+39', 'y_best_beta_1_t+39', 'y_best_omega_t+39', 'y_best_mu_t+39', 'y_best_nu_tgarch_t+39', 'rv_t+40', 'lg_return_t+40', 'y_best_alpha_1_t+40', 'y_best_beta_1_t+40', 'y_best_omega_t+40', 'y_best_mu_t+40', 'y_best_nu_tgarch_t+40', 'rv_t+41', 'lg_return_t+41', 'y_best_alpha_1_t+41', 'y_best_beta_1_t+41', 'y_best_omega_t+41', 'y_best_mu_t+41', 'y_best_nu_tgarch_t+41', 'rv_t+42', 'lg_return_t+42', 'y_best_alpha_1_t+42', 'y_best_beta_1_t+42', 'y_best_omega_t+42', 'y_best_mu_t+42', 'y_best_nu_tgarch_t+42', 'rv_t+43', 'lg_return_t+43', 'y_best_alpha_1_t+43', 'y_best_beta_1_t+43', 'y_best_omega_t+43', 'y_best_mu_t+43', 'y_best_nu_tgarch_t+43', 'rv_t+44', 'lg_return_t+44', 'y_best_alpha_1_t+44', 'y_best_beta_1_t+44', 'y_best_omega_t+44', 'y_best_mu_t+44', 'y_best_nu_tgarch_t+44', 'rv_t+45', 'lg_return_t+45', 'y_best_alpha_1_t+45', 'y_best_beta_1_t+45', 'y_best_omega_t+45', 'y_best_mu_t+45', 'y_best_nu_tgarch_t+45', 'rv_t+46', 'lg_return_t+46', 'y_best_alpha_1_t+46', 'y_best_beta_1_t+46', 'y_best_omega_t+46', 'y_best_mu_t+46', 'y_best_nu_tgarch_t+46', 'rv_t+47', 'lg_return_t+47', 'y_best_alpha_1_t+47', 'y_best_beta_1_t+47', 'y_best_omega_t+47', 'y_best_mu_t+47', 'y_best_nu_tgarch_t+47', 'rv_t+48', 'lg_return_t+48', 'y_best_alpha_1_t+48', 'y_best_beta_1_t+48', 'y_best_omega_t+48', 'y_best_mu_t+48', 'y_best_nu_tgarch_t+48', 'rv_t+49', 'lg_return_t+49', 'y_best_alpha_1_t+49', 'y_best_beta_1_t+49', 'y_best_omega_t+49', 'y_best_mu_t+49', 'y_best_nu_tgarch_t+49', 'rv_t+50', 'lg_return_t+50', 'y_best_alpha_1_t+50', 'y_best_beta_1_t+50', 'y_best_omega_t+50', 'y_best_mu_t+50', 'y_best_nu_tgarch_t+50', 'rv_t+51', 'lg_return_t+51', 'y_best_alpha_1_t+51', 'y_best_beta_1_t+51', 'y_best_omega_t+51', 'y_best_mu_t+51', 'y_best_nu_tgarch_t+51', 'rv_t+52', 'lg_return_t+52', 'y_best_alpha_1_t+52', 'y_best_beta_1_t+52', 'y_best_omega_t+52', 'y_best_mu_t+52', 'y_best_nu_tgarch_t+52', 'rv_t+53', 'lg_return_t+53', 'y_best_alpha_1_t+53', 'y_best_beta_1_t+53', 'y_best_omega_t+53', 'y_best_mu_t+53', 'y_best_nu_tgarch_t+53', 'rv_t+54', 'lg_return_t+54', 'y_best_alpha_1_t+54', 'y_best_beta_1_t+54', 'y_best_omega_t+54', 'y_best_mu_t+54', 'y_best_nu_tgarch_t+54', 'rv_t+55', 'lg_return_t+55', 'y_best_alpha_1_t+55', 'y_best_beta_1_t+55', 'y_best_omega_t+55', 'y_best_mu_t+55', 'y_best_nu_tgarch_t+55', 'rv_t+56', 'lg_return_t+56', 'y_best_alpha_1_t+56', 'y_best_beta_1_t+56', 'y_best_omega_t+56', 'y_best_mu_t+56', 'y_best_nu_tgarch_t+56', 'rv_t+57', 'lg_return_t+57', 'y_best_alpha_1_t+57', 'y_best_beta_1_t+57', 'y_best_omega_t+57', 'y_best_mu_t+57', 'y_best_nu_tgarch_t+57', 'rv_t+58', 'lg_return_t+58', 'y_best_alpha_1_t+58', 'y_best_beta_1_t+58', 'y_best_omega_t+58', 'y_best_mu_t+58', 'y_best_nu_tgarch_t+58', 'rv_t+59', 'lg_return_t+59', 'y_best_alpha_1_t+59', 'y_best_beta_1_t+59', 'y_best_omega_t+59', 'y_best_mu_t+59', 'y_best_nu_tgarch_t+59', 'rv_t+60', 'lg_return_t+60', 'y_best_alpha_1_t+60', 'y_best_beta_1_t+60', 'y_best_omega_t+60', 'y_best_mu_t+60', 'y_best_nu_tgarch_t+60']
Loading data for hierharhical tasks with no additional x feature¶
In [5]:
import numpy as np
load_data_object_2_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_2.pkl")
with open(load_data_object_2_file_path, "rb") as f:
structured_data_dict_2 = pickle.load(f)
print("Data dictionary 2 loaded successfully.")
eur2_X_price = structured_data_dict_2["EURUSD"].get("X_other")
eur2_X_time = structured_data_dict_2["EURUSD"].get("X_time")
eur2_y = structured_data_dict_2["EURUSD"].get("y")
eur2_y_columns = structured_data_dict_2["EURUSD"].get("y_columns")
eur2_X_columns = structured_data_dict_2["EURUSD"].get("X_other_columns")
print(eur2_X_columns)
print(eur2_y[:,0,0])
Data dictionary 2 loaded successfully. ['rv_t-59', 'x_best_alpha_1_t-59', 'x_best_beta_1_t-59', 'x_best_omega_t-59', 'x_best_mu_t-59', 'x_best_nu_tgarch_t-59', 'rv_t-58', 'x_best_alpha_1_t-58', 'x_best_beta_1_t-58', 'x_best_omega_t-58', 'x_best_mu_t-58', 'x_best_nu_tgarch_t-58', 'rv_t-57', 'x_best_alpha_1_t-57', 'x_best_beta_1_t-57', 'x_best_omega_t-57', 'x_best_mu_t-57', 'x_best_nu_tgarch_t-57', 'rv_t-56', 'x_best_alpha_1_t-56', 'x_best_beta_1_t-56', 'x_best_omega_t-56', 'x_best_mu_t-56', 'x_best_nu_tgarch_t-56', 'rv_t-55', 'x_best_alpha_1_t-55', 'x_best_beta_1_t-55', 'x_best_omega_t-55', 'x_best_mu_t-55', 'x_best_nu_tgarch_t-55', 'rv_t-54', 'x_best_alpha_1_t-54', 'x_best_beta_1_t-54', 'x_best_omega_t-54', 'x_best_mu_t-54', 'x_best_nu_tgarch_t-54', 'rv_t-53', 'x_best_alpha_1_t-53', 'x_best_beta_1_t-53', 'x_best_omega_t-53', 'x_best_mu_t-53', 'x_best_nu_tgarch_t-53', 'rv_t-52', 'x_best_alpha_1_t-52', 'x_best_beta_1_t-52', 'x_best_omega_t-52', 'x_best_mu_t-52', 'x_best_nu_tgarch_t-52', 'rv_t-51', 'x_best_alpha_1_t-51', 'x_best_beta_1_t-51', 'x_best_omega_t-51', 'x_best_mu_t-51', 'x_best_nu_tgarch_t-51', 'rv_t-50', 'x_best_alpha_1_t-50', 'x_best_beta_1_t-50', 'x_best_omega_t-50', 'x_best_mu_t-50', 'x_best_nu_tgarch_t-50', 'rv_t-49', 'x_best_alpha_1_t-49', 'x_best_beta_1_t-49', 'x_best_omega_t-49', 'x_best_mu_t-49', 'x_best_nu_tgarch_t-49', 'rv_t-48', 'x_best_alpha_1_t-48', 'x_best_beta_1_t-48', 'x_best_omega_t-48', 'x_best_mu_t-48', 'x_best_nu_tgarch_t-48', 'rv_t-47', 'x_best_alpha_1_t-47', 'x_best_beta_1_t-47', 'x_best_omega_t-47', 'x_best_mu_t-47', 'x_best_nu_tgarch_t-47', 'rv_t-46', 'x_best_alpha_1_t-46', 'x_best_beta_1_t-46', 'x_best_omega_t-46', 'x_best_mu_t-46', 'x_best_nu_tgarch_t-46', 'rv_t-45', 'x_best_alpha_1_t-45', 'x_best_beta_1_t-45', 'x_best_omega_t-45', 'x_best_mu_t-45', 'x_best_nu_tgarch_t-45', 'rv_t-44', 'x_best_alpha_1_t-44', 'x_best_beta_1_t-44', 'x_best_omega_t-44', 'x_best_mu_t-44', 'x_best_nu_tgarch_t-44', 'rv_t-43', 'x_best_alpha_1_t-43', 'x_best_beta_1_t-43', 'x_best_omega_t-43', 'x_best_mu_t-43', 'x_best_nu_tgarch_t-43', 'rv_t-42', 'x_best_alpha_1_t-42', 'x_best_beta_1_t-42', 'x_best_omega_t-42', 'x_best_mu_t-42', 'x_best_nu_tgarch_t-42', 'rv_t-41', 'x_best_alpha_1_t-41', 'x_best_beta_1_t-41', 'x_best_omega_t-41', 'x_best_mu_t-41', 'x_best_nu_tgarch_t-41', 'rv_t-40', 'x_best_alpha_1_t-40', 'x_best_beta_1_t-40', 'x_best_omega_t-40', 'x_best_mu_t-40', 'x_best_nu_tgarch_t-40', 'rv_t-39', 'x_best_alpha_1_t-39', 'x_best_beta_1_t-39', 'x_best_omega_t-39', 'x_best_mu_t-39', 'x_best_nu_tgarch_t-39', 'rv_t-38', 'x_best_alpha_1_t-38', 'x_best_beta_1_t-38', 'x_best_omega_t-38', 'x_best_mu_t-38', 'x_best_nu_tgarch_t-38', 'rv_t-37', 'x_best_alpha_1_t-37', 'x_best_beta_1_t-37', 'x_best_omega_t-37', 'x_best_mu_t-37', 'x_best_nu_tgarch_t-37', 'rv_t-36', 'x_best_alpha_1_t-36', 'x_best_beta_1_t-36', 'x_best_omega_t-36', 'x_best_mu_t-36', 'x_best_nu_tgarch_t-36', 'rv_t-35', 'x_best_alpha_1_t-35', 'x_best_beta_1_t-35', 'x_best_omega_t-35', 'x_best_mu_t-35', 'x_best_nu_tgarch_t-35', 'rv_t-34', 'x_best_alpha_1_t-34', 'x_best_beta_1_t-34', 'x_best_omega_t-34', 'x_best_mu_t-34', 'x_best_nu_tgarch_t-34', 'rv_t-33', 'x_best_alpha_1_t-33', 'x_best_beta_1_t-33', 'x_best_omega_t-33', 'x_best_mu_t-33', 'x_best_nu_tgarch_t-33', 'rv_t-32', 'x_best_alpha_1_t-32', 'x_best_beta_1_t-32', 'x_best_omega_t-32', 'x_best_mu_t-32', 'x_best_nu_tgarch_t-32', 'rv_t-31', 'x_best_alpha_1_t-31', 'x_best_beta_1_t-31', 'x_best_omega_t-31', 'x_best_mu_t-31', 'x_best_nu_tgarch_t-31', 'rv_t-30', 'x_best_alpha_1_t-30', 'x_best_beta_1_t-30', 'x_best_omega_t-30', 'x_best_mu_t-30', 'x_best_nu_tgarch_t-30', 'rv_t-29', 'x_best_alpha_1_t-29', 'x_best_beta_1_t-29', 'x_best_omega_t-29', 'x_best_mu_t-29', 'x_best_nu_tgarch_t-29', 'rv_t-28', 'x_best_alpha_1_t-28', 'x_best_beta_1_t-28', 'x_best_omega_t-28', 'x_best_mu_t-28', 'x_best_nu_tgarch_t-28', 'rv_t-27', 'x_best_alpha_1_t-27', 'x_best_beta_1_t-27', 'x_best_omega_t-27', 'x_best_mu_t-27', 'x_best_nu_tgarch_t-27', 'rv_t-26', 'x_best_alpha_1_t-26', 'x_best_beta_1_t-26', 'x_best_omega_t-26', 'x_best_mu_t-26', 'x_best_nu_tgarch_t-26', 'rv_t-25', 'x_best_alpha_1_t-25', 'x_best_beta_1_t-25', 'x_best_omega_t-25', 'x_best_mu_t-25', 'x_best_nu_tgarch_t-25', 'rv_t-24', 'x_best_alpha_1_t-24', 'x_best_beta_1_t-24', 'x_best_omega_t-24', 'x_best_mu_t-24', 'x_best_nu_tgarch_t-24', 'rv_t-23', 'x_best_alpha_1_t-23', 'x_best_beta_1_t-23', 'x_best_omega_t-23', 'x_best_mu_t-23', 'x_best_nu_tgarch_t-23', 'rv_t-22', 'x_best_alpha_1_t-22', 'x_best_beta_1_t-22', 'x_best_omega_t-22', 'x_best_mu_t-22', 'x_best_nu_tgarch_t-22', 'rv_t-21', 'x_best_alpha_1_t-21', 'x_best_beta_1_t-21', 'x_best_omega_t-21', 'x_best_mu_t-21', 'x_best_nu_tgarch_t-21', 'rv_t-20', 'x_best_alpha_1_t-20', 'x_best_beta_1_t-20', 'x_best_omega_t-20', 'x_best_mu_t-20', 'x_best_nu_tgarch_t-20', 'rv_t-19', 'x_best_alpha_1_t-19', 'x_best_beta_1_t-19', 'x_best_omega_t-19', 'x_best_mu_t-19', 'x_best_nu_tgarch_t-19', 'rv_t-18', 'x_best_alpha_1_t-18', 'x_best_beta_1_t-18', 'x_best_omega_t-18', 'x_best_mu_t-18', 'x_best_nu_tgarch_t-18', 'rv_t-17', 'x_best_alpha_1_t-17', 'x_best_beta_1_t-17', 'x_best_omega_t-17', 'x_best_mu_t-17', 'x_best_nu_tgarch_t-17', 'rv_t-16', 'x_best_alpha_1_t-16', 'x_best_beta_1_t-16', 'x_best_omega_t-16', 'x_best_mu_t-16', 'x_best_nu_tgarch_t-16', 'rv_t-15', 'x_best_alpha_1_t-15', 'x_best_beta_1_t-15', 'x_best_omega_t-15', 'x_best_mu_t-15', 'x_best_nu_tgarch_t-15', 'rv_t-14', 'x_best_alpha_1_t-14', 'x_best_beta_1_t-14', 'x_best_omega_t-14', 'x_best_mu_t-14', 'x_best_nu_tgarch_t-14', 'rv_t-13', 'x_best_alpha_1_t-13', 'x_best_beta_1_t-13', 'x_best_omega_t-13', 'x_best_mu_t-13', 'x_best_nu_tgarch_t-13', 'rv_t-12', 'x_best_alpha_1_t-12', 'x_best_beta_1_t-12', 'x_best_omega_t-12', 'x_best_mu_t-12', 'x_best_nu_tgarch_t-12', 'rv_t-11', 'x_best_alpha_1_t-11', 'x_best_beta_1_t-11', 'x_best_omega_t-11', 'x_best_mu_t-11', 'x_best_nu_tgarch_t-11', 'rv_t-10', 'x_best_alpha_1_t-10', 'x_best_beta_1_t-10', 'x_best_omega_t-10', 'x_best_mu_t-10', 'x_best_nu_tgarch_t-10', 'rv_t-9', 'x_best_alpha_1_t-9', 'x_best_beta_1_t-9', 'x_best_omega_t-9', 'x_best_mu_t-9', 'x_best_nu_tgarch_t-9', 'rv_t-8', 'x_best_alpha_1_t-8', 'x_best_beta_1_t-8', 'x_best_omega_t-8', 'x_best_mu_t-8', 'x_best_nu_tgarch_t-8', 'rv_t-7', 'x_best_alpha_1_t-7', 'x_best_beta_1_t-7', 'x_best_omega_t-7', 'x_best_mu_t-7', 'x_best_nu_tgarch_t-7', 'rv_t-6', 'x_best_alpha_1_t-6', 'x_best_beta_1_t-6', 'x_best_omega_t-6', 'x_best_mu_t-6', 'x_best_nu_tgarch_t-6', 'rv_t-5', 'x_best_alpha_1_t-5', 'x_best_beta_1_t-5', 'x_best_omega_t-5', 'x_best_mu_t-5', 'x_best_nu_tgarch_t-5', 'rv_t-4', 'x_best_alpha_1_t-4', 'x_best_beta_1_t-4', 'x_best_omega_t-4', 'x_best_mu_t-4', 'x_best_nu_tgarch_t-4', 'rv_t-3', 'x_best_alpha_1_t-3', 'x_best_beta_1_t-3', 'x_best_omega_t-3', 'x_best_mu_t-3', 'x_best_nu_tgarch_t-3', 'rv_t-2', 'x_best_alpha_1_t-2', 'x_best_beta_1_t-2', 'x_best_omega_t-2', 'x_best_mu_t-2', 'x_best_nu_tgarch_t-2', 'rv_t-1', 'x_best_alpha_1_t-1', 'x_best_beta_1_t-1', 'x_best_omega_t-1', 'x_best_mu_t-1', 'x_best_nu_tgarch_t-1', 'rv_t-0', 'x_best_alpha_1_t-0', 'x_best_beta_1_t-0', 'x_best_omega_t-0', 'x_best_mu_t-0', 'x_best_nu_tgarch_t-0'] [0.10789115 0.0094245 0.12050298 ... 0.35689057 0.31497138 0.32454847]
Loading a third type of data¶
In [6]:
import numpy as np
load_data_object_3_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_3.pkl")
with open(load_data_object_3_file_path, "rb") as f:
structured_data_dict_3 = pickle.load(f)
print("Data dictionary 3 loaded successfully.")
eur3_X_price = structured_data_dict_3["EURUSD"].get("X_other")
eur3_X_time = structured_data_dict_3["EURUSD"].get("X_time")
eur3_y = structured_data_dict_3["EURUSD"].get("y")
eur3_y_columns = structured_data_dict_3["EURUSD"].get("y_columns")
eur3_X_columns = structured_data_dict_3["EURUSD"].get("X_other_columns")
print(eur3_X_price.shape)
print(eur3_y.shape)
print(eur3_X_columns)
print(eur3_y_columns)
print(eur3_y[:,0,0])
Data dictionary 3 loaded successfully. (3782, 60, 1) (3782, 60, 7) ['rv_t-59', 'rv_t-58', 'rv_t-57', 'rv_t-56', 'rv_t-55', 'rv_t-54', 'rv_t-53', 'rv_t-52', 'rv_t-51', 'rv_t-50', 'rv_t-49', 'rv_t-48', 'rv_t-47', 'rv_t-46', 'rv_t-45', 'rv_t-44', 'rv_t-43', 'rv_t-42', 'rv_t-41', 'rv_t-40', 'rv_t-39', 'rv_t-38', 'rv_t-37', 'rv_t-36', 'rv_t-35', 'rv_t-34', 'rv_t-33', 'rv_t-32', 'rv_t-31', 'rv_t-30', 'rv_t-29', 'rv_t-28', 'rv_t-27', 'rv_t-26', 'rv_t-25', 'rv_t-24', 'rv_t-23', 'rv_t-22', 'rv_t-21', 'rv_t-20', 'rv_t-19', 'rv_t-18', 'rv_t-17', 'rv_t-16', 'rv_t-15', 'rv_t-14', 'rv_t-13', 'rv_t-12', 'rv_t-11', 'rv_t-10', 'rv_t-9', 'rv_t-8', 'rv_t-7', 'rv_t-6', 'rv_t-5', 'rv_t-4', 'rv_t-3', 'rv_t-2', 'rv_t-1', 'rv_t-0'] ['rv_t+1', 'lg_return_t+1', 'y_best_alpha_1_t+1', 'y_best_beta_1_t+1', 'y_best_omega_t+1', 'y_best_mu_t+1', 'y_best_nu_tgarch_t+1', 'rv_t+2', 'lg_return_t+2', 'y_best_alpha_1_t+2', 'y_best_beta_1_t+2', 'y_best_omega_t+2', 'y_best_mu_t+2', 'y_best_nu_tgarch_t+2', 'rv_t+3', 'lg_return_t+3', 'y_best_alpha_1_t+3', 'y_best_beta_1_t+3', 'y_best_omega_t+3', 'y_best_mu_t+3', 'y_best_nu_tgarch_t+3', 'rv_t+4', 'lg_return_t+4', 'y_best_alpha_1_t+4', 'y_best_beta_1_t+4', 'y_best_omega_t+4', 'y_best_mu_t+4', 'y_best_nu_tgarch_t+4', 'rv_t+5', 'lg_return_t+5', 'y_best_alpha_1_t+5', 'y_best_beta_1_t+5', 'y_best_omega_t+5', 'y_best_mu_t+5', 'y_best_nu_tgarch_t+5', 'rv_t+6', 'lg_return_t+6', 'y_best_alpha_1_t+6', 'y_best_beta_1_t+6', 'y_best_omega_t+6', 'y_best_mu_t+6', 'y_best_nu_tgarch_t+6', 'rv_t+7', 'lg_return_t+7', 'y_best_alpha_1_t+7', 'y_best_beta_1_t+7', 'y_best_omega_t+7', 'y_best_mu_t+7', 'y_best_nu_tgarch_t+7', 'rv_t+8', 'lg_return_t+8', 'y_best_alpha_1_t+8', 'y_best_beta_1_t+8', 'y_best_omega_t+8', 'y_best_mu_t+8', 'y_best_nu_tgarch_t+8', 'rv_t+9', 'lg_return_t+9', 'y_best_alpha_1_t+9', 'y_best_beta_1_t+9', 'y_best_omega_t+9', 'y_best_mu_t+9', 'y_best_nu_tgarch_t+9', 'rv_t+10', 'lg_return_t+10', 'y_best_alpha_1_t+10', 'y_best_beta_1_t+10', 'y_best_omega_t+10', 'y_best_mu_t+10', 'y_best_nu_tgarch_t+10', 'rv_t+11', 'lg_return_t+11', 'y_best_alpha_1_t+11', 'y_best_beta_1_t+11', 'y_best_omega_t+11', 'y_best_mu_t+11', 'y_best_nu_tgarch_t+11', 'rv_t+12', 'lg_return_t+12', 'y_best_alpha_1_t+12', 'y_best_beta_1_t+12', 'y_best_omega_t+12', 'y_best_mu_t+12', 'y_best_nu_tgarch_t+12', 'rv_t+13', 'lg_return_t+13', 'y_best_alpha_1_t+13', 'y_best_beta_1_t+13', 'y_best_omega_t+13', 'y_best_mu_t+13', 'y_best_nu_tgarch_t+13', 'rv_t+14', 'lg_return_t+14', 'y_best_alpha_1_t+14', 'y_best_beta_1_t+14', 'y_best_omega_t+14', 'y_best_mu_t+14', 'y_best_nu_tgarch_t+14', 'rv_t+15', 'lg_return_t+15', 'y_best_alpha_1_t+15', 'y_best_beta_1_t+15', 'y_best_omega_t+15', 'y_best_mu_t+15', 'y_best_nu_tgarch_t+15', 'rv_t+16', 'lg_return_t+16', 'y_best_alpha_1_t+16', 'y_best_beta_1_t+16', 'y_best_omega_t+16', 'y_best_mu_t+16', 'y_best_nu_tgarch_t+16', 'rv_t+17', 'lg_return_t+17', 'y_best_alpha_1_t+17', 'y_best_beta_1_t+17', 'y_best_omega_t+17', 'y_best_mu_t+17', 'y_best_nu_tgarch_t+17', 'rv_t+18', 'lg_return_t+18', 'y_best_alpha_1_t+18', 'y_best_beta_1_t+18', 'y_best_omega_t+18', 'y_best_mu_t+18', 'y_best_nu_tgarch_t+18', 'rv_t+19', 'lg_return_t+19', 'y_best_alpha_1_t+19', 'y_best_beta_1_t+19', 'y_best_omega_t+19', 'y_best_mu_t+19', 'y_best_nu_tgarch_t+19', 'rv_t+20', 'lg_return_t+20', 'y_best_alpha_1_t+20', 'y_best_beta_1_t+20', 'y_best_omega_t+20', 'y_best_mu_t+20', 'y_best_nu_tgarch_t+20', 'rv_t+21', 'lg_return_t+21', 'y_best_alpha_1_t+21', 'y_best_beta_1_t+21', 'y_best_omega_t+21', 'y_best_mu_t+21', 'y_best_nu_tgarch_t+21', 'rv_t+22', 'lg_return_t+22', 'y_best_alpha_1_t+22', 'y_best_beta_1_t+22', 'y_best_omega_t+22', 'y_best_mu_t+22', 'y_best_nu_tgarch_t+22', 'rv_t+23', 'lg_return_t+23', 'y_best_alpha_1_t+23', 'y_best_beta_1_t+23', 'y_best_omega_t+23', 'y_best_mu_t+23', 'y_best_nu_tgarch_t+23', 'rv_t+24', 'lg_return_t+24', 'y_best_alpha_1_t+24', 'y_best_beta_1_t+24', 'y_best_omega_t+24', 'y_best_mu_t+24', 'y_best_nu_tgarch_t+24', 'rv_t+25', 'lg_return_t+25', 'y_best_alpha_1_t+25', 'y_best_beta_1_t+25', 'y_best_omega_t+25', 'y_best_mu_t+25', 'y_best_nu_tgarch_t+25', 'rv_t+26', 'lg_return_t+26', 'y_best_alpha_1_t+26', 'y_best_beta_1_t+26', 'y_best_omega_t+26', 'y_best_mu_t+26', 'y_best_nu_tgarch_t+26', 'rv_t+27', 'lg_return_t+27', 'y_best_alpha_1_t+27', 'y_best_beta_1_t+27', 'y_best_omega_t+27', 'y_best_mu_t+27', 'y_best_nu_tgarch_t+27', 'rv_t+28', 'lg_return_t+28', 'y_best_alpha_1_t+28', 'y_best_beta_1_t+28', 'y_best_omega_t+28', 'y_best_mu_t+28', 'y_best_nu_tgarch_t+28', 'rv_t+29', 'lg_return_t+29', 'y_best_alpha_1_t+29', 'y_best_beta_1_t+29', 'y_best_omega_t+29', 'y_best_mu_t+29', 'y_best_nu_tgarch_t+29', 'rv_t+30', 'lg_return_t+30', 'y_best_alpha_1_t+30', 'y_best_beta_1_t+30', 'y_best_omega_t+30', 'y_best_mu_t+30', 'y_best_nu_tgarch_t+30', 'rv_t+31', 'lg_return_t+31', 'y_best_alpha_1_t+31', 'y_best_beta_1_t+31', 'y_best_omega_t+31', 'y_best_mu_t+31', 'y_best_nu_tgarch_t+31', 'rv_t+32', 'lg_return_t+32', 'y_best_alpha_1_t+32', 'y_best_beta_1_t+32', 'y_best_omega_t+32', 'y_best_mu_t+32', 'y_best_nu_tgarch_t+32', 'rv_t+33', 'lg_return_t+33', 'y_best_alpha_1_t+33', 'y_best_beta_1_t+33', 'y_best_omega_t+33', 'y_best_mu_t+33', 'y_best_nu_tgarch_t+33', 'rv_t+34', 'lg_return_t+34', 'y_best_alpha_1_t+34', 'y_best_beta_1_t+34', 'y_best_omega_t+34', 'y_best_mu_t+34', 'y_best_nu_tgarch_t+34', 'rv_t+35', 'lg_return_t+35', 'y_best_alpha_1_t+35', 'y_best_beta_1_t+35', 'y_best_omega_t+35', 'y_best_mu_t+35', 'y_best_nu_tgarch_t+35', 'rv_t+36', 'lg_return_t+36', 'y_best_alpha_1_t+36', 'y_best_beta_1_t+36', 'y_best_omega_t+36', 'y_best_mu_t+36', 'y_best_nu_tgarch_t+36', 'rv_t+37', 'lg_return_t+37', 'y_best_alpha_1_t+37', 'y_best_beta_1_t+37', 'y_best_omega_t+37', 'y_best_mu_t+37', 'y_best_nu_tgarch_t+37', 'rv_t+38', 'lg_return_t+38', 'y_best_alpha_1_t+38', 'y_best_beta_1_t+38', 'y_best_omega_t+38', 'y_best_mu_t+38', 'y_best_nu_tgarch_t+38', 'rv_t+39', 'lg_return_t+39', 'y_best_alpha_1_t+39', 'y_best_beta_1_t+39', 'y_best_omega_t+39', 'y_best_mu_t+39', 'y_best_nu_tgarch_t+39', 'rv_t+40', 'lg_return_t+40', 'y_best_alpha_1_t+40', 'y_best_beta_1_t+40', 'y_best_omega_t+40', 'y_best_mu_t+40', 'y_best_nu_tgarch_t+40', 'rv_t+41', 'lg_return_t+41', 'y_best_alpha_1_t+41', 'y_best_beta_1_t+41', 'y_best_omega_t+41', 'y_best_mu_t+41', 'y_best_nu_tgarch_t+41', 'rv_t+42', 'lg_return_t+42', 'y_best_alpha_1_t+42', 'y_best_beta_1_t+42', 'y_best_omega_t+42', 'y_best_mu_t+42', 'y_best_nu_tgarch_t+42', 'rv_t+43', 'lg_return_t+43', 'y_best_alpha_1_t+43', 'y_best_beta_1_t+43', 'y_best_omega_t+43', 'y_best_mu_t+43', 'y_best_nu_tgarch_t+43', 'rv_t+44', 'lg_return_t+44', 'y_best_alpha_1_t+44', 'y_best_beta_1_t+44', 'y_best_omega_t+44', 'y_best_mu_t+44', 'y_best_nu_tgarch_t+44', 'rv_t+45', 'lg_return_t+45', 'y_best_alpha_1_t+45', 'y_best_beta_1_t+45', 'y_best_omega_t+45', 'y_best_mu_t+45', 'y_best_nu_tgarch_t+45', 'rv_t+46', 'lg_return_t+46', 'y_best_alpha_1_t+46', 'y_best_beta_1_t+46', 'y_best_omega_t+46', 'y_best_mu_t+46', 'y_best_nu_tgarch_t+46', 'rv_t+47', 'lg_return_t+47', 'y_best_alpha_1_t+47', 'y_best_beta_1_t+47', 'y_best_omega_t+47', 'y_best_mu_t+47', 'y_best_nu_tgarch_t+47', 'rv_t+48', 'lg_return_t+48', 'y_best_alpha_1_t+48', 'y_best_beta_1_t+48', 'y_best_omega_t+48', 'y_best_mu_t+48', 'y_best_nu_tgarch_t+48', 'rv_t+49', 'lg_return_t+49', 'y_best_alpha_1_t+49', 'y_best_beta_1_t+49', 'y_best_omega_t+49', 'y_best_mu_t+49', 'y_best_nu_tgarch_t+49', 'rv_t+50', 'lg_return_t+50', 'y_best_alpha_1_t+50', 'y_best_beta_1_t+50', 'y_best_omega_t+50', 'y_best_mu_t+50', 'y_best_nu_tgarch_t+50', 'rv_t+51', 'lg_return_t+51', 'y_best_alpha_1_t+51', 'y_best_beta_1_t+51', 'y_best_omega_t+51', 'y_best_mu_t+51', 'y_best_nu_tgarch_t+51', 'rv_t+52', 'lg_return_t+52', 'y_best_alpha_1_t+52', 'y_best_beta_1_t+52', 'y_best_omega_t+52', 'y_best_mu_t+52', 'y_best_nu_tgarch_t+52', 'rv_t+53', 'lg_return_t+53', 'y_best_alpha_1_t+53', 'y_best_beta_1_t+53', 'y_best_omega_t+53', 'y_best_mu_t+53', 'y_best_nu_tgarch_t+53', 'rv_t+54', 'lg_return_t+54', 'y_best_alpha_1_t+54', 'y_best_beta_1_t+54', 'y_best_omega_t+54', 'y_best_mu_t+54', 'y_best_nu_tgarch_t+54', 'rv_t+55', 'lg_return_t+55', 'y_best_alpha_1_t+55', 'y_best_beta_1_t+55', 'y_best_omega_t+55', 'y_best_mu_t+55', 'y_best_nu_tgarch_t+55', 'rv_t+56', 'lg_return_t+56', 'y_best_alpha_1_t+56', 'y_best_beta_1_t+56', 'y_best_omega_t+56', 'y_best_mu_t+56', 'y_best_nu_tgarch_t+56', 'rv_t+57', 'lg_return_t+57', 'y_best_alpha_1_t+57', 'y_best_beta_1_t+57', 'y_best_omega_t+57', 'y_best_mu_t+57', 'y_best_nu_tgarch_t+57', 'rv_t+58', 'lg_return_t+58', 'y_best_alpha_1_t+58', 'y_best_beta_1_t+58', 'y_best_omega_t+58', 'y_best_mu_t+58', 'y_best_nu_tgarch_t+58', 'rv_t+59', 'lg_return_t+59', 'y_best_alpha_1_t+59', 'y_best_beta_1_t+59', 'y_best_omega_t+59', 'y_best_mu_t+59', 'y_best_nu_tgarch_t+59', 'rv_t+60', 'lg_return_t+60', 'y_best_alpha_1_t+60', 'y_best_beta_1_t+60', 'y_best_omega_t+60', 'y_best_mu_t+60', 'y_best_nu_tgarch_t+60'] [0.10789115 0.0094245 0.12050298 ... 0.35689057 0.31497138 0.32454847]
Loading a small data set¶
In [7]:
load_data_object_3_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_3.pkl")
with open(load_data_object_3_file_path, "rb") as f:
structured_data_dict_3 = pickle.load(f)
print("Data dictionary 4 loaded successfully.")
eur3s_X_price = structured_data_dict_3["EURUSD"]["X_other"][:200]
eur3s_X_time = structured_data_dict_3["EURUSD"]["X_time"][:200]
eur3s_y = structured_data_dict_3 ["EURUSD"]["y"][:200]
eur3s_y_columns = structured_data_dict_3["EURUSD"].get("y_columns")
eur3s_X_columns = structured_data_dict_3["EURUSD"].get("X_other_columns")
print(eur3s_X_columns)
print(eur3s_y_columns)
Data dictionary 4 loaded successfully. ['rv_t-59', 'rv_t-58', 'rv_t-57', 'rv_t-56', 'rv_t-55', 'rv_t-54', 'rv_t-53', 'rv_t-52', 'rv_t-51', 'rv_t-50', 'rv_t-49', 'rv_t-48', 'rv_t-47', 'rv_t-46', 'rv_t-45', 'rv_t-44', 'rv_t-43', 'rv_t-42', 'rv_t-41', 'rv_t-40', 'rv_t-39', 'rv_t-38', 'rv_t-37', 'rv_t-36', 'rv_t-35', 'rv_t-34', 'rv_t-33', 'rv_t-32', 'rv_t-31', 'rv_t-30', 'rv_t-29', 'rv_t-28', 'rv_t-27', 'rv_t-26', 'rv_t-25', 'rv_t-24', 'rv_t-23', 'rv_t-22', 'rv_t-21', 'rv_t-20', 'rv_t-19', 'rv_t-18', 'rv_t-17', 'rv_t-16', 'rv_t-15', 'rv_t-14', 'rv_t-13', 'rv_t-12', 'rv_t-11', 'rv_t-10', 'rv_t-9', 'rv_t-8', 'rv_t-7', 'rv_t-6', 'rv_t-5', 'rv_t-4', 'rv_t-3', 'rv_t-2', 'rv_t-1', 'rv_t-0'] ['rv_t+1', 'lg_return_t+1', 'y_best_alpha_1_t+1', 'y_best_beta_1_t+1', 'y_best_omega_t+1', 'y_best_mu_t+1', 'y_best_nu_tgarch_t+1', 'rv_t+2', 'lg_return_t+2', 'y_best_alpha_1_t+2', 'y_best_beta_1_t+2', 'y_best_omega_t+2', 'y_best_mu_t+2', 'y_best_nu_tgarch_t+2', 'rv_t+3', 'lg_return_t+3', 'y_best_alpha_1_t+3', 'y_best_beta_1_t+3', 'y_best_omega_t+3', 'y_best_mu_t+3', 'y_best_nu_tgarch_t+3', 'rv_t+4', 'lg_return_t+4', 'y_best_alpha_1_t+4', 'y_best_beta_1_t+4', 'y_best_omega_t+4', 'y_best_mu_t+4', 'y_best_nu_tgarch_t+4', 'rv_t+5', 'lg_return_t+5', 'y_best_alpha_1_t+5', 'y_best_beta_1_t+5', 'y_best_omega_t+5', 'y_best_mu_t+5', 'y_best_nu_tgarch_t+5', 'rv_t+6', 'lg_return_t+6', 'y_best_alpha_1_t+6', 'y_best_beta_1_t+6', 'y_best_omega_t+6', 'y_best_mu_t+6', 'y_best_nu_tgarch_t+6', 'rv_t+7', 'lg_return_t+7', 'y_best_alpha_1_t+7', 'y_best_beta_1_t+7', 'y_best_omega_t+7', 'y_best_mu_t+7', 'y_best_nu_tgarch_t+7', 'rv_t+8', 'lg_return_t+8', 'y_best_alpha_1_t+8', 'y_best_beta_1_t+8', 'y_best_omega_t+8', 'y_best_mu_t+8', 'y_best_nu_tgarch_t+8', 'rv_t+9', 'lg_return_t+9', 'y_best_alpha_1_t+9', 'y_best_beta_1_t+9', 'y_best_omega_t+9', 'y_best_mu_t+9', 'y_best_nu_tgarch_t+9', 'rv_t+10', 'lg_return_t+10', 'y_best_alpha_1_t+10', 'y_best_beta_1_t+10', 'y_best_omega_t+10', 'y_best_mu_t+10', 'y_best_nu_tgarch_t+10', 'rv_t+11', 'lg_return_t+11', 'y_best_alpha_1_t+11', 'y_best_beta_1_t+11', 'y_best_omega_t+11', 'y_best_mu_t+11', 'y_best_nu_tgarch_t+11', 'rv_t+12', 'lg_return_t+12', 'y_best_alpha_1_t+12', 'y_best_beta_1_t+12', 'y_best_omega_t+12', 'y_best_mu_t+12', 'y_best_nu_tgarch_t+12', 'rv_t+13', 'lg_return_t+13', 'y_best_alpha_1_t+13', 'y_best_beta_1_t+13', 'y_best_omega_t+13', 'y_best_mu_t+13', 'y_best_nu_tgarch_t+13', 'rv_t+14', 'lg_return_t+14', 'y_best_alpha_1_t+14', 'y_best_beta_1_t+14', 'y_best_omega_t+14', 'y_best_mu_t+14', 'y_best_nu_tgarch_t+14', 'rv_t+15', 'lg_return_t+15', 'y_best_alpha_1_t+15', 'y_best_beta_1_t+15', 'y_best_omega_t+15', 'y_best_mu_t+15', 'y_best_nu_tgarch_t+15', 'rv_t+16', 'lg_return_t+16', 'y_best_alpha_1_t+16', 'y_best_beta_1_t+16', 'y_best_omega_t+16', 'y_best_mu_t+16', 'y_best_nu_tgarch_t+16', 'rv_t+17', 'lg_return_t+17', 'y_best_alpha_1_t+17', 'y_best_beta_1_t+17', 'y_best_omega_t+17', 'y_best_mu_t+17', 'y_best_nu_tgarch_t+17', 'rv_t+18', 'lg_return_t+18', 'y_best_alpha_1_t+18', 'y_best_beta_1_t+18', 'y_best_omega_t+18', 'y_best_mu_t+18', 'y_best_nu_tgarch_t+18', 'rv_t+19', 'lg_return_t+19', 'y_best_alpha_1_t+19', 'y_best_beta_1_t+19', 'y_best_omega_t+19', 'y_best_mu_t+19', 'y_best_nu_tgarch_t+19', 'rv_t+20', 'lg_return_t+20', 'y_best_alpha_1_t+20', 'y_best_beta_1_t+20', 'y_best_omega_t+20', 'y_best_mu_t+20', 'y_best_nu_tgarch_t+20', 'rv_t+21', 'lg_return_t+21', 'y_best_alpha_1_t+21', 'y_best_beta_1_t+21', 'y_best_omega_t+21', 'y_best_mu_t+21', 'y_best_nu_tgarch_t+21', 'rv_t+22', 'lg_return_t+22', 'y_best_alpha_1_t+22', 'y_best_beta_1_t+22', 'y_best_omega_t+22', 'y_best_mu_t+22', 'y_best_nu_tgarch_t+22', 'rv_t+23', 'lg_return_t+23', 'y_best_alpha_1_t+23', 'y_best_beta_1_t+23', 'y_best_omega_t+23', 'y_best_mu_t+23', 'y_best_nu_tgarch_t+23', 'rv_t+24', 'lg_return_t+24', 'y_best_alpha_1_t+24', 'y_best_beta_1_t+24', 'y_best_omega_t+24', 'y_best_mu_t+24', 'y_best_nu_tgarch_t+24', 'rv_t+25', 'lg_return_t+25', 'y_best_alpha_1_t+25', 'y_best_beta_1_t+25', 'y_best_omega_t+25', 'y_best_mu_t+25', 'y_best_nu_tgarch_t+25', 'rv_t+26', 'lg_return_t+26', 'y_best_alpha_1_t+26', 'y_best_beta_1_t+26', 'y_best_omega_t+26', 'y_best_mu_t+26', 'y_best_nu_tgarch_t+26', 'rv_t+27', 'lg_return_t+27', 'y_best_alpha_1_t+27', 'y_best_beta_1_t+27', 'y_best_omega_t+27', 'y_best_mu_t+27', 'y_best_nu_tgarch_t+27', 'rv_t+28', 'lg_return_t+28', 'y_best_alpha_1_t+28', 'y_best_beta_1_t+28', 'y_best_omega_t+28', 'y_best_mu_t+28', 'y_best_nu_tgarch_t+28', 'rv_t+29', 'lg_return_t+29', 'y_best_alpha_1_t+29', 'y_best_beta_1_t+29', 'y_best_omega_t+29', 'y_best_mu_t+29', 'y_best_nu_tgarch_t+29', 'rv_t+30', 'lg_return_t+30', 'y_best_alpha_1_t+30', 'y_best_beta_1_t+30', 'y_best_omega_t+30', 'y_best_mu_t+30', 'y_best_nu_tgarch_t+30', 'rv_t+31', 'lg_return_t+31', 'y_best_alpha_1_t+31', 'y_best_beta_1_t+31', 'y_best_omega_t+31', 'y_best_mu_t+31', 'y_best_nu_tgarch_t+31', 'rv_t+32', 'lg_return_t+32', 'y_best_alpha_1_t+32', 'y_best_beta_1_t+32', 'y_best_omega_t+32', 'y_best_mu_t+32', 'y_best_nu_tgarch_t+32', 'rv_t+33', 'lg_return_t+33', 'y_best_alpha_1_t+33', 'y_best_beta_1_t+33', 'y_best_omega_t+33', 'y_best_mu_t+33', 'y_best_nu_tgarch_t+33', 'rv_t+34', 'lg_return_t+34', 'y_best_alpha_1_t+34', 'y_best_beta_1_t+34', 'y_best_omega_t+34', 'y_best_mu_t+34', 'y_best_nu_tgarch_t+34', 'rv_t+35', 'lg_return_t+35', 'y_best_alpha_1_t+35', 'y_best_beta_1_t+35', 'y_best_omega_t+35', 'y_best_mu_t+35', 'y_best_nu_tgarch_t+35', 'rv_t+36', 'lg_return_t+36', 'y_best_alpha_1_t+36', 'y_best_beta_1_t+36', 'y_best_omega_t+36', 'y_best_mu_t+36', 'y_best_nu_tgarch_t+36', 'rv_t+37', 'lg_return_t+37', 'y_best_alpha_1_t+37', 'y_best_beta_1_t+37', 'y_best_omega_t+37', 'y_best_mu_t+37', 'y_best_nu_tgarch_t+37', 'rv_t+38', 'lg_return_t+38', 'y_best_alpha_1_t+38', 'y_best_beta_1_t+38', 'y_best_omega_t+38', 'y_best_mu_t+38', 'y_best_nu_tgarch_t+38', 'rv_t+39', 'lg_return_t+39', 'y_best_alpha_1_t+39', 'y_best_beta_1_t+39', 'y_best_omega_t+39', 'y_best_mu_t+39', 'y_best_nu_tgarch_t+39', 'rv_t+40', 'lg_return_t+40', 'y_best_alpha_1_t+40', 'y_best_beta_1_t+40', 'y_best_omega_t+40', 'y_best_mu_t+40', 'y_best_nu_tgarch_t+40', 'rv_t+41', 'lg_return_t+41', 'y_best_alpha_1_t+41', 'y_best_beta_1_t+41', 'y_best_omega_t+41', 'y_best_mu_t+41', 'y_best_nu_tgarch_t+41', 'rv_t+42', 'lg_return_t+42', 'y_best_alpha_1_t+42', 'y_best_beta_1_t+42', 'y_best_omega_t+42', 'y_best_mu_t+42', 'y_best_nu_tgarch_t+42', 'rv_t+43', 'lg_return_t+43', 'y_best_alpha_1_t+43', 'y_best_beta_1_t+43', 'y_best_omega_t+43', 'y_best_mu_t+43', 'y_best_nu_tgarch_t+43', 'rv_t+44', 'lg_return_t+44', 'y_best_alpha_1_t+44', 'y_best_beta_1_t+44', 'y_best_omega_t+44', 'y_best_mu_t+44', 'y_best_nu_tgarch_t+44', 'rv_t+45', 'lg_return_t+45', 'y_best_alpha_1_t+45', 'y_best_beta_1_t+45', 'y_best_omega_t+45', 'y_best_mu_t+45', 'y_best_nu_tgarch_t+45', 'rv_t+46', 'lg_return_t+46', 'y_best_alpha_1_t+46', 'y_best_beta_1_t+46', 'y_best_omega_t+46', 'y_best_mu_t+46', 'y_best_nu_tgarch_t+46', 'rv_t+47', 'lg_return_t+47', 'y_best_alpha_1_t+47', 'y_best_beta_1_t+47', 'y_best_omega_t+47', 'y_best_mu_t+47', 'y_best_nu_tgarch_t+47', 'rv_t+48', 'lg_return_t+48', 'y_best_alpha_1_t+48', 'y_best_beta_1_t+48', 'y_best_omega_t+48', 'y_best_mu_t+48', 'y_best_nu_tgarch_t+48', 'rv_t+49', 'lg_return_t+49', 'y_best_alpha_1_t+49', 'y_best_beta_1_t+49', 'y_best_omega_t+49', 'y_best_mu_t+49', 'y_best_nu_tgarch_t+49', 'rv_t+50', 'lg_return_t+50', 'y_best_alpha_1_t+50', 'y_best_beta_1_t+50', 'y_best_omega_t+50', 'y_best_mu_t+50', 'y_best_nu_tgarch_t+50', 'rv_t+51', 'lg_return_t+51', 'y_best_alpha_1_t+51', 'y_best_beta_1_t+51', 'y_best_omega_t+51', 'y_best_mu_t+51', 'y_best_nu_tgarch_t+51', 'rv_t+52', 'lg_return_t+52', 'y_best_alpha_1_t+52', 'y_best_beta_1_t+52', 'y_best_omega_t+52', 'y_best_mu_t+52', 'y_best_nu_tgarch_t+52', 'rv_t+53', 'lg_return_t+53', 'y_best_alpha_1_t+53', 'y_best_beta_1_t+53', 'y_best_omega_t+53', 'y_best_mu_t+53', 'y_best_nu_tgarch_t+53', 'rv_t+54', 'lg_return_t+54', 'y_best_alpha_1_t+54', 'y_best_beta_1_t+54', 'y_best_omega_t+54', 'y_best_mu_t+54', 'y_best_nu_tgarch_t+54', 'rv_t+55', 'lg_return_t+55', 'y_best_alpha_1_t+55', 'y_best_beta_1_t+55', 'y_best_omega_t+55', 'y_best_mu_t+55', 'y_best_nu_tgarch_t+55', 'rv_t+56', 'lg_return_t+56', 'y_best_alpha_1_t+56', 'y_best_beta_1_t+56', 'y_best_omega_t+56', 'y_best_mu_t+56', 'y_best_nu_tgarch_t+56', 'rv_t+57', 'lg_return_t+57', 'y_best_alpha_1_t+57', 'y_best_beta_1_t+57', 'y_best_omega_t+57', 'y_best_mu_t+57', 'y_best_nu_tgarch_t+57', 'rv_t+58', 'lg_return_t+58', 'y_best_alpha_1_t+58', 'y_best_beta_1_t+58', 'y_best_omega_t+58', 'y_best_mu_t+58', 'y_best_nu_tgarch_t+58', 'rv_t+59', 'lg_return_t+59', 'y_best_alpha_1_t+59', 'y_best_beta_1_t+59', 'y_best_omega_t+59', 'y_best_mu_t+59', 'y_best_nu_tgarch_t+59', 'rv_t+60', 'lg_return_t+60', 'y_best_alpha_1_t+60', 'y_best_beta_1_t+60', 'y_best_omega_t+60', 'y_best_mu_t+60', 'y_best_nu_tgarch_t+60']
The forth type of data Compatible with Times Net and ITransformer¶
In [8]:
load_data_object_4_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_4.pkl")
with open(load_data_object_4_file_path, "rb") as f:
structured_data_dict_4 = pickle.load(f)
print("Data dictionary 4 loaded successfully.")
eur4_X_price = structured_data_dict_4["EURUSD"].get("X_other")
eur4_X_time = structured_data_dict_4["EURUSD"].get("X_time")
eur4_y = structured_data_dict_4["EURUSD"].get("y")
eur4_y_columns = structured_data_dict_4["EURUSD"].get("y_columns")
eur4_X_columns = structured_data_dict_4["EURUSD"].get("X_other_columns")
print(eur4_X_columns)
print(eur4_y_columns)
print(eur4_y[:,0,0])
Data dictionary 4 loaded successfully. ['rv_t-59', 'rv_t-58', 'rv_t-57', 'rv_t-56', 'rv_t-55', 'rv_t-54', 'rv_t-53', 'rv_t-52', 'rv_t-51', 'rv_t-50', 'rv_t-49', 'rv_t-48', 'rv_t-47', 'rv_t-46', 'rv_t-45', 'rv_t-44', 'rv_t-43', 'rv_t-42', 'rv_t-41', 'rv_t-40', 'rv_t-39', 'rv_t-38', 'rv_t-37', 'rv_t-36', 'rv_t-35', 'rv_t-34', 'rv_t-33', 'rv_t-32', 'rv_t-31', 'rv_t-30', 'rv_t-29', 'rv_t-28', 'rv_t-27', 'rv_t-26', 'rv_t-25', 'rv_t-24', 'rv_t-23', 'rv_t-22', 'rv_t-21', 'rv_t-20', 'rv_t-19', 'rv_t-18', 'rv_t-17', 'rv_t-16', 'rv_t-15', 'rv_t-14', 'rv_t-13', 'rv_t-12', 'rv_t-11', 'rv_t-10', 'rv_t-9', 'rv_t-8', 'rv_t-7', 'rv_t-6', 'rv_t-5', 'rv_t-4', 'rv_t-3', 'rv_t-2', 'rv_t-1', 'rv_t-0'] ['rv_t+1', 'lg_return_t+1', 'rv_t+2', 'lg_return_t+2', 'rv_t+3', 'lg_return_t+3', 'rv_t+4', 'lg_return_t+4', 'rv_t+5', 'lg_return_t+5', 'rv_t+6', 'lg_return_t+6', 'rv_t+7', 'lg_return_t+7', 'rv_t+8', 'lg_return_t+8', 'rv_t+9', 'lg_return_t+9', 'rv_t+10', 'lg_return_t+10', 'rv_t+11', 'lg_return_t+11', 'rv_t+12', 'lg_return_t+12', 'rv_t+13', 'lg_return_t+13', 'rv_t+14', 'lg_return_t+14', 'rv_t+15', 'lg_return_t+15', 'rv_t+16', 'lg_return_t+16', 'rv_t+17', 'lg_return_t+17', 'rv_t+18', 'lg_return_t+18', 'rv_t+19', 'lg_return_t+19', 'rv_t+20', 'lg_return_t+20', 'rv_t+21', 'lg_return_t+21', 'rv_t+22', 'lg_return_t+22', 'rv_t+23', 'lg_return_t+23', 'rv_t+24', 'lg_return_t+24', 'rv_t+25', 'lg_return_t+25', 'rv_t+26', 'lg_return_t+26', 'rv_t+27', 'lg_return_t+27', 'rv_t+28', 'lg_return_t+28', 'rv_t+29', 'lg_return_t+29', 'rv_t+30', 'lg_return_t+30', 'rv_t+31', 'lg_return_t+31', 'rv_t+32', 'lg_return_t+32', 'rv_t+33', 'lg_return_t+33', 'rv_t+34', 'lg_return_t+34', 'rv_t+35', 'lg_return_t+35', 'rv_t+36', 'lg_return_t+36', 'rv_t+37', 'lg_return_t+37', 'rv_t+38', 'lg_return_t+38', 'rv_t+39', 'lg_return_t+39', 'rv_t+40', 'lg_return_t+40', 'rv_t+41', 'lg_return_t+41', 'rv_t+42', 'lg_return_t+42', 'rv_t+43', 'lg_return_t+43', 'rv_t+44', 'lg_return_t+44', 'rv_t+45', 'lg_return_t+45', 'rv_t+46', 'lg_return_t+46', 'rv_t+47', 'lg_return_t+47', 'rv_t+48', 'lg_return_t+48', 'rv_t+49', 'lg_return_t+49', 'rv_t+50', 'lg_return_t+50', 'rv_t+51', 'lg_return_t+51', 'rv_t+52', 'lg_return_t+52', 'rv_t+53', 'lg_return_t+53', 'rv_t+54', 'lg_return_t+54', 'rv_t+55', 'lg_return_t+55', 'rv_t+56', 'lg_return_t+56', 'rv_t+57', 'lg_return_t+57', 'rv_t+58', 'lg_return_t+58', 'rv_t+59', 'lg_return_t+59', 'rv_t+60', 'lg_return_t+60'] [0.10789115 0.0094245 0.12050298 ... 0.35689057 0.31497138 0.32454847]
APPLE Test data¶
In [9]:
load_data_object_4_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_4.pkl")
with open(load_data_object_4_file_path, "rb") as f:
structured_data_dict_4 = pickle.load(f)
print("Data dictionary 4 loaded successfully.")
appl4_X_price = structured_data_dict_4["AAPL"].get("X_other")
appl4_X_time = structured_data_dict_4["AAPL"].get("X_time")
appl4_y = structured_data_dict_4["AAPL"].get("y")
appl4_y_columns = structured_data_dict_4["AAPL"].get("y_columns")
appl4_X_columns = structured_data_dict_4["AAPL"].get("X_other_columns")
print(appl4_X_columns)
print(appl4_y_columns)
print(appl4_y[:,0,0])
Data dictionary 4 loaded successfully. ['rv_t-59', 'rv_t-58', 'rv_t-57', 'rv_t-56', 'rv_t-55', 'rv_t-54', 'rv_t-53', 'rv_t-52', 'rv_t-51', 'rv_t-50', 'rv_t-49', 'rv_t-48', 'rv_t-47', 'rv_t-46', 'rv_t-45', 'rv_t-44', 'rv_t-43', 'rv_t-42', 'rv_t-41', 'rv_t-40', 'rv_t-39', 'rv_t-38', 'rv_t-37', 'rv_t-36', 'rv_t-35', 'rv_t-34', 'rv_t-33', 'rv_t-32', 'rv_t-31', 'rv_t-30', 'rv_t-29', 'rv_t-28', 'rv_t-27', 'rv_t-26', 'rv_t-25', 'rv_t-24', 'rv_t-23', 'rv_t-22', 'rv_t-21', 'rv_t-20', 'rv_t-19', 'rv_t-18', 'rv_t-17', 'rv_t-16', 'rv_t-15', 'rv_t-14', 'rv_t-13', 'rv_t-12', 'rv_t-11', 'rv_t-10', 'rv_t-9', 'rv_t-8', 'rv_t-7', 'rv_t-6', 'rv_t-5', 'rv_t-4', 'rv_t-3', 'rv_t-2', 'rv_t-1', 'rv_t-0'] ['rv_t+1', 'lg_return_t+1', 'rv_t+2', 'lg_return_t+2', 'rv_t+3', 'lg_return_t+3', 'rv_t+4', 'lg_return_t+4', 'rv_t+5', 'lg_return_t+5', 'rv_t+6', 'lg_return_t+6', 'rv_t+7', 'lg_return_t+7', 'rv_t+8', 'lg_return_t+8', 'rv_t+9', 'lg_return_t+9', 'rv_t+10', 'lg_return_t+10', 'rv_t+11', 'lg_return_t+11', 'rv_t+12', 'lg_return_t+12', 'rv_t+13', 'lg_return_t+13', 'rv_t+14', 'lg_return_t+14', 'rv_t+15', 'lg_return_t+15', 'rv_t+16', 'lg_return_t+16', 'rv_t+17', 'lg_return_t+17', 'rv_t+18', 'lg_return_t+18', 'rv_t+19', 'lg_return_t+19', 'rv_t+20', 'lg_return_t+20', 'rv_t+21', 'lg_return_t+21', 'rv_t+22', 'lg_return_t+22', 'rv_t+23', 'lg_return_t+23', 'rv_t+24', 'lg_return_t+24', 'rv_t+25', 'lg_return_t+25', 'rv_t+26', 'lg_return_t+26', 'rv_t+27', 'lg_return_t+27', 'rv_t+28', 'lg_return_t+28', 'rv_t+29', 'lg_return_t+29', 'rv_t+30', 'lg_return_t+30', 'rv_t+31', 'lg_return_t+31', 'rv_t+32', 'lg_return_t+32', 'rv_t+33', 'lg_return_t+33', 'rv_t+34', 'lg_return_t+34', 'rv_t+35', 'lg_return_t+35', 'rv_t+36', 'lg_return_t+36', 'rv_t+37', 'lg_return_t+37', 'rv_t+38', 'lg_return_t+38', 'rv_t+39', 'lg_return_t+39', 'rv_t+40', 'lg_return_t+40', 'rv_t+41', 'lg_return_t+41', 'rv_t+42', 'lg_return_t+42', 'rv_t+43', 'lg_return_t+43', 'rv_t+44', 'lg_return_t+44', 'rv_t+45', 'lg_return_t+45', 'rv_t+46', 'lg_return_t+46', 'rv_t+47', 'lg_return_t+47', 'rv_t+48', 'lg_return_t+48', 'rv_t+49', 'lg_return_t+49', 'rv_t+50', 'lg_return_t+50', 'rv_t+51', 'lg_return_t+51', 'rv_t+52', 'lg_return_t+52', 'rv_t+53', 'lg_return_t+53', 'rv_t+54', 'lg_return_t+54', 'rv_t+55', 'lg_return_t+55', 'rv_t+56', 'lg_return_t+56', 'rv_t+57', 'lg_return_t+57', 'rv_t+58', 'lg_return_t+58', 'rv_t+59', 'lg_return_t+59', 'rv_t+60', 'lg_return_t+60'] [ 0.44148683 1.36512504 0.86625199 ... 112.40750435 30.95861336 16.24492026]
Fift type of data¶
In [10]:
load_data_object_5_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_5.pkl")
with open(load_data_object_5_file_path, "rb") as f:
structured_data_dict_5 = pickle.load(f)
print("Data dictionary 5 loaded successfully.")
eur5_X_price = structured_data_dict_5["EURUSD"].get("X_other")
eur5_X_time = structured_data_dict_5["EURUSD"].get("X_time")
eur5_y = structured_data_dict_5["EURUSD"].get("y")
eur5_y_columns = structured_data_dict_5["EURUSD"].get("y_columns")
eur5_X_columns = structured_data_dict_5["EURUSD"].get("X_other_columns")
print(eur5_X_columns)
print(eur5_y_columns)
Data dictionary 5 loaded successfully. ['rv_t-59', 'rv_t-58', 'rv_t-57', 'rv_t-56', 'rv_t-55', 'rv_t-54', 'rv_t-53', 'rv_t-52', 'rv_t-51', 'rv_t-50', 'rv_t-49', 'rv_t-48', 'rv_t-47', 'rv_t-46', 'rv_t-45', 'rv_t-44', 'rv_t-43', 'rv_t-42', 'rv_t-41', 'rv_t-40', 'rv_t-39', 'rv_t-38', 'rv_t-37', 'rv_t-36', 'rv_t-35', 'rv_t-34', 'rv_t-33', 'rv_t-32', 'rv_t-31', 'rv_t-30', 'rv_t-29', 'rv_t-28', 'rv_t-27', 'rv_t-26', 'rv_t-25', 'rv_t-24', 'rv_t-23', 'rv_t-22', 'rv_t-21', 'rv_t-20', 'rv_t-19', 'rv_t-18', 'rv_t-17', 'rv_t-16', 'rv_t-15', 'rv_t-14', 'rv_t-13', 'rv_t-12', 'rv_t-11', 'rv_t-10', 'rv_t-9', 'rv_t-8', 'rv_t-7', 'rv_t-6', 'rv_t-5', 'rv_t-4', 'rv_t-3', 'rv_t-2', 'rv_t-1', 'rv_t-0'] ['rv_t+1', 'rv_t+2', 'rv_t+3', 'rv_t+4', 'rv_t+5', 'rv_t+6', 'rv_t+7', 'rv_t+8', 'rv_t+9', 'rv_t+10', 'rv_t+11', 'rv_t+12', 'rv_t+13', 'rv_t+14', 'rv_t+15', 'rv_t+16', 'rv_t+17', 'rv_t+18', 'rv_t+19', 'rv_t+20', 'rv_t+21', 'rv_t+22', 'rv_t+23', 'rv_t+24', 'rv_t+25', 'rv_t+26', 'rv_t+27', 'rv_t+28', 'rv_t+29', 'rv_t+30', 'rv_t+31', 'rv_t+32', 'rv_t+33', 'rv_t+34', 'rv_t+35', 'rv_t+36', 'rv_t+37', 'rv_t+38', 'rv_t+39', 'rv_t+40', 'rv_t+41', 'rv_t+42', 'rv_t+43', 'rv_t+44', 'rv_t+45', 'rv_t+46', 'rv_t+47', 'rv_t+48', 'rv_t+49', 'rv_t+50', 'rv_t+51', 'rv_t+52', 'rv_t+53', 'rv_t+54', 'rv_t+55', 'rv_t+56', 'rv_t+57', 'rv_t+58', 'rv_t+59', 'rv_t+60']
Sixt type of data¶
In [11]:
load_data_object_6_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_6.pkl")
with open(load_data_object_6_file_path, "rb") as f:
structured_data_dict_6 = pickle.load(f)
print("Data dictionary 6 loaded successfully.")
eur6_X_price = structured_data_dict_6["EURUSD"].get("X_other")
eur6_X_time = structured_data_dict_6["EURUSD"].get("X_time")
eur6_y = structured_data_dict_6["EURUSD"].get("y")
eur6_y_columns = structured_data_dict_6["EURUSD"].get("y_columns")
eur6_X_columns = structured_data_dict_6["EURUSD"].get("X_other_columns")
print(eur6_X_columns)
print(eur6_y_columns)
print(eur6_X_price[0:5, 0, 1:3])
Data dictionary 6 loaded successfully. ['rv_t-59', 'x_best_alpha_1_t-59', 'x_best_beta_1_t-59', 'x_best_omega_t-59', 'x_best_mu_t-59', 'x_best_nu_tgarch_t-59', 'rv_t-58', 'x_best_alpha_1_t-58', 'x_best_beta_1_t-58', 'x_best_omega_t-58', 'x_best_mu_t-58', 'x_best_nu_tgarch_t-58', 'rv_t-57', 'x_best_alpha_1_t-57', 'x_best_beta_1_t-57', 'x_best_omega_t-57', 'x_best_mu_t-57', 'x_best_nu_tgarch_t-57', 'rv_t-56', 'x_best_alpha_1_t-56', 'x_best_beta_1_t-56', 'x_best_omega_t-56', 'x_best_mu_t-56', 'x_best_nu_tgarch_t-56', 'rv_t-55', 'x_best_alpha_1_t-55', 'x_best_beta_1_t-55', 'x_best_omega_t-55', 'x_best_mu_t-55', 'x_best_nu_tgarch_t-55', 'rv_t-54', 'x_best_alpha_1_t-54', 'x_best_beta_1_t-54', 'x_best_omega_t-54', 'x_best_mu_t-54', 'x_best_nu_tgarch_t-54', 'rv_t-53', 'x_best_alpha_1_t-53', 'x_best_beta_1_t-53', 'x_best_omega_t-53', 'x_best_mu_t-53', 'x_best_nu_tgarch_t-53', 'rv_t-52', 'x_best_alpha_1_t-52', 'x_best_beta_1_t-52', 'x_best_omega_t-52', 'x_best_mu_t-52', 'x_best_nu_tgarch_t-52', 'rv_t-51', 'x_best_alpha_1_t-51', 'x_best_beta_1_t-51', 'x_best_omega_t-51', 'x_best_mu_t-51', 'x_best_nu_tgarch_t-51', 'rv_t-50', 'x_best_alpha_1_t-50', 'x_best_beta_1_t-50', 'x_best_omega_t-50', 'x_best_mu_t-50', 'x_best_nu_tgarch_t-50', 'rv_t-49', 'x_best_alpha_1_t-49', 'x_best_beta_1_t-49', 'x_best_omega_t-49', 'x_best_mu_t-49', 'x_best_nu_tgarch_t-49', 'rv_t-48', 'x_best_alpha_1_t-48', 'x_best_beta_1_t-48', 'x_best_omega_t-48', 'x_best_mu_t-48', 'x_best_nu_tgarch_t-48', 'rv_t-47', 'x_best_alpha_1_t-47', 'x_best_beta_1_t-47', 'x_best_omega_t-47', 'x_best_mu_t-47', 'x_best_nu_tgarch_t-47', 'rv_t-46', 'x_best_alpha_1_t-46', 'x_best_beta_1_t-46', 'x_best_omega_t-46', 'x_best_mu_t-46', 'x_best_nu_tgarch_t-46', 'rv_t-45', 'x_best_alpha_1_t-45', 'x_best_beta_1_t-45', 'x_best_omega_t-45', 'x_best_mu_t-45', 'x_best_nu_tgarch_t-45', 'rv_t-44', 'x_best_alpha_1_t-44', 'x_best_beta_1_t-44', 'x_best_omega_t-44', 'x_best_mu_t-44', 'x_best_nu_tgarch_t-44', 'rv_t-43', 'x_best_alpha_1_t-43', 'x_best_beta_1_t-43', 'x_best_omega_t-43', 'x_best_mu_t-43', 'x_best_nu_tgarch_t-43', 'rv_t-42', 'x_best_alpha_1_t-42', 'x_best_beta_1_t-42', 'x_best_omega_t-42', 'x_best_mu_t-42', 'x_best_nu_tgarch_t-42', 'rv_t-41', 'x_best_alpha_1_t-41', 'x_best_beta_1_t-41', 'x_best_omega_t-41', 'x_best_mu_t-41', 'x_best_nu_tgarch_t-41', 'rv_t-40', 'x_best_alpha_1_t-40', 'x_best_beta_1_t-40', 'x_best_omega_t-40', 'x_best_mu_t-40', 'x_best_nu_tgarch_t-40', 'rv_t-39', 'x_best_alpha_1_t-39', 'x_best_beta_1_t-39', 'x_best_omega_t-39', 'x_best_mu_t-39', 'x_best_nu_tgarch_t-39', 'rv_t-38', 'x_best_alpha_1_t-38', 'x_best_beta_1_t-38', 'x_best_omega_t-38', 'x_best_mu_t-38', 'x_best_nu_tgarch_t-38', 'rv_t-37', 'x_best_alpha_1_t-37', 'x_best_beta_1_t-37', 'x_best_omega_t-37', 'x_best_mu_t-37', 'x_best_nu_tgarch_t-37', 'rv_t-36', 'x_best_alpha_1_t-36', 'x_best_beta_1_t-36', 'x_best_omega_t-36', 'x_best_mu_t-36', 'x_best_nu_tgarch_t-36', 'rv_t-35', 'x_best_alpha_1_t-35', 'x_best_beta_1_t-35', 'x_best_omega_t-35', 'x_best_mu_t-35', 'x_best_nu_tgarch_t-35', 'rv_t-34', 'x_best_alpha_1_t-34', 'x_best_beta_1_t-34', 'x_best_omega_t-34', 'x_best_mu_t-34', 'x_best_nu_tgarch_t-34', 'rv_t-33', 'x_best_alpha_1_t-33', 'x_best_beta_1_t-33', 'x_best_omega_t-33', 'x_best_mu_t-33', 'x_best_nu_tgarch_t-33', 'rv_t-32', 'x_best_alpha_1_t-32', 'x_best_beta_1_t-32', 'x_best_omega_t-32', 'x_best_mu_t-32', 'x_best_nu_tgarch_t-32', 'rv_t-31', 'x_best_alpha_1_t-31', 'x_best_beta_1_t-31', 'x_best_omega_t-31', 'x_best_mu_t-31', 'x_best_nu_tgarch_t-31', 'rv_t-30', 'x_best_alpha_1_t-30', 'x_best_beta_1_t-30', 'x_best_omega_t-30', 'x_best_mu_t-30', 'x_best_nu_tgarch_t-30', 'rv_t-29', 'x_best_alpha_1_t-29', 'x_best_beta_1_t-29', 'x_best_omega_t-29', 'x_best_mu_t-29', 'x_best_nu_tgarch_t-29', 'rv_t-28', 'x_best_alpha_1_t-28', 'x_best_beta_1_t-28', 'x_best_omega_t-28', 'x_best_mu_t-28', 'x_best_nu_tgarch_t-28', 'rv_t-27', 'x_best_alpha_1_t-27', 'x_best_beta_1_t-27', 'x_best_omega_t-27', 'x_best_mu_t-27', 'x_best_nu_tgarch_t-27', 'rv_t-26', 'x_best_alpha_1_t-26', 'x_best_beta_1_t-26', 'x_best_omega_t-26', 'x_best_mu_t-26', 'x_best_nu_tgarch_t-26', 'rv_t-25', 'x_best_alpha_1_t-25', 'x_best_beta_1_t-25', 'x_best_omega_t-25', 'x_best_mu_t-25', 'x_best_nu_tgarch_t-25', 'rv_t-24', 'x_best_alpha_1_t-24', 'x_best_beta_1_t-24', 'x_best_omega_t-24', 'x_best_mu_t-24', 'x_best_nu_tgarch_t-24', 'rv_t-23', 'x_best_alpha_1_t-23', 'x_best_beta_1_t-23', 'x_best_omega_t-23', 'x_best_mu_t-23', 'x_best_nu_tgarch_t-23', 'rv_t-22', 'x_best_alpha_1_t-22', 'x_best_beta_1_t-22', 'x_best_omega_t-22', 'x_best_mu_t-22', 'x_best_nu_tgarch_t-22', 'rv_t-21', 'x_best_alpha_1_t-21', 'x_best_beta_1_t-21', 'x_best_omega_t-21', 'x_best_mu_t-21', 'x_best_nu_tgarch_t-21', 'rv_t-20', 'x_best_alpha_1_t-20', 'x_best_beta_1_t-20', 'x_best_omega_t-20', 'x_best_mu_t-20', 'x_best_nu_tgarch_t-20', 'rv_t-19', 'x_best_alpha_1_t-19', 'x_best_beta_1_t-19', 'x_best_omega_t-19', 'x_best_mu_t-19', 'x_best_nu_tgarch_t-19', 'rv_t-18', 'x_best_alpha_1_t-18', 'x_best_beta_1_t-18', 'x_best_omega_t-18', 'x_best_mu_t-18', 'x_best_nu_tgarch_t-18', 'rv_t-17', 'x_best_alpha_1_t-17', 'x_best_beta_1_t-17', 'x_best_omega_t-17', 'x_best_mu_t-17', 'x_best_nu_tgarch_t-17', 'rv_t-16', 'x_best_alpha_1_t-16', 'x_best_beta_1_t-16', 'x_best_omega_t-16', 'x_best_mu_t-16', 'x_best_nu_tgarch_t-16', 'rv_t-15', 'x_best_alpha_1_t-15', 'x_best_beta_1_t-15', 'x_best_omega_t-15', 'x_best_mu_t-15', 'x_best_nu_tgarch_t-15', 'rv_t-14', 'x_best_alpha_1_t-14', 'x_best_beta_1_t-14', 'x_best_omega_t-14', 'x_best_mu_t-14', 'x_best_nu_tgarch_t-14', 'rv_t-13', 'x_best_alpha_1_t-13', 'x_best_beta_1_t-13', 'x_best_omega_t-13', 'x_best_mu_t-13', 'x_best_nu_tgarch_t-13', 'rv_t-12', 'x_best_alpha_1_t-12', 'x_best_beta_1_t-12', 'x_best_omega_t-12', 'x_best_mu_t-12', 'x_best_nu_tgarch_t-12', 'rv_t-11', 'x_best_alpha_1_t-11', 'x_best_beta_1_t-11', 'x_best_omega_t-11', 'x_best_mu_t-11', 'x_best_nu_tgarch_t-11', 'rv_t-10', 'x_best_alpha_1_t-10', 'x_best_beta_1_t-10', 'x_best_omega_t-10', 'x_best_mu_t-10', 'x_best_nu_tgarch_t-10', 'rv_t-9', 'x_best_alpha_1_t-9', 'x_best_beta_1_t-9', 'x_best_omega_t-9', 'x_best_mu_t-9', 'x_best_nu_tgarch_t-9', 'rv_t-8', 'x_best_alpha_1_t-8', 'x_best_beta_1_t-8', 'x_best_omega_t-8', 'x_best_mu_t-8', 'x_best_nu_tgarch_t-8', 'rv_t-7', 'x_best_alpha_1_t-7', 'x_best_beta_1_t-7', 'x_best_omega_t-7', 'x_best_mu_t-7', 'x_best_nu_tgarch_t-7', 'rv_t-6', 'x_best_alpha_1_t-6', 'x_best_beta_1_t-6', 'x_best_omega_t-6', 'x_best_mu_t-6', 'x_best_nu_tgarch_t-6', 'rv_t-5', 'x_best_alpha_1_t-5', 'x_best_beta_1_t-5', 'x_best_omega_t-5', 'x_best_mu_t-5', 'x_best_nu_tgarch_t-5', 'rv_t-4', 'x_best_alpha_1_t-4', 'x_best_beta_1_t-4', 'x_best_omega_t-4', 'x_best_mu_t-4', 'x_best_nu_tgarch_t-4', 'rv_t-3', 'x_best_alpha_1_t-3', 'x_best_beta_1_t-3', 'x_best_omega_t-3', 'x_best_mu_t-3', 'x_best_nu_tgarch_t-3', 'rv_t-2', 'x_best_alpha_1_t-2', 'x_best_beta_1_t-2', 'x_best_omega_t-2', 'x_best_mu_t-2', 'x_best_nu_tgarch_t-2', 'rv_t-1', 'x_best_alpha_1_t-1', 'x_best_beta_1_t-1', 'x_best_omega_t-1', 'x_best_mu_t-1', 'x_best_nu_tgarch_t-1', 'rv_t-0', 'x_best_alpha_1_t-0', 'x_best_beta_1_t-0', 'x_best_omega_t-0', 'x_best_mu_t-0', 'x_best_nu_tgarch_t-0'] ['rv_t+1', 'y_best_alpha_1_t+1', 'y_best_beta_1_t+1', 'y_best_omega_t+1', 'y_best_mu_t+1', 'y_best_nu_tgarch_t+1', 'rv_t+2', 'y_best_alpha_1_t+2', 'y_best_beta_1_t+2', 'y_best_omega_t+2', 'y_best_mu_t+2', 'y_best_nu_tgarch_t+2', 'rv_t+3', 'y_best_alpha_1_t+3', 'y_best_beta_1_t+3', 'y_best_omega_t+3', 'y_best_mu_t+3', 'y_best_nu_tgarch_t+3', 'rv_t+4', 'y_best_alpha_1_t+4', 'y_best_beta_1_t+4', 'y_best_omega_t+4', 'y_best_mu_t+4', 'y_best_nu_tgarch_t+4', 'rv_t+5', 'y_best_alpha_1_t+5', 'y_best_beta_1_t+5', 'y_best_omega_t+5', 'y_best_mu_t+5', 'y_best_nu_tgarch_t+5', 'rv_t+6', 'y_best_alpha_1_t+6', 'y_best_beta_1_t+6', 'y_best_omega_t+6', 'y_best_mu_t+6', 'y_best_nu_tgarch_t+6', 'rv_t+7', 'y_best_alpha_1_t+7', 'y_best_beta_1_t+7', 'y_best_omega_t+7', 'y_best_mu_t+7', 'y_best_nu_tgarch_t+7', 'rv_t+8', 'y_best_alpha_1_t+8', 'y_best_beta_1_t+8', 'y_best_omega_t+8', 'y_best_mu_t+8', 'y_best_nu_tgarch_t+8', 'rv_t+9', 'y_best_alpha_1_t+9', 'y_best_beta_1_t+9', 'y_best_omega_t+9', 'y_best_mu_t+9', 'y_best_nu_tgarch_t+9', 'rv_t+10', 'y_best_alpha_1_t+10', 'y_best_beta_1_t+10', 'y_best_omega_t+10', 'y_best_mu_t+10', 'y_best_nu_tgarch_t+10', 'rv_t+11', 'y_best_alpha_1_t+11', 'y_best_beta_1_t+11', 'y_best_omega_t+11', 'y_best_mu_t+11', 'y_best_nu_tgarch_t+11', 'rv_t+12', 'y_best_alpha_1_t+12', 'y_best_beta_1_t+12', 'y_best_omega_t+12', 'y_best_mu_t+12', 'y_best_nu_tgarch_t+12', 'rv_t+13', 'y_best_alpha_1_t+13', 'y_best_beta_1_t+13', 'y_best_omega_t+13', 'y_best_mu_t+13', 'y_best_nu_tgarch_t+13', 'rv_t+14', 'y_best_alpha_1_t+14', 'y_best_beta_1_t+14', 'y_best_omega_t+14', 'y_best_mu_t+14', 'y_best_nu_tgarch_t+14', 'rv_t+15', 'y_best_alpha_1_t+15', 'y_best_beta_1_t+15', 'y_best_omega_t+15', 'y_best_mu_t+15', 'y_best_nu_tgarch_t+15', 'rv_t+16', 'y_best_alpha_1_t+16', 'y_best_beta_1_t+16', 'y_best_omega_t+16', 'y_best_mu_t+16', 'y_best_nu_tgarch_t+16', 'rv_t+17', 'y_best_alpha_1_t+17', 'y_best_beta_1_t+17', 'y_best_omega_t+17', 'y_best_mu_t+17', 'y_best_nu_tgarch_t+17', 'rv_t+18', 'y_best_alpha_1_t+18', 'y_best_beta_1_t+18', 'y_best_omega_t+18', 'y_best_mu_t+18', 'y_best_nu_tgarch_t+18', 'rv_t+19', 'y_best_alpha_1_t+19', 'y_best_beta_1_t+19', 'y_best_omega_t+19', 'y_best_mu_t+19', 'y_best_nu_tgarch_t+19', 'rv_t+20', 'y_best_alpha_1_t+20', 'y_best_beta_1_t+20', 'y_best_omega_t+20', 'y_best_mu_t+20', 'y_best_nu_tgarch_t+20', 'rv_t+21', 'y_best_alpha_1_t+21', 'y_best_beta_1_t+21', 'y_best_omega_t+21', 'y_best_mu_t+21', 'y_best_nu_tgarch_t+21', 'rv_t+22', 'y_best_alpha_1_t+22', 'y_best_beta_1_t+22', 'y_best_omega_t+22', 'y_best_mu_t+22', 'y_best_nu_tgarch_t+22', 'rv_t+23', 'y_best_alpha_1_t+23', 'y_best_beta_1_t+23', 'y_best_omega_t+23', 'y_best_mu_t+23', 'y_best_nu_tgarch_t+23', 'rv_t+24', 'y_best_alpha_1_t+24', 'y_best_beta_1_t+24', 'y_best_omega_t+24', 'y_best_mu_t+24', 'y_best_nu_tgarch_t+24', 'rv_t+25', 'y_best_alpha_1_t+25', 'y_best_beta_1_t+25', 'y_best_omega_t+25', 'y_best_mu_t+25', 'y_best_nu_tgarch_t+25', 'rv_t+26', 'y_best_alpha_1_t+26', 'y_best_beta_1_t+26', 'y_best_omega_t+26', 'y_best_mu_t+26', 'y_best_nu_tgarch_t+26', 'rv_t+27', 'y_best_alpha_1_t+27', 'y_best_beta_1_t+27', 'y_best_omega_t+27', 'y_best_mu_t+27', 'y_best_nu_tgarch_t+27', 'rv_t+28', 'y_best_alpha_1_t+28', 'y_best_beta_1_t+28', 'y_best_omega_t+28', 'y_best_mu_t+28', 'y_best_nu_tgarch_t+28', 'rv_t+29', 'y_best_alpha_1_t+29', 'y_best_beta_1_t+29', 'y_best_omega_t+29', 'y_best_mu_t+29', 'y_best_nu_tgarch_t+29', 'rv_t+30', 'y_best_alpha_1_t+30', 'y_best_beta_1_t+30', 'y_best_omega_t+30', 'y_best_mu_t+30', 'y_best_nu_tgarch_t+30', 'rv_t+31', 'y_best_alpha_1_t+31', 'y_best_beta_1_t+31', 'y_best_omega_t+31', 'y_best_mu_t+31', 'y_best_nu_tgarch_t+31', 'rv_t+32', 'y_best_alpha_1_t+32', 'y_best_beta_1_t+32', 'y_best_omega_t+32', 'y_best_mu_t+32', 'y_best_nu_tgarch_t+32', 'rv_t+33', 'y_best_alpha_1_t+33', 'y_best_beta_1_t+33', 'y_best_omega_t+33', 'y_best_mu_t+33', 'y_best_nu_tgarch_t+33', 'rv_t+34', 'y_best_alpha_1_t+34', 'y_best_beta_1_t+34', 'y_best_omega_t+34', 'y_best_mu_t+34', 'y_best_nu_tgarch_t+34', 'rv_t+35', 'y_best_alpha_1_t+35', 'y_best_beta_1_t+35', 'y_best_omega_t+35', 'y_best_mu_t+35', 'y_best_nu_tgarch_t+35', 'rv_t+36', 'y_best_alpha_1_t+36', 'y_best_beta_1_t+36', 'y_best_omega_t+36', 'y_best_mu_t+36', 'y_best_nu_tgarch_t+36', 'rv_t+37', 'y_best_alpha_1_t+37', 'y_best_beta_1_t+37', 'y_best_omega_t+37', 'y_best_mu_t+37', 'y_best_nu_tgarch_t+37', 'rv_t+38', 'y_best_alpha_1_t+38', 'y_best_beta_1_t+38', 'y_best_omega_t+38', 'y_best_mu_t+38', 'y_best_nu_tgarch_t+38', 'rv_t+39', 'y_best_alpha_1_t+39', 'y_best_beta_1_t+39', 'y_best_omega_t+39', 'y_best_mu_t+39', 'y_best_nu_tgarch_t+39', 'rv_t+40', 'y_best_alpha_1_t+40', 'y_best_beta_1_t+40', 'y_best_omega_t+40', 'y_best_mu_t+40', 'y_best_nu_tgarch_t+40', 'rv_t+41', 'y_best_alpha_1_t+41', 'y_best_beta_1_t+41', 'y_best_omega_t+41', 'y_best_mu_t+41', 'y_best_nu_tgarch_t+41', 'rv_t+42', 'y_best_alpha_1_t+42', 'y_best_beta_1_t+42', 'y_best_omega_t+42', 'y_best_mu_t+42', 'y_best_nu_tgarch_t+42', 'rv_t+43', 'y_best_alpha_1_t+43', 'y_best_beta_1_t+43', 'y_best_omega_t+43', 'y_best_mu_t+43', 'y_best_nu_tgarch_t+43', 'rv_t+44', 'y_best_alpha_1_t+44', 'y_best_beta_1_t+44', 'y_best_omega_t+44', 'y_best_mu_t+44', 'y_best_nu_tgarch_t+44', 'rv_t+45', 'y_best_alpha_1_t+45', 'y_best_beta_1_t+45', 'y_best_omega_t+45', 'y_best_mu_t+45', 'y_best_nu_tgarch_t+45', 'rv_t+46', 'y_best_alpha_1_t+46', 'y_best_beta_1_t+46', 'y_best_omega_t+46', 'y_best_mu_t+46', 'y_best_nu_tgarch_t+46', 'rv_t+47', 'y_best_alpha_1_t+47', 'y_best_beta_1_t+47', 'y_best_omega_t+47', 'y_best_mu_t+47', 'y_best_nu_tgarch_t+47', 'rv_t+48', 'y_best_alpha_1_t+48', 'y_best_beta_1_t+48', 'y_best_omega_t+48', 'y_best_mu_t+48', 'y_best_nu_tgarch_t+48', 'rv_t+49', 'y_best_alpha_1_t+49', 'y_best_beta_1_t+49', 'y_best_omega_t+49', 'y_best_mu_t+49', 'y_best_nu_tgarch_t+49', 'rv_t+50', 'y_best_alpha_1_t+50', 'y_best_beta_1_t+50', 'y_best_omega_t+50', 'y_best_mu_t+50', 'y_best_nu_tgarch_t+50', 'rv_t+51', 'y_best_alpha_1_t+51', 'y_best_beta_1_t+51', 'y_best_omega_t+51', 'y_best_mu_t+51', 'y_best_nu_tgarch_t+51', 'rv_t+52', 'y_best_alpha_1_t+52', 'y_best_beta_1_t+52', 'y_best_omega_t+52', 'y_best_mu_t+52', 'y_best_nu_tgarch_t+52', 'rv_t+53', 'y_best_alpha_1_t+53', 'y_best_beta_1_t+53', 'y_best_omega_t+53', 'y_best_mu_t+53', 'y_best_nu_tgarch_t+53', 'rv_t+54', 'y_best_alpha_1_t+54', 'y_best_beta_1_t+54', 'y_best_omega_t+54', 'y_best_mu_t+54', 'y_best_nu_tgarch_t+54', 'rv_t+55', 'y_best_alpha_1_t+55', 'y_best_beta_1_t+55', 'y_best_omega_t+55', 'y_best_mu_t+55', 'y_best_nu_tgarch_t+55', 'rv_t+56', 'y_best_alpha_1_t+56', 'y_best_beta_1_t+56', 'y_best_omega_t+56', 'y_best_mu_t+56', 'y_best_nu_tgarch_t+56', 'rv_t+57', 'y_best_alpha_1_t+57', 'y_best_beta_1_t+57', 'y_best_omega_t+57', 'y_best_mu_t+57', 'y_best_nu_tgarch_t+57', 'rv_t+58', 'y_best_alpha_1_t+58', 'y_best_beta_1_t+58', 'y_best_omega_t+58', 'y_best_mu_t+58', 'y_best_nu_tgarch_t+58', 'rv_t+59', 'y_best_alpha_1_t+59', 'y_best_beta_1_t+59', 'y_best_omega_t+59', 'y_best_mu_t+59', 'y_best_nu_tgarch_t+59', 'rv_t+60', 'y_best_alpha_1_t+60', 'y_best_beta_1_t+60', 'y_best_omega_t+60', 'y_best_mu_t+60', 'y_best_nu_tgarch_t+60'] [[3.31688180e-11 9.97802572e-01] [2.13094142e-02 9.63861495e-01] [1.84962299e-02 9.70108134e-01] [3.04809820e-17 9.98244968e-01] [8.17518273e-15 9.98014310e-01]]
Load data v7¶
In [12]:
load_data_object_7_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_7.pkl")
with open(load_data_object_7_file_path, "rb") as f:
structured_data_dict_7 = pickle.load(f)
print("Data dictionary 7 loaded successfully.")
eur7_X_price = structured_data_dict_7["EURUSD"].get("X_other")
eur7_X_time = structured_data_dict_7["EURUSD"].get("X_time")
eur7_y = structured_data_dict_7["EURUSD"].get("y")
eur7_y_columns = structured_data_dict_7["EURUSD"].get("y_columns")
eur7_X_columns = structured_data_dict_7["EURUSD"].get("X_other_columns")
print(eur7_X_columns)
print(eur7_y_columns)
df_xprice = summarize_features(eur7_X_price, eur7_X_columns, name="X_price")
df_y = summarize_features(eur7_y, eur7_y_columns, name="y")
print(df_xprice.head(20))
print(df_y.head(20))
Data dictionary 7 loaded successfully.
['rv_t-59', 'x_best_alpha_1_t-59', 'x_best_beta_1_t-59', 'x_best_omega_t-59', 'x_best_mu_t-59', 'x_best_nu_tgarch_t-59', 'rv_t-58', 'x_best_alpha_1_t-58', 'x_best_beta_1_t-58', 'x_best_omega_t-58', 'x_best_mu_t-58', 'x_best_nu_tgarch_t-58', 'rv_t-57', 'x_best_alpha_1_t-57', 'x_best_beta_1_t-57', 'x_best_omega_t-57', 'x_best_mu_t-57', 'x_best_nu_tgarch_t-57', 'rv_t-56', 'x_best_alpha_1_t-56', 'x_best_beta_1_t-56', 'x_best_omega_t-56', 'x_best_mu_t-56', 'x_best_nu_tgarch_t-56', 'rv_t-55', 'x_best_alpha_1_t-55', 'x_best_beta_1_t-55', 'x_best_omega_t-55', 'x_best_mu_t-55', 'x_best_nu_tgarch_t-55', 'rv_t-54', 'x_best_alpha_1_t-54', 'x_best_beta_1_t-54', 'x_best_omega_t-54', 'x_best_mu_t-54', 'x_best_nu_tgarch_t-54', 'rv_t-53', 'x_best_alpha_1_t-53', 'x_best_beta_1_t-53', 'x_best_omega_t-53', 'x_best_mu_t-53', 'x_best_nu_tgarch_t-53', 'rv_t-52', 'x_best_alpha_1_t-52', 'x_best_beta_1_t-52', 'x_best_omega_t-52', 'x_best_mu_t-52', 'x_best_nu_tgarch_t-52', 'rv_t-51', 'x_best_alpha_1_t-51', 'x_best_beta_1_t-51', 'x_best_omega_t-51', 'x_best_mu_t-51', 'x_best_nu_tgarch_t-51', 'rv_t-50', 'x_best_alpha_1_t-50', 'x_best_beta_1_t-50', 'x_best_omega_t-50', 'x_best_mu_t-50', 'x_best_nu_tgarch_t-50', 'rv_t-49', 'x_best_alpha_1_t-49', 'x_best_beta_1_t-49', 'x_best_omega_t-49', 'x_best_mu_t-49', 'x_best_nu_tgarch_t-49', 'rv_t-48', 'x_best_alpha_1_t-48', 'x_best_beta_1_t-48', 'x_best_omega_t-48', 'x_best_mu_t-48', 'x_best_nu_tgarch_t-48', 'rv_t-47', 'x_best_alpha_1_t-47', 'x_best_beta_1_t-47', 'x_best_omega_t-47', 'x_best_mu_t-47', 'x_best_nu_tgarch_t-47', 'rv_t-46', 'x_best_alpha_1_t-46', 'x_best_beta_1_t-46', 'x_best_omega_t-46', 'x_best_mu_t-46', 'x_best_nu_tgarch_t-46', 'rv_t-45', 'x_best_alpha_1_t-45', 'x_best_beta_1_t-45', 'x_best_omega_t-45', 'x_best_mu_t-45', 'x_best_nu_tgarch_t-45', 'rv_t-44', 'x_best_alpha_1_t-44', 'x_best_beta_1_t-44', 'x_best_omega_t-44', 'x_best_mu_t-44', 'x_best_nu_tgarch_t-44', 'rv_t-43', 'x_best_alpha_1_t-43', 'x_best_beta_1_t-43', 'x_best_omega_t-43', 'x_best_mu_t-43', 'x_best_nu_tgarch_t-43', 'rv_t-42', 'x_best_alpha_1_t-42', 'x_best_beta_1_t-42', 'x_best_omega_t-42', 'x_best_mu_t-42', 'x_best_nu_tgarch_t-42', 'rv_t-41', 'x_best_alpha_1_t-41', 'x_best_beta_1_t-41', 'x_best_omega_t-41', 'x_best_mu_t-41', 'x_best_nu_tgarch_t-41', 'rv_t-40', 'x_best_alpha_1_t-40', 'x_best_beta_1_t-40', 'x_best_omega_t-40', 'x_best_mu_t-40', 'x_best_nu_tgarch_t-40', 'rv_t-39', 'x_best_alpha_1_t-39', 'x_best_beta_1_t-39', 'x_best_omega_t-39', 'x_best_mu_t-39', 'x_best_nu_tgarch_t-39', 'rv_t-38', 'x_best_alpha_1_t-38', 'x_best_beta_1_t-38', 'x_best_omega_t-38', 'x_best_mu_t-38', 'x_best_nu_tgarch_t-38', 'rv_t-37', 'x_best_alpha_1_t-37', 'x_best_beta_1_t-37', 'x_best_omega_t-37', 'x_best_mu_t-37', 'x_best_nu_tgarch_t-37', 'rv_t-36', 'x_best_alpha_1_t-36', 'x_best_beta_1_t-36', 'x_best_omega_t-36', 'x_best_mu_t-36', 'x_best_nu_tgarch_t-36', 'rv_t-35', 'x_best_alpha_1_t-35', 'x_best_beta_1_t-35', 'x_best_omega_t-35', 'x_best_mu_t-35', 'x_best_nu_tgarch_t-35', 'rv_t-34', 'x_best_alpha_1_t-34', 'x_best_beta_1_t-34', 'x_best_omega_t-34', 'x_best_mu_t-34', 'x_best_nu_tgarch_t-34', 'rv_t-33', 'x_best_alpha_1_t-33', 'x_best_beta_1_t-33', 'x_best_omega_t-33', 'x_best_mu_t-33', 'x_best_nu_tgarch_t-33', 'rv_t-32', 'x_best_alpha_1_t-32', 'x_best_beta_1_t-32', 'x_best_omega_t-32', 'x_best_mu_t-32', 'x_best_nu_tgarch_t-32', 'rv_t-31', 'x_best_alpha_1_t-31', 'x_best_beta_1_t-31', 'x_best_omega_t-31', 'x_best_mu_t-31', 'x_best_nu_tgarch_t-31', 'rv_t-30', 'x_best_alpha_1_t-30', 'x_best_beta_1_t-30', 'x_best_omega_t-30', 'x_best_mu_t-30', 'x_best_nu_tgarch_t-30', 'rv_t-29', 'x_best_alpha_1_t-29', 'x_best_beta_1_t-29', 'x_best_omega_t-29', 'x_best_mu_t-29', 'x_best_nu_tgarch_t-29', 'rv_t-28', 'x_best_alpha_1_t-28', 'x_best_beta_1_t-28', 'x_best_omega_t-28', 'x_best_mu_t-28', 'x_best_nu_tgarch_t-28', 'rv_t-27', 'x_best_alpha_1_t-27', 'x_best_beta_1_t-27', 'x_best_omega_t-27', 'x_best_mu_t-27', 'x_best_nu_tgarch_t-27', 'rv_t-26', 'x_best_alpha_1_t-26', 'x_best_beta_1_t-26', 'x_best_omega_t-26', 'x_best_mu_t-26', 'x_best_nu_tgarch_t-26', 'rv_t-25', 'x_best_alpha_1_t-25', 'x_best_beta_1_t-25', 'x_best_omega_t-25', 'x_best_mu_t-25', 'x_best_nu_tgarch_t-25', 'rv_t-24', 'x_best_alpha_1_t-24', 'x_best_beta_1_t-24', 'x_best_omega_t-24', 'x_best_mu_t-24', 'x_best_nu_tgarch_t-24', 'rv_t-23', 'x_best_alpha_1_t-23', 'x_best_beta_1_t-23', 'x_best_omega_t-23', 'x_best_mu_t-23', 'x_best_nu_tgarch_t-23', 'rv_t-22', 'x_best_alpha_1_t-22', 'x_best_beta_1_t-22', 'x_best_omega_t-22', 'x_best_mu_t-22', 'x_best_nu_tgarch_t-22', 'rv_t-21', 'x_best_alpha_1_t-21', 'x_best_beta_1_t-21', 'x_best_omega_t-21', 'x_best_mu_t-21', 'x_best_nu_tgarch_t-21', 'rv_t-20', 'x_best_alpha_1_t-20', 'x_best_beta_1_t-20', 'x_best_omega_t-20', 'x_best_mu_t-20', 'x_best_nu_tgarch_t-20', 'rv_t-19', 'x_best_alpha_1_t-19', 'x_best_beta_1_t-19', 'x_best_omega_t-19', 'x_best_mu_t-19', 'x_best_nu_tgarch_t-19', 'rv_t-18', 'x_best_alpha_1_t-18', 'x_best_beta_1_t-18', 'x_best_omega_t-18', 'x_best_mu_t-18', 'x_best_nu_tgarch_t-18', 'rv_t-17', 'x_best_alpha_1_t-17', 'x_best_beta_1_t-17', 'x_best_omega_t-17', 'x_best_mu_t-17', 'x_best_nu_tgarch_t-17', 'rv_t-16', 'x_best_alpha_1_t-16', 'x_best_beta_1_t-16', 'x_best_omega_t-16', 'x_best_mu_t-16', 'x_best_nu_tgarch_t-16', 'rv_t-15', 'x_best_alpha_1_t-15', 'x_best_beta_1_t-15', 'x_best_omega_t-15', 'x_best_mu_t-15', 'x_best_nu_tgarch_t-15', 'rv_t-14', 'x_best_alpha_1_t-14', 'x_best_beta_1_t-14', 'x_best_omega_t-14', 'x_best_mu_t-14', 'x_best_nu_tgarch_t-14', 'rv_t-13', 'x_best_alpha_1_t-13', 'x_best_beta_1_t-13', 'x_best_omega_t-13', 'x_best_mu_t-13', 'x_best_nu_tgarch_t-13', 'rv_t-12', 'x_best_alpha_1_t-12', 'x_best_beta_1_t-12', 'x_best_omega_t-12', 'x_best_mu_t-12', 'x_best_nu_tgarch_t-12', 'rv_t-11', 'x_best_alpha_1_t-11', 'x_best_beta_1_t-11', 'x_best_omega_t-11', 'x_best_mu_t-11', 'x_best_nu_tgarch_t-11', 'rv_t-10', 'x_best_alpha_1_t-10', 'x_best_beta_1_t-10', 'x_best_omega_t-10', 'x_best_mu_t-10', 'x_best_nu_tgarch_t-10', 'rv_t-9', 'x_best_alpha_1_t-9', 'x_best_beta_1_t-9', 'x_best_omega_t-9', 'x_best_mu_t-9', 'x_best_nu_tgarch_t-9', 'rv_t-8', 'x_best_alpha_1_t-8', 'x_best_beta_1_t-8', 'x_best_omega_t-8', 'x_best_mu_t-8', 'x_best_nu_tgarch_t-8', 'rv_t-7', 'x_best_alpha_1_t-7', 'x_best_beta_1_t-7', 'x_best_omega_t-7', 'x_best_mu_t-7', 'x_best_nu_tgarch_t-7', 'rv_t-6', 'x_best_alpha_1_t-6', 'x_best_beta_1_t-6', 'x_best_omega_t-6', 'x_best_mu_t-6', 'x_best_nu_tgarch_t-6', 'rv_t-5', 'x_best_alpha_1_t-5', 'x_best_beta_1_t-5', 'x_best_omega_t-5', 'x_best_mu_t-5', 'x_best_nu_tgarch_t-5', 'rv_t-4', 'x_best_alpha_1_t-4', 'x_best_beta_1_t-4', 'x_best_omega_t-4', 'x_best_mu_t-4', 'x_best_nu_tgarch_t-4', 'rv_t-3', 'x_best_alpha_1_t-3', 'x_best_beta_1_t-3', 'x_best_omega_t-3', 'x_best_mu_t-3', 'x_best_nu_tgarch_t-3', 'rv_t-2', 'x_best_alpha_1_t-2', 'x_best_beta_1_t-2', 'x_best_omega_t-2', 'x_best_mu_t-2', 'x_best_nu_tgarch_t-2', 'rv_t-1', 'x_best_alpha_1_t-1', 'x_best_beta_1_t-1', 'x_best_omega_t-1', 'x_best_mu_t-1', 'x_best_nu_tgarch_t-1', 'rv_t-0', 'x_best_alpha_1_t-0', 'x_best_beta_1_t-0', 'x_best_omega_t-0', 'x_best_mu_t-0', 'x_best_nu_tgarch_t-0']
['rv_t+1', 'y_best_alpha_1_t+1', 'y_best_beta_1_t+1', 'y_best_omega_t+1', 'y_best_mu_t+1', 'y_best_nu_tgarch_t+1', 'rv_t+2', 'y_best_alpha_1_t+2', 'y_best_beta_1_t+2', 'y_best_omega_t+2', 'y_best_mu_t+2', 'y_best_nu_tgarch_t+2', 'rv_t+3', 'y_best_alpha_1_t+3', 'y_best_beta_1_t+3', 'y_best_omega_t+3', 'y_best_mu_t+3', 'y_best_nu_tgarch_t+3', 'rv_t+4', 'y_best_alpha_1_t+4', 'y_best_beta_1_t+4', 'y_best_omega_t+4', 'y_best_mu_t+4', 'y_best_nu_tgarch_t+4', 'rv_t+5', 'y_best_alpha_1_t+5', 'y_best_beta_1_t+5', 'y_best_omega_t+5', 'y_best_mu_t+5', 'y_best_nu_tgarch_t+5', 'rv_t+6', 'y_best_alpha_1_t+6', 'y_best_beta_1_t+6', 'y_best_omega_t+6', 'y_best_mu_t+6', 'y_best_nu_tgarch_t+6', 'rv_t+7', 'y_best_alpha_1_t+7', 'y_best_beta_1_t+7', 'y_best_omega_t+7', 'y_best_mu_t+7', 'y_best_nu_tgarch_t+7', 'rv_t+8', 'y_best_alpha_1_t+8', 'y_best_beta_1_t+8', 'y_best_omega_t+8', 'y_best_mu_t+8', 'y_best_nu_tgarch_t+8', 'rv_t+9', 'y_best_alpha_1_t+9', 'y_best_beta_1_t+9', 'y_best_omega_t+9', 'y_best_mu_t+9', 'y_best_nu_tgarch_t+9', 'rv_t+10', 'y_best_alpha_1_t+10', 'y_best_beta_1_t+10', 'y_best_omega_t+10', 'y_best_mu_t+10', 'y_best_nu_tgarch_t+10', 'rv_t+11', 'y_best_alpha_1_t+11', 'y_best_beta_1_t+11', 'y_best_omega_t+11', 'y_best_mu_t+11', 'y_best_nu_tgarch_t+11', 'rv_t+12', 'y_best_alpha_1_t+12', 'y_best_beta_1_t+12', 'y_best_omega_t+12', 'y_best_mu_t+12', 'y_best_nu_tgarch_t+12', 'rv_t+13', 'y_best_alpha_1_t+13', 'y_best_beta_1_t+13', 'y_best_omega_t+13', 'y_best_mu_t+13', 'y_best_nu_tgarch_t+13', 'rv_t+14', 'y_best_alpha_1_t+14', 'y_best_beta_1_t+14', 'y_best_omega_t+14', 'y_best_mu_t+14', 'y_best_nu_tgarch_t+14', 'rv_t+15', 'y_best_alpha_1_t+15', 'y_best_beta_1_t+15', 'y_best_omega_t+15', 'y_best_mu_t+15', 'y_best_nu_tgarch_t+15', 'rv_t+16', 'y_best_alpha_1_t+16', 'y_best_beta_1_t+16', 'y_best_omega_t+16', 'y_best_mu_t+16', 'y_best_nu_tgarch_t+16', 'rv_t+17', 'y_best_alpha_1_t+17', 'y_best_beta_1_t+17', 'y_best_omega_t+17', 'y_best_mu_t+17', 'y_best_nu_tgarch_t+17', 'rv_t+18', 'y_best_alpha_1_t+18', 'y_best_beta_1_t+18', 'y_best_omega_t+18', 'y_best_mu_t+18', 'y_best_nu_tgarch_t+18', 'rv_t+19', 'y_best_alpha_1_t+19', 'y_best_beta_1_t+19', 'y_best_omega_t+19', 'y_best_mu_t+19', 'y_best_nu_tgarch_t+19', 'rv_t+20', 'y_best_alpha_1_t+20', 'y_best_beta_1_t+20', 'y_best_omega_t+20', 'y_best_mu_t+20', 'y_best_nu_tgarch_t+20', 'rv_t+21', 'y_best_alpha_1_t+21', 'y_best_beta_1_t+21', 'y_best_omega_t+21', 'y_best_mu_t+21', 'y_best_nu_tgarch_t+21', 'rv_t+22', 'y_best_alpha_1_t+22', 'y_best_beta_1_t+22', 'y_best_omega_t+22', 'y_best_mu_t+22', 'y_best_nu_tgarch_t+22', 'rv_t+23', 'y_best_alpha_1_t+23', 'y_best_beta_1_t+23', 'y_best_omega_t+23', 'y_best_mu_t+23', 'y_best_nu_tgarch_t+23', 'rv_t+24', 'y_best_alpha_1_t+24', 'y_best_beta_1_t+24', 'y_best_omega_t+24', 'y_best_mu_t+24', 'y_best_nu_tgarch_t+24', 'rv_t+25', 'y_best_alpha_1_t+25', 'y_best_beta_1_t+25', 'y_best_omega_t+25', 'y_best_mu_t+25', 'y_best_nu_tgarch_t+25', 'rv_t+26', 'y_best_alpha_1_t+26', 'y_best_beta_1_t+26', 'y_best_omega_t+26', 'y_best_mu_t+26', 'y_best_nu_tgarch_t+26', 'rv_t+27', 'y_best_alpha_1_t+27', 'y_best_beta_1_t+27', 'y_best_omega_t+27', 'y_best_mu_t+27', 'y_best_nu_tgarch_t+27', 'rv_t+28', 'y_best_alpha_1_t+28', 'y_best_beta_1_t+28', 'y_best_omega_t+28', 'y_best_mu_t+28', 'y_best_nu_tgarch_t+28', 'rv_t+29', 'y_best_alpha_1_t+29', 'y_best_beta_1_t+29', 'y_best_omega_t+29', 'y_best_mu_t+29', 'y_best_nu_tgarch_t+29', 'rv_t+30', 'y_best_alpha_1_t+30', 'y_best_beta_1_t+30', 'y_best_omega_t+30', 'y_best_mu_t+30', 'y_best_nu_tgarch_t+30', 'rv_t+31', 'y_best_alpha_1_t+31', 'y_best_beta_1_t+31', 'y_best_omega_t+31', 'y_best_mu_t+31', 'y_best_nu_tgarch_t+31', 'rv_t+32', 'y_best_alpha_1_t+32', 'y_best_beta_1_t+32', 'y_best_omega_t+32', 'y_best_mu_t+32', 'y_best_nu_tgarch_t+32', 'rv_t+33', 'y_best_alpha_1_t+33', 'y_best_beta_1_t+33', 'y_best_omega_t+33', 'y_best_mu_t+33', 'y_best_nu_tgarch_t+33', 'rv_t+34', 'y_best_alpha_1_t+34', 'y_best_beta_1_t+34', 'y_best_omega_t+34', 'y_best_mu_t+34', 'y_best_nu_tgarch_t+34', 'rv_t+35', 'y_best_alpha_1_t+35', 'y_best_beta_1_t+35', 'y_best_omega_t+35', 'y_best_mu_t+35', 'y_best_nu_tgarch_t+35', 'rv_t+36', 'y_best_alpha_1_t+36', 'y_best_beta_1_t+36', 'y_best_omega_t+36', 'y_best_mu_t+36', 'y_best_nu_tgarch_t+36', 'rv_t+37', 'y_best_alpha_1_t+37', 'y_best_beta_1_t+37', 'y_best_omega_t+37', 'y_best_mu_t+37', 'y_best_nu_tgarch_t+37', 'rv_t+38', 'y_best_alpha_1_t+38', 'y_best_beta_1_t+38', 'y_best_omega_t+38', 'y_best_mu_t+38', 'y_best_nu_tgarch_t+38', 'rv_t+39', 'y_best_alpha_1_t+39', 'y_best_beta_1_t+39', 'y_best_omega_t+39', 'y_best_mu_t+39', 'y_best_nu_tgarch_t+39', 'rv_t+40', 'y_best_alpha_1_t+40', 'y_best_beta_1_t+40', 'y_best_omega_t+40', 'y_best_mu_t+40', 'y_best_nu_tgarch_t+40', 'rv_t+41', 'y_best_alpha_1_t+41', 'y_best_beta_1_t+41', 'y_best_omega_t+41', 'y_best_mu_t+41', 'y_best_nu_tgarch_t+41', 'rv_t+42', 'y_best_alpha_1_t+42', 'y_best_beta_1_t+42', 'y_best_omega_t+42', 'y_best_mu_t+42', 'y_best_nu_tgarch_t+42', 'rv_t+43', 'y_best_alpha_1_t+43', 'y_best_beta_1_t+43', 'y_best_omega_t+43', 'y_best_mu_t+43', 'y_best_nu_tgarch_t+43', 'rv_t+44', 'y_best_alpha_1_t+44', 'y_best_beta_1_t+44', 'y_best_omega_t+44', 'y_best_mu_t+44', 'y_best_nu_tgarch_t+44', 'rv_t+45', 'y_best_alpha_1_t+45', 'y_best_beta_1_t+45', 'y_best_omega_t+45', 'y_best_mu_t+45', 'y_best_nu_tgarch_t+45', 'rv_t+46', 'y_best_alpha_1_t+46', 'y_best_beta_1_t+46', 'y_best_omega_t+46', 'y_best_mu_t+46', 'y_best_nu_tgarch_t+46', 'rv_t+47', 'y_best_alpha_1_t+47', 'y_best_beta_1_t+47', 'y_best_omega_t+47', 'y_best_mu_t+47', 'y_best_nu_tgarch_t+47', 'rv_t+48', 'y_best_alpha_1_t+48', 'y_best_beta_1_t+48', 'y_best_omega_t+48', 'y_best_mu_t+48', 'y_best_nu_tgarch_t+48', 'rv_t+49', 'y_best_alpha_1_t+49', 'y_best_beta_1_t+49', 'y_best_omega_t+49', 'y_best_mu_t+49', 'y_best_nu_tgarch_t+49', 'rv_t+50', 'y_best_alpha_1_t+50', 'y_best_beta_1_t+50', 'y_best_omega_t+50', 'y_best_mu_t+50', 'y_best_nu_tgarch_t+50', 'rv_t+51', 'y_best_alpha_1_t+51', 'y_best_beta_1_t+51', 'y_best_omega_t+51', 'y_best_mu_t+51', 'y_best_nu_tgarch_t+51', 'rv_t+52', 'y_best_alpha_1_t+52', 'y_best_beta_1_t+52', 'y_best_omega_t+52', 'y_best_mu_t+52', 'y_best_nu_tgarch_t+52', 'rv_t+53', 'y_best_alpha_1_t+53', 'y_best_beta_1_t+53', 'y_best_omega_t+53', 'y_best_mu_t+53', 'y_best_nu_tgarch_t+53', 'rv_t+54', 'y_best_alpha_1_t+54', 'y_best_beta_1_t+54', 'y_best_omega_t+54', 'y_best_mu_t+54', 'y_best_nu_tgarch_t+54', 'rv_t+55', 'y_best_alpha_1_t+55', 'y_best_beta_1_t+55', 'y_best_omega_t+55', 'y_best_mu_t+55', 'y_best_nu_tgarch_t+55', 'rv_t+56', 'y_best_alpha_1_t+56', 'y_best_beta_1_t+56', 'y_best_omega_t+56', 'y_best_mu_t+56', 'y_best_nu_tgarch_t+56', 'rv_t+57', 'y_best_alpha_1_t+57', 'y_best_beta_1_t+57', 'y_best_omega_t+57', 'y_best_mu_t+57', 'y_best_nu_tgarch_t+57', 'rv_t+58', 'y_best_alpha_1_t+58', 'y_best_beta_1_t+58', 'y_best_omega_t+58', 'y_best_mu_t+58', 'y_best_nu_tgarch_t+58', 'rv_t+59', 'y_best_alpha_1_t+59', 'y_best_beta_1_t+59', 'y_best_omega_t+59', 'y_best_mu_t+59', 'y_best_nu_tgarch_t+59', 'rv_t+60', 'y_best_alpha_1_t+60', 'y_best_beta_1_t+60', 'y_best_omega_t+60', 'y_best_mu_t+60', 'y_best_nu_tgarch_t+60']
[X_price] shape=(3782, 60, 6) | NaN: 0 | +Inf: 0 | -Inf: 0 | Any Inf: 0
[y] shape=(3782, 60, 6) | NaN: 0 | +Inf: 0 | -Inf: 0 | Any Inf: 0
feature min p01 median p99 max mean \
0 f5 0.000000 3.031849e+00 5.034044 106.962327 345.033304 7.723787
1 f0 0.000096 2.542676e-03 0.151064 1.340520 5.997252 0.220419
2 f2 0.000000 0.000000e+00 0.954145 1.000000 1.000000 0.817385
3 f3 0.000000 9.629686e-10 0.003832 0.219574 0.424595 0.028887
4 f1 0.000000 0.000000e+00 0.006112 0.136720 0.205071 0.024899
5 f4 -0.071164 -6.033247e-02 -0.002668 0.037148 0.043256 -0.003317
std
0 18.160206
1 0.287035
2 0.315141
3 0.052974
4 0.034081
5 0.020569
feature min p01 median p99 max \
0 f5 2.400685e+00 3.057405e+00 4.889405 108.026063 339.032393
1 f0 9.561894e-05 2.542676e-03 0.151394 1.342547 5.997252
2 f2 0.000000e+00 0.000000e+00 0.955367 1.000000 1.000000
3 f3 7.501062e-10 9.604417e-10 0.003576 0.219686 0.424926
4 f1 0.000000e+00 0.000000e+00 0.001614 0.137276 0.202748
5 f4 -7.201752e-02 -6.041314e-02 -0.002971 0.037185 0.043658
mean std
0 7.677354 17.956992
1 0.221915 0.290001
2 0.820012 0.313103
3 0.028502 0.052676
4 0.024652 0.034231
5 -0.003463 0.020489
Load data v8¶
In [13]:
load_data_object_8_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_8.pkl")
with open(load_data_object_8_file_path, "rb") as f:
structured_data_dict_8 = pickle.load(f)
print("Data dictionary 8 loaded successfully.")
eur8_X_price = structured_data_dict_8["EURUSD"].get("X_other")
eur8_X_time = structured_data_dict_8["EURUSD"].get("X_time")
eur8_y = structured_data_dict_8["EURUSD"].get("y")
eur8_y_columns = structured_data_dict_8["EURUSD"].get("y_columns")
eur8_X_columns = structured_data_dict_8["EURUSD"].get("X_other_columns")
print(eur8_X_columns)
print(eur8_y_columns)
df_xprice = summarize_features(eur8_X_price, eur8_X_columns, name="X_price")
df_y = summarize_features(eur8_y, eur8_y_columns, name="y")
print(df_xprice.head(20))
print(df_y.head(20))
Data dictionary 8 loaded successfully.
['rv_t-59', 'rv_t-58', 'rv_t-57', 'rv_t-56', 'rv_t-55', 'rv_t-54', 'rv_t-53', 'rv_t-52', 'rv_t-51', 'rv_t-50', 'rv_t-49', 'rv_t-48', 'rv_t-47', 'rv_t-46', 'rv_t-45', 'rv_t-44', 'rv_t-43', 'rv_t-42', 'rv_t-41', 'rv_t-40', 'rv_t-39', 'rv_t-38', 'rv_t-37', 'rv_t-36', 'rv_t-35', 'rv_t-34', 'rv_t-33', 'rv_t-32', 'rv_t-31', 'rv_t-30', 'rv_t-29', 'rv_t-28', 'rv_t-27', 'rv_t-26', 'rv_t-25', 'rv_t-24', 'rv_t-23', 'rv_t-22', 'rv_t-21', 'rv_t-20', 'rv_t-19', 'rv_t-18', 'rv_t-17', 'rv_t-16', 'rv_t-15', 'rv_t-14', 'rv_t-13', 'rv_t-12', 'rv_t-11', 'rv_t-10', 'rv_t-9', 'rv_t-8', 'rv_t-7', 'rv_t-6', 'rv_t-5', 'rv_t-4', 'rv_t-3', 'rv_t-2', 'rv_t-1', 'rv_t-0']
['rv_t+1', 'y_best_alpha_1_t+1', 'y_best_beta_1_t+1', 'y_best_omega_t+1', 'y_best_mu_t+1', 'y_best_nu_tgarch_t+1', 'rv_t+2', 'y_best_alpha_1_t+2', 'y_best_beta_1_t+2', 'y_best_omega_t+2', 'y_best_mu_t+2', 'y_best_nu_tgarch_t+2', 'rv_t+3', 'y_best_alpha_1_t+3', 'y_best_beta_1_t+3', 'y_best_omega_t+3', 'y_best_mu_t+3', 'y_best_nu_tgarch_t+3', 'rv_t+4', 'y_best_alpha_1_t+4', 'y_best_beta_1_t+4', 'y_best_omega_t+4', 'y_best_mu_t+4', 'y_best_nu_tgarch_t+4', 'rv_t+5', 'y_best_alpha_1_t+5', 'y_best_beta_1_t+5', 'y_best_omega_t+5', 'y_best_mu_t+5', 'y_best_nu_tgarch_t+5', 'rv_t+6', 'y_best_alpha_1_t+6', 'y_best_beta_1_t+6', 'y_best_omega_t+6', 'y_best_mu_t+6', 'y_best_nu_tgarch_t+6', 'rv_t+7', 'y_best_alpha_1_t+7', 'y_best_beta_1_t+7', 'y_best_omega_t+7', 'y_best_mu_t+7', 'y_best_nu_tgarch_t+7', 'rv_t+8', 'y_best_alpha_1_t+8', 'y_best_beta_1_t+8', 'y_best_omega_t+8', 'y_best_mu_t+8', 'y_best_nu_tgarch_t+8', 'rv_t+9', 'y_best_alpha_1_t+9', 'y_best_beta_1_t+9', 'y_best_omega_t+9', 'y_best_mu_t+9', 'y_best_nu_tgarch_t+9', 'rv_t+10', 'y_best_alpha_1_t+10', 'y_best_beta_1_t+10', 'y_best_omega_t+10', 'y_best_mu_t+10', 'y_best_nu_tgarch_t+10', 'rv_t+11', 'y_best_alpha_1_t+11', 'y_best_beta_1_t+11', 'y_best_omega_t+11', 'y_best_mu_t+11', 'y_best_nu_tgarch_t+11', 'rv_t+12', 'y_best_alpha_1_t+12', 'y_best_beta_1_t+12', 'y_best_omega_t+12', 'y_best_mu_t+12', 'y_best_nu_tgarch_t+12', 'rv_t+13', 'y_best_alpha_1_t+13', 'y_best_beta_1_t+13', 'y_best_omega_t+13', 'y_best_mu_t+13', 'y_best_nu_tgarch_t+13', 'rv_t+14', 'y_best_alpha_1_t+14', 'y_best_beta_1_t+14', 'y_best_omega_t+14', 'y_best_mu_t+14', 'y_best_nu_tgarch_t+14', 'rv_t+15', 'y_best_alpha_1_t+15', 'y_best_beta_1_t+15', 'y_best_omega_t+15', 'y_best_mu_t+15', 'y_best_nu_tgarch_t+15', 'rv_t+16', 'y_best_alpha_1_t+16', 'y_best_beta_1_t+16', 'y_best_omega_t+16', 'y_best_mu_t+16', 'y_best_nu_tgarch_t+16', 'rv_t+17', 'y_best_alpha_1_t+17', 'y_best_beta_1_t+17', 'y_best_omega_t+17', 'y_best_mu_t+17', 'y_best_nu_tgarch_t+17', 'rv_t+18', 'y_best_alpha_1_t+18', 'y_best_beta_1_t+18', 'y_best_omega_t+18', 'y_best_mu_t+18', 'y_best_nu_tgarch_t+18', 'rv_t+19', 'y_best_alpha_1_t+19', 'y_best_beta_1_t+19', 'y_best_omega_t+19', 'y_best_mu_t+19', 'y_best_nu_tgarch_t+19', 'rv_t+20', 'y_best_alpha_1_t+20', 'y_best_beta_1_t+20', 'y_best_omega_t+20', 'y_best_mu_t+20', 'y_best_nu_tgarch_t+20', 'rv_t+21', 'y_best_alpha_1_t+21', 'y_best_beta_1_t+21', 'y_best_omega_t+21', 'y_best_mu_t+21', 'y_best_nu_tgarch_t+21', 'rv_t+22', 'y_best_alpha_1_t+22', 'y_best_beta_1_t+22', 'y_best_omega_t+22', 'y_best_mu_t+22', 'y_best_nu_tgarch_t+22', 'rv_t+23', 'y_best_alpha_1_t+23', 'y_best_beta_1_t+23', 'y_best_omega_t+23', 'y_best_mu_t+23', 'y_best_nu_tgarch_t+23', 'rv_t+24', 'y_best_alpha_1_t+24', 'y_best_beta_1_t+24', 'y_best_omega_t+24', 'y_best_mu_t+24', 'y_best_nu_tgarch_t+24', 'rv_t+25', 'y_best_alpha_1_t+25', 'y_best_beta_1_t+25', 'y_best_omega_t+25', 'y_best_mu_t+25', 'y_best_nu_tgarch_t+25', 'rv_t+26', 'y_best_alpha_1_t+26', 'y_best_beta_1_t+26', 'y_best_omega_t+26', 'y_best_mu_t+26', 'y_best_nu_tgarch_t+26', 'rv_t+27', 'y_best_alpha_1_t+27', 'y_best_beta_1_t+27', 'y_best_omega_t+27', 'y_best_mu_t+27', 'y_best_nu_tgarch_t+27', 'rv_t+28', 'y_best_alpha_1_t+28', 'y_best_beta_1_t+28', 'y_best_omega_t+28', 'y_best_mu_t+28', 'y_best_nu_tgarch_t+28', 'rv_t+29', 'y_best_alpha_1_t+29', 'y_best_beta_1_t+29', 'y_best_omega_t+29', 'y_best_mu_t+29', 'y_best_nu_tgarch_t+29', 'rv_t+30', 'y_best_alpha_1_t+30', 'y_best_beta_1_t+30', 'y_best_omega_t+30', 'y_best_mu_t+30', 'y_best_nu_tgarch_t+30', 'rv_t+31', 'y_best_alpha_1_t+31', 'y_best_beta_1_t+31', 'y_best_omega_t+31', 'y_best_mu_t+31', 'y_best_nu_tgarch_t+31', 'rv_t+32', 'y_best_alpha_1_t+32', 'y_best_beta_1_t+32', 'y_best_omega_t+32', 'y_best_mu_t+32', 'y_best_nu_tgarch_t+32', 'rv_t+33', 'y_best_alpha_1_t+33', 'y_best_beta_1_t+33', 'y_best_omega_t+33', 'y_best_mu_t+33', 'y_best_nu_tgarch_t+33', 'rv_t+34', 'y_best_alpha_1_t+34', 'y_best_beta_1_t+34', 'y_best_omega_t+34', 'y_best_mu_t+34', 'y_best_nu_tgarch_t+34', 'rv_t+35', 'y_best_alpha_1_t+35', 'y_best_beta_1_t+35', 'y_best_omega_t+35', 'y_best_mu_t+35', 'y_best_nu_tgarch_t+35', 'rv_t+36', 'y_best_alpha_1_t+36', 'y_best_beta_1_t+36', 'y_best_omega_t+36', 'y_best_mu_t+36', 'y_best_nu_tgarch_t+36', 'rv_t+37', 'y_best_alpha_1_t+37', 'y_best_beta_1_t+37', 'y_best_omega_t+37', 'y_best_mu_t+37', 'y_best_nu_tgarch_t+37', 'rv_t+38', 'y_best_alpha_1_t+38', 'y_best_beta_1_t+38', 'y_best_omega_t+38', 'y_best_mu_t+38', 'y_best_nu_tgarch_t+38', 'rv_t+39', 'y_best_alpha_1_t+39', 'y_best_beta_1_t+39', 'y_best_omega_t+39', 'y_best_mu_t+39', 'y_best_nu_tgarch_t+39', 'rv_t+40', 'y_best_alpha_1_t+40', 'y_best_beta_1_t+40', 'y_best_omega_t+40', 'y_best_mu_t+40', 'y_best_nu_tgarch_t+40', 'rv_t+41', 'y_best_alpha_1_t+41', 'y_best_beta_1_t+41', 'y_best_omega_t+41', 'y_best_mu_t+41', 'y_best_nu_tgarch_t+41', 'rv_t+42', 'y_best_alpha_1_t+42', 'y_best_beta_1_t+42', 'y_best_omega_t+42', 'y_best_mu_t+42', 'y_best_nu_tgarch_t+42', 'rv_t+43', 'y_best_alpha_1_t+43', 'y_best_beta_1_t+43', 'y_best_omega_t+43', 'y_best_mu_t+43', 'y_best_nu_tgarch_t+43', 'rv_t+44', 'y_best_alpha_1_t+44', 'y_best_beta_1_t+44', 'y_best_omega_t+44', 'y_best_mu_t+44', 'y_best_nu_tgarch_t+44', 'rv_t+45', 'y_best_alpha_1_t+45', 'y_best_beta_1_t+45', 'y_best_omega_t+45', 'y_best_mu_t+45', 'y_best_nu_tgarch_t+45', 'rv_t+46', 'y_best_alpha_1_t+46', 'y_best_beta_1_t+46', 'y_best_omega_t+46', 'y_best_mu_t+46', 'y_best_nu_tgarch_t+46', 'rv_t+47', 'y_best_alpha_1_t+47', 'y_best_beta_1_t+47', 'y_best_omega_t+47', 'y_best_mu_t+47', 'y_best_nu_tgarch_t+47', 'rv_t+48', 'y_best_alpha_1_t+48', 'y_best_beta_1_t+48', 'y_best_omega_t+48', 'y_best_mu_t+48', 'y_best_nu_tgarch_t+48', 'rv_t+49', 'y_best_alpha_1_t+49', 'y_best_beta_1_t+49', 'y_best_omega_t+49', 'y_best_mu_t+49', 'y_best_nu_tgarch_t+49', 'rv_t+50', 'y_best_alpha_1_t+50', 'y_best_beta_1_t+50', 'y_best_omega_t+50', 'y_best_mu_t+50', 'y_best_nu_tgarch_t+50', 'rv_t+51', 'y_best_alpha_1_t+51', 'y_best_beta_1_t+51', 'y_best_omega_t+51', 'y_best_mu_t+51', 'y_best_nu_tgarch_t+51', 'rv_t+52', 'y_best_alpha_1_t+52', 'y_best_beta_1_t+52', 'y_best_omega_t+52', 'y_best_mu_t+52', 'y_best_nu_tgarch_t+52', 'rv_t+53', 'y_best_alpha_1_t+53', 'y_best_beta_1_t+53', 'y_best_omega_t+53', 'y_best_mu_t+53', 'y_best_nu_tgarch_t+53', 'rv_t+54', 'y_best_alpha_1_t+54', 'y_best_beta_1_t+54', 'y_best_omega_t+54', 'y_best_mu_t+54', 'y_best_nu_tgarch_t+54', 'rv_t+55', 'y_best_alpha_1_t+55', 'y_best_beta_1_t+55', 'y_best_omega_t+55', 'y_best_mu_t+55', 'y_best_nu_tgarch_t+55', 'rv_t+56', 'y_best_alpha_1_t+56', 'y_best_beta_1_t+56', 'y_best_omega_t+56', 'y_best_mu_t+56', 'y_best_nu_tgarch_t+56', 'rv_t+57', 'y_best_alpha_1_t+57', 'y_best_beta_1_t+57', 'y_best_omega_t+57', 'y_best_mu_t+57', 'y_best_nu_tgarch_t+57', 'rv_t+58', 'y_best_alpha_1_t+58', 'y_best_beta_1_t+58', 'y_best_omega_t+58', 'y_best_mu_t+58', 'y_best_nu_tgarch_t+58', 'rv_t+59', 'y_best_alpha_1_t+59', 'y_best_beta_1_t+59', 'y_best_omega_t+59', 'y_best_mu_t+59', 'y_best_nu_tgarch_t+59', 'rv_t+60', 'y_best_alpha_1_t+60', 'y_best_beta_1_t+60', 'y_best_omega_t+60', 'y_best_mu_t+60', 'y_best_nu_tgarch_t+60']
[X_price] shape=(3782, 60, 1) | NaN: 0 | +Inf: 0 | -Inf: 0 | Any Inf: 0
[y] shape=(3782, 60, 6) | NaN: 0 | +Inf: 0 | -Inf: 0 | Any Inf: 0
feature min p01 median p99 max mean std
0 f0 0.000096 0.002543 0.151064 1.34052 5.997252 0.220419 0.287035
feature min p01 median p99 max \
0 f5 2.400685e+00 3.057405e+00 4.889405 108.026063 339.032393
1 f0 9.561894e-05 2.542676e-03 0.151394 1.342547 5.997252
2 f2 0.000000e+00 0.000000e+00 0.955367 1.000000 1.000000
3 f3 7.501062e-10 9.604417e-10 0.003576 0.219686 0.424926
4 f1 0.000000e+00 0.000000e+00 0.001614 0.137276 0.202748
5 f4 -7.201752e-02 -6.041314e-02 -0.002971 0.037185 0.043658
mean std
0 7.677354 17.956992
1 0.221915 0.290001
2 0.820012 0.313103
3 0.028502 0.052676
4 0.024652 0.034231
5 -0.003463 0.020489
Apple v8¶
In [14]:
appl8_X_price = structured_data_dict_8["AAPL"].get("X_other")
appl8_X_time = structured_data_dict_8["AAPL"].get("X_time")
appl8_y = structured_data_dict_8["AAPL"].get("y")
appl8_y_columns = structured_data_dict_8["AAPL"].get("y_columns")
appl8_X_columns = structured_data_dict_8["AAPL"].get("X_other_columns")
print(appl8_X_columns)
print(appl8_y_columns)
print(appl8_y[:,0,0])
['rv_t-59', 'rv_t-58', 'rv_t-57', 'rv_t-56', 'rv_t-55', 'rv_t-54', 'rv_t-53', 'rv_t-52', 'rv_t-51', 'rv_t-50', 'rv_t-49', 'rv_t-48', 'rv_t-47', 'rv_t-46', 'rv_t-45', 'rv_t-44', 'rv_t-43', 'rv_t-42', 'rv_t-41', 'rv_t-40', 'rv_t-39', 'rv_t-38', 'rv_t-37', 'rv_t-36', 'rv_t-35', 'rv_t-34', 'rv_t-33', 'rv_t-32', 'rv_t-31', 'rv_t-30', 'rv_t-29', 'rv_t-28', 'rv_t-27', 'rv_t-26', 'rv_t-25', 'rv_t-24', 'rv_t-23', 'rv_t-22', 'rv_t-21', 'rv_t-20', 'rv_t-19', 'rv_t-18', 'rv_t-17', 'rv_t-16', 'rv_t-15', 'rv_t-14', 'rv_t-13', 'rv_t-12', 'rv_t-11', 'rv_t-10', 'rv_t-9', 'rv_t-8', 'rv_t-7', 'rv_t-6', 'rv_t-5', 'rv_t-4', 'rv_t-3', 'rv_t-2', 'rv_t-1', 'rv_t-0'] ['rv_t+1', 'y_best_alpha_1_t+1', 'y_best_beta_1_t+1', 'y_best_omega_t+1', 'y_best_mu_t+1', 'y_best_nu_tgarch_t+1', 'rv_t+2', 'y_best_alpha_1_t+2', 'y_best_beta_1_t+2', 'y_best_omega_t+2', 'y_best_mu_t+2', 'y_best_nu_tgarch_t+2', 'rv_t+3', 'y_best_alpha_1_t+3', 'y_best_beta_1_t+3', 'y_best_omega_t+3', 'y_best_mu_t+3', 'y_best_nu_tgarch_t+3', 'rv_t+4', 'y_best_alpha_1_t+4', 'y_best_beta_1_t+4', 'y_best_omega_t+4', 'y_best_mu_t+4', 'y_best_nu_tgarch_t+4', 'rv_t+5', 'y_best_alpha_1_t+5', 'y_best_beta_1_t+5', 'y_best_omega_t+5', 'y_best_mu_t+5', 'y_best_nu_tgarch_t+5', 'rv_t+6', 'y_best_alpha_1_t+6', 'y_best_beta_1_t+6', 'y_best_omega_t+6', 'y_best_mu_t+6', 'y_best_nu_tgarch_t+6', 'rv_t+7', 'y_best_alpha_1_t+7', 'y_best_beta_1_t+7', 'y_best_omega_t+7', 'y_best_mu_t+7', 'y_best_nu_tgarch_t+7', 'rv_t+8', 'y_best_alpha_1_t+8', 'y_best_beta_1_t+8', 'y_best_omega_t+8', 'y_best_mu_t+8', 'y_best_nu_tgarch_t+8', 'rv_t+9', 'y_best_alpha_1_t+9', 'y_best_beta_1_t+9', 'y_best_omega_t+9', 'y_best_mu_t+9', 'y_best_nu_tgarch_t+9', 'rv_t+10', 'y_best_alpha_1_t+10', 'y_best_beta_1_t+10', 'y_best_omega_t+10', 'y_best_mu_t+10', 'y_best_nu_tgarch_t+10', 'rv_t+11', 'y_best_alpha_1_t+11', 'y_best_beta_1_t+11', 'y_best_omega_t+11', 'y_best_mu_t+11', 'y_best_nu_tgarch_t+11', 'rv_t+12', 'y_best_alpha_1_t+12', 'y_best_beta_1_t+12', 'y_best_omega_t+12', 'y_best_mu_t+12', 'y_best_nu_tgarch_t+12', 'rv_t+13', 'y_best_alpha_1_t+13', 'y_best_beta_1_t+13', 'y_best_omega_t+13', 'y_best_mu_t+13', 'y_best_nu_tgarch_t+13', 'rv_t+14', 'y_best_alpha_1_t+14', 'y_best_beta_1_t+14', 'y_best_omega_t+14', 'y_best_mu_t+14', 'y_best_nu_tgarch_t+14', 'rv_t+15', 'y_best_alpha_1_t+15', 'y_best_beta_1_t+15', 'y_best_omega_t+15', 'y_best_mu_t+15', 'y_best_nu_tgarch_t+15', 'rv_t+16', 'y_best_alpha_1_t+16', 'y_best_beta_1_t+16', 'y_best_omega_t+16', 'y_best_mu_t+16', 'y_best_nu_tgarch_t+16', 'rv_t+17', 'y_best_alpha_1_t+17', 'y_best_beta_1_t+17', 'y_best_omega_t+17', 'y_best_mu_t+17', 'y_best_nu_tgarch_t+17', 'rv_t+18', 'y_best_alpha_1_t+18', 'y_best_beta_1_t+18', 'y_best_omega_t+18', 'y_best_mu_t+18', 'y_best_nu_tgarch_t+18', 'rv_t+19', 'y_best_alpha_1_t+19', 'y_best_beta_1_t+19', 'y_best_omega_t+19', 'y_best_mu_t+19', 'y_best_nu_tgarch_t+19', 'rv_t+20', 'y_best_alpha_1_t+20', 'y_best_beta_1_t+20', 'y_best_omega_t+20', 'y_best_mu_t+20', 'y_best_nu_tgarch_t+20', 'rv_t+21', 'y_best_alpha_1_t+21', 'y_best_beta_1_t+21', 'y_best_omega_t+21', 'y_best_mu_t+21', 'y_best_nu_tgarch_t+21', 'rv_t+22', 'y_best_alpha_1_t+22', 'y_best_beta_1_t+22', 'y_best_omega_t+22', 'y_best_mu_t+22', 'y_best_nu_tgarch_t+22', 'rv_t+23', 'y_best_alpha_1_t+23', 'y_best_beta_1_t+23', 'y_best_omega_t+23', 'y_best_mu_t+23', 'y_best_nu_tgarch_t+23', 'rv_t+24', 'y_best_alpha_1_t+24', 'y_best_beta_1_t+24', 'y_best_omega_t+24', 'y_best_mu_t+24', 'y_best_nu_tgarch_t+24', 'rv_t+25', 'y_best_alpha_1_t+25', 'y_best_beta_1_t+25', 'y_best_omega_t+25', 'y_best_mu_t+25', 'y_best_nu_tgarch_t+25', 'rv_t+26', 'y_best_alpha_1_t+26', 'y_best_beta_1_t+26', 'y_best_omega_t+26', 'y_best_mu_t+26', 'y_best_nu_tgarch_t+26', 'rv_t+27', 'y_best_alpha_1_t+27', 'y_best_beta_1_t+27', 'y_best_omega_t+27', 'y_best_mu_t+27', 'y_best_nu_tgarch_t+27', 'rv_t+28', 'y_best_alpha_1_t+28', 'y_best_beta_1_t+28', 'y_best_omega_t+28', 'y_best_mu_t+28', 'y_best_nu_tgarch_t+28', 'rv_t+29', 'y_best_alpha_1_t+29', 'y_best_beta_1_t+29', 'y_best_omega_t+29', 'y_best_mu_t+29', 'y_best_nu_tgarch_t+29', 'rv_t+30', 'y_best_alpha_1_t+30', 'y_best_beta_1_t+30', 'y_best_omega_t+30', 'y_best_mu_t+30', 'y_best_nu_tgarch_t+30', 'rv_t+31', 'y_best_alpha_1_t+31', 'y_best_beta_1_t+31', 'y_best_omega_t+31', 'y_best_mu_t+31', 'y_best_nu_tgarch_t+31', 'rv_t+32', 'y_best_alpha_1_t+32', 'y_best_beta_1_t+32', 'y_best_omega_t+32', 'y_best_mu_t+32', 'y_best_nu_tgarch_t+32', 'rv_t+33', 'y_best_alpha_1_t+33', 'y_best_beta_1_t+33', 'y_best_omega_t+33', 'y_best_mu_t+33', 'y_best_nu_tgarch_t+33', 'rv_t+34', 'y_best_alpha_1_t+34', 'y_best_beta_1_t+34', 'y_best_omega_t+34', 'y_best_mu_t+34', 'y_best_nu_tgarch_t+34', 'rv_t+35', 'y_best_alpha_1_t+35', 'y_best_beta_1_t+35', 'y_best_omega_t+35', 'y_best_mu_t+35', 'y_best_nu_tgarch_t+35', 'rv_t+36', 'y_best_alpha_1_t+36', 'y_best_beta_1_t+36', 'y_best_omega_t+36', 'y_best_mu_t+36', 'y_best_nu_tgarch_t+36', 'rv_t+37', 'y_best_alpha_1_t+37', 'y_best_beta_1_t+37', 'y_best_omega_t+37', 'y_best_mu_t+37', 'y_best_nu_tgarch_t+37', 'rv_t+38', 'y_best_alpha_1_t+38', 'y_best_beta_1_t+38', 'y_best_omega_t+38', 'y_best_mu_t+38', 'y_best_nu_tgarch_t+38', 'rv_t+39', 'y_best_alpha_1_t+39', 'y_best_beta_1_t+39', 'y_best_omega_t+39', 'y_best_mu_t+39', 'y_best_nu_tgarch_t+39', 'rv_t+40', 'y_best_alpha_1_t+40', 'y_best_beta_1_t+40', 'y_best_omega_t+40', 'y_best_mu_t+40', 'y_best_nu_tgarch_t+40', 'rv_t+41', 'y_best_alpha_1_t+41', 'y_best_beta_1_t+41', 'y_best_omega_t+41', 'y_best_mu_t+41', 'y_best_nu_tgarch_t+41', 'rv_t+42', 'y_best_alpha_1_t+42', 'y_best_beta_1_t+42', 'y_best_omega_t+42', 'y_best_mu_t+42', 'y_best_nu_tgarch_t+42', 'rv_t+43', 'y_best_alpha_1_t+43', 'y_best_beta_1_t+43', 'y_best_omega_t+43', 'y_best_mu_t+43', 'y_best_nu_tgarch_t+43', 'rv_t+44', 'y_best_alpha_1_t+44', 'y_best_beta_1_t+44', 'y_best_omega_t+44', 'y_best_mu_t+44', 'y_best_nu_tgarch_t+44', 'rv_t+45', 'y_best_alpha_1_t+45', 'y_best_beta_1_t+45', 'y_best_omega_t+45', 'y_best_mu_t+45', 'y_best_nu_tgarch_t+45', 'rv_t+46', 'y_best_alpha_1_t+46', 'y_best_beta_1_t+46', 'y_best_omega_t+46', 'y_best_mu_t+46', 'y_best_nu_tgarch_t+46', 'rv_t+47', 'y_best_alpha_1_t+47', 'y_best_beta_1_t+47', 'y_best_omega_t+47', 'y_best_mu_t+47', 'y_best_nu_tgarch_t+47', 'rv_t+48', 'y_best_alpha_1_t+48', 'y_best_beta_1_t+48', 'y_best_omega_t+48', 'y_best_mu_t+48', 'y_best_nu_tgarch_t+48', 'rv_t+49', 'y_best_alpha_1_t+49', 'y_best_beta_1_t+49', 'y_best_omega_t+49', 'y_best_mu_t+49', 'y_best_nu_tgarch_t+49', 'rv_t+50', 'y_best_alpha_1_t+50', 'y_best_beta_1_t+50', 'y_best_omega_t+50', 'y_best_mu_t+50', 'y_best_nu_tgarch_t+50', 'rv_t+51', 'y_best_alpha_1_t+51', 'y_best_beta_1_t+51', 'y_best_omega_t+51', 'y_best_mu_t+51', 'y_best_nu_tgarch_t+51', 'rv_t+52', 'y_best_alpha_1_t+52', 'y_best_beta_1_t+52', 'y_best_omega_t+52', 'y_best_mu_t+52', 'y_best_nu_tgarch_t+52', 'rv_t+53', 'y_best_alpha_1_t+53', 'y_best_beta_1_t+53', 'y_best_omega_t+53', 'y_best_mu_t+53', 'y_best_nu_tgarch_t+53', 'rv_t+54', 'y_best_alpha_1_t+54', 'y_best_beta_1_t+54', 'y_best_omega_t+54', 'y_best_mu_t+54', 'y_best_nu_tgarch_t+54', 'rv_t+55', 'y_best_alpha_1_t+55', 'y_best_beta_1_t+55', 'y_best_omega_t+55', 'y_best_mu_t+55', 'y_best_nu_tgarch_t+55', 'rv_t+56', 'y_best_alpha_1_t+56', 'y_best_beta_1_t+56', 'y_best_omega_t+56', 'y_best_mu_t+56', 'y_best_nu_tgarch_t+56', 'rv_t+57', 'y_best_alpha_1_t+57', 'y_best_beta_1_t+57', 'y_best_omega_t+57', 'y_best_mu_t+57', 'y_best_nu_tgarch_t+57', 'rv_t+58', 'y_best_alpha_1_t+58', 'y_best_beta_1_t+58', 'y_best_omega_t+58', 'y_best_mu_t+58', 'y_best_nu_tgarch_t+58', 'rv_t+59', 'y_best_alpha_1_t+59', 'y_best_beta_1_t+59', 'y_best_omega_t+59', 'y_best_mu_t+59', 'y_best_nu_tgarch_t+59', 'rv_t+60', 'y_best_alpha_1_t+60', 'y_best_beta_1_t+60', 'y_best_omega_t+60', 'y_best_mu_t+60', 'y_best_nu_tgarch_t+60'] [ 0.44148683 1.36512504 0.86625199 ... 112.40750435 30.95861336 16.24492026]
Load data v9¶
In [15]:
load_data_object_9_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_9.pkl")
with open(load_data_object_9_file_path, "rb") as f:
structured_data_dict_9 = pickle.load(f)
print("Data dictionary 9 loaded successfully.")
eur9_X_price = structured_data_dict_9["EURUSD"].get("X_other")
eur9_X_time = structured_data_dict_9["EURUSD"].get("X_time")
eur9_y = structured_data_dict_9["EURUSD"].get("y")
eur9_y_columns = structured_data_dict_9["EURUSD"].get("y_columns")
eur9_X_columns = structured_data_dict_9["EURUSD"].get("X_other_columns")
print(eur9_X_columns)
print(eur9_y_columns)
df_xprice = summarize_features(eur9_X_price, eur9_X_columns, name="X_price")
df_y = summarize_features(eur9_y, eur9_y_columns, name="y")
print(df_xprice.head(20))
print(df_y.head(20))
Data dictionary 9 loaded successfully.
['rv_t-59', 'x_best_pred_var_t-59', 'rv_t-58', 'x_best_pred_var_t-58', 'rv_t-57', 'x_best_pred_var_t-57', 'rv_t-56', 'x_best_pred_var_t-56', 'rv_t-55', 'x_best_pred_var_t-55', 'rv_t-54', 'x_best_pred_var_t-54', 'rv_t-53', 'x_best_pred_var_t-53', 'rv_t-52', 'x_best_pred_var_t-52', 'rv_t-51', 'x_best_pred_var_t-51', 'rv_t-50', 'x_best_pred_var_t-50', 'rv_t-49', 'x_best_pred_var_t-49', 'rv_t-48', 'x_best_pred_var_t-48', 'rv_t-47', 'x_best_pred_var_t-47', 'rv_t-46', 'x_best_pred_var_t-46', 'rv_t-45', 'x_best_pred_var_t-45', 'rv_t-44', 'x_best_pred_var_t-44', 'rv_t-43', 'x_best_pred_var_t-43', 'rv_t-42', 'x_best_pred_var_t-42', 'rv_t-41', 'x_best_pred_var_t-41', 'rv_t-40', 'x_best_pred_var_t-40', 'rv_t-39', 'x_best_pred_var_t-39', 'rv_t-38', 'x_best_pred_var_t-38', 'rv_t-37', 'x_best_pred_var_t-37', 'rv_t-36', 'x_best_pred_var_t-36', 'rv_t-35', 'x_best_pred_var_t-35', 'rv_t-34', 'x_best_pred_var_t-34', 'rv_t-33', 'x_best_pred_var_t-33', 'rv_t-32', 'x_best_pred_var_t-32', 'rv_t-31', 'x_best_pred_var_t-31', 'rv_t-30', 'x_best_pred_var_t-30', 'rv_t-29', 'x_best_pred_var_t-29', 'rv_t-28', 'x_best_pred_var_t-28', 'rv_t-27', 'x_best_pred_var_t-27', 'rv_t-26', 'x_best_pred_var_t-26', 'rv_t-25', 'x_best_pred_var_t-25', 'rv_t-24', 'x_best_pred_var_t-24', 'rv_t-23', 'x_best_pred_var_t-23', 'rv_t-22', 'x_best_pred_var_t-22', 'rv_t-21', 'x_best_pred_var_t-21', 'rv_t-20', 'x_best_pred_var_t-20', 'rv_t-19', 'x_best_pred_var_t-19', 'rv_t-18', 'x_best_pred_var_t-18', 'rv_t-17', 'x_best_pred_var_t-17', 'rv_t-16', 'x_best_pred_var_t-16', 'rv_t-15', 'x_best_pred_var_t-15', 'rv_t-14', 'x_best_pred_var_t-14', 'rv_t-13', 'x_best_pred_var_t-13', 'rv_t-12', 'x_best_pred_var_t-12', 'rv_t-11', 'x_best_pred_var_t-11', 'rv_t-10', 'x_best_pred_var_t-10', 'rv_t-9', 'x_best_pred_var_t-9', 'rv_t-8', 'x_best_pred_var_t-8', 'rv_t-7', 'x_best_pred_var_t-7', 'rv_t-6', 'x_best_pred_var_t-6', 'rv_t-5', 'x_best_pred_var_t-5', 'rv_t-4', 'x_best_pred_var_t-4', 'rv_t-3', 'x_best_pred_var_t-3', 'rv_t-2', 'x_best_pred_var_t-2', 'rv_t-1', 'x_best_pred_var_t-1', 'rv_t-0', 'x_best_pred_var_t-0']
['rv_t+1', 'y_best_pred_var_t+1', 'rv_t+2', 'y_best_pred_var_t+2', 'rv_t+3', 'y_best_pred_var_t+3', 'rv_t+4', 'y_best_pred_var_t+4', 'rv_t+5', 'y_best_pred_var_t+5', 'rv_t+6', 'y_best_pred_var_t+6', 'rv_t+7', 'y_best_pred_var_t+7', 'rv_t+8', 'y_best_pred_var_t+8', 'rv_t+9', 'y_best_pred_var_t+9', 'rv_t+10', 'y_best_pred_var_t+10', 'rv_t+11', 'y_best_pred_var_t+11', 'rv_t+12', 'y_best_pred_var_t+12', 'rv_t+13', 'y_best_pred_var_t+13', 'rv_t+14', 'y_best_pred_var_t+14', 'rv_t+15', 'y_best_pred_var_t+15', 'rv_t+16', 'y_best_pred_var_t+16', 'rv_t+17', 'y_best_pred_var_t+17', 'rv_t+18', 'y_best_pred_var_t+18', 'rv_t+19', 'y_best_pred_var_t+19', 'rv_t+20', 'y_best_pred_var_t+20', 'rv_t+21', 'y_best_pred_var_t+21', 'rv_t+22', 'y_best_pred_var_t+22', 'rv_t+23', 'y_best_pred_var_t+23', 'rv_t+24', 'y_best_pred_var_t+24', 'rv_t+25', 'y_best_pred_var_t+25', 'rv_t+26', 'y_best_pred_var_t+26', 'rv_t+27', 'y_best_pred_var_t+27', 'rv_t+28', 'y_best_pred_var_t+28', 'rv_t+29', 'y_best_pred_var_t+29', 'rv_t+30', 'y_best_pred_var_t+30', 'rv_t+31', 'y_best_pred_var_t+31', 'rv_t+32', 'y_best_pred_var_t+32', 'rv_t+33', 'y_best_pred_var_t+33', 'rv_t+34', 'y_best_pred_var_t+34', 'rv_t+35', 'y_best_pred_var_t+35', 'rv_t+36', 'y_best_pred_var_t+36', 'rv_t+37', 'y_best_pred_var_t+37', 'rv_t+38', 'y_best_pred_var_t+38', 'rv_t+39', 'y_best_pred_var_t+39', 'rv_t+40', 'y_best_pred_var_t+40', 'rv_t+41', 'y_best_pred_var_t+41', 'rv_t+42', 'y_best_pred_var_t+42', 'rv_t+43', 'y_best_pred_var_t+43', 'rv_t+44', 'y_best_pred_var_t+44', 'rv_t+45', 'y_best_pred_var_t+45', 'rv_t+46', 'y_best_pred_var_t+46', 'rv_t+47', 'y_best_pred_var_t+47', 'rv_t+48', 'y_best_pred_var_t+48', 'rv_t+49', 'y_best_pred_var_t+49', 'rv_t+50', 'y_best_pred_var_t+50', 'rv_t+51', 'y_best_pred_var_t+51', 'rv_t+52', 'y_best_pred_var_t+52', 'rv_t+53', 'y_best_pred_var_t+53', 'rv_t+54', 'y_best_pred_var_t+54', 'rv_t+55', 'y_best_pred_var_t+55', 'rv_t+56', 'y_best_pred_var_t+56', 'rv_t+57', 'y_best_pred_var_t+57', 'rv_t+58', 'y_best_pred_var_t+58', 'rv_t+59', 'y_best_pred_var_t+59', 'rv_t+60', 'y_best_pred_var_t+60']
[X_price] shape=(3782, 60, 2) | NaN: 0 | +Inf: 0 | -Inf: 0 | Any Inf: 0
[y] shape=(3782, 60, 2) | NaN: 0 | +Inf: 0 | -Inf: 0 | Any Inf: 0
feature min p01 median p99 max mean \
0 f1 -3.193956 -3.013444 -1.744387 -0.294217 23.025851 -1.694000
1 f0 0.000096 0.002543 0.151064 1.340520 5.997252 0.220419
std
0 0.709077
1 0.287035
feature min p01 median p99 max mean \
0 f0 0.000096 0.002543 0.151394 1.342547 5.997252 0.221915
1 f1 0.041813 0.049371 0.174895 0.733523 1.309894 0.219821
std
0 0.290001
1 0.145982
Load data v10¶
In [16]:
load_data_object_10_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_10.pkl")
with open(load_data_object_10_file_path, "rb") as f:
structured_data_dict_10 = pickle.load(f)
print("Data dictionary 10 loaded successfully.")
eur10_X_price = structured_data_dict_10["EURUSD"].get("X_other")
eur10_X_time = structured_data_dict_10["EURUSD"].get("X_time")
eur10_y = structured_data_dict_10["EURUSD"].get("y")
eur10_y_columns = structured_data_dict_10["EURUSD"].get("y_columns")
eur10_X_columns = structured_data_dict_10["EURUSD"].get("X_other_columns")
print(eur10_X_columns)
print(eur10_y_columns)
print(eur10_y.shape)
df_xprice = summarize_features(eur10_X_price, eur10_X_columns, name="X_price")
df_y = summarize_features(eur10_y, eur10_y_columns, name="y")
print(df_xprice.head(20))
print(df_y.head(20))
Data dictionary 10 loaded successfully.
['rv_t-59', 'lg_return_t-59', 'rv_t-58', 'lg_return_t-58', 'rv_t-57', 'lg_return_t-57', 'rv_t-56', 'lg_return_t-56', 'rv_t-55', 'lg_return_t-55', 'rv_t-54', 'lg_return_t-54', 'rv_t-53', 'lg_return_t-53', 'rv_t-52', 'lg_return_t-52', 'rv_t-51', 'lg_return_t-51', 'rv_t-50', 'lg_return_t-50', 'rv_t-49', 'lg_return_t-49', 'rv_t-48', 'lg_return_t-48', 'rv_t-47', 'lg_return_t-47', 'rv_t-46', 'lg_return_t-46', 'rv_t-45', 'lg_return_t-45', 'rv_t-44', 'lg_return_t-44', 'rv_t-43', 'lg_return_t-43', 'rv_t-42', 'lg_return_t-42', 'rv_t-41', 'lg_return_t-41', 'rv_t-40', 'lg_return_t-40', 'rv_t-39', 'lg_return_t-39', 'rv_t-38', 'lg_return_t-38', 'rv_t-37', 'lg_return_t-37', 'rv_t-36', 'lg_return_t-36', 'rv_t-35', 'lg_return_t-35', 'rv_t-34', 'lg_return_t-34', 'rv_t-33', 'lg_return_t-33', 'rv_t-32', 'lg_return_t-32', 'rv_t-31', 'lg_return_t-31', 'rv_t-30', 'lg_return_t-30', 'rv_t-29', 'lg_return_t-29', 'rv_t-28', 'lg_return_t-28', 'rv_t-27', 'lg_return_t-27', 'rv_t-26', 'lg_return_t-26', 'rv_t-25', 'lg_return_t-25', 'rv_t-24', 'lg_return_t-24', 'rv_t-23', 'lg_return_t-23', 'rv_t-22', 'lg_return_t-22', 'rv_t-21', 'lg_return_t-21', 'rv_t-20', 'lg_return_t-20', 'rv_t-19', 'lg_return_t-19', 'rv_t-18', 'lg_return_t-18', 'rv_t-17', 'lg_return_t-17', 'rv_t-16', 'lg_return_t-16', 'rv_t-15', 'lg_return_t-15', 'rv_t-14', 'lg_return_t-14', 'rv_t-13', 'lg_return_t-13', 'rv_t-12', 'lg_return_t-12', 'rv_t-11', 'lg_return_t-11', 'rv_t-10', 'lg_return_t-10', 'rv_t-9', 'lg_return_t-9', 'rv_t-8', 'lg_return_t-8', 'rv_t-7', 'lg_return_t-7', 'rv_t-6', 'lg_return_t-6', 'rv_t-5', 'lg_return_t-5', 'rv_t-4', 'lg_return_t-4', 'rv_t-3', 'lg_return_t-3', 'rv_t-2', 'lg_return_t-2', 'rv_t-1', 'lg_return_t-1', 'rv_t-0', 'lg_return_t-0']
['rv_t+1', 'lg_return_t+1', 'rv_t+2', 'lg_return_t+2', 'rv_t+3', 'lg_return_t+3', 'rv_t+4', 'lg_return_t+4', 'rv_t+5', 'lg_return_t+5', 'rv_t+6', 'lg_return_t+6', 'rv_t+7', 'lg_return_t+7', 'rv_t+8', 'lg_return_t+8', 'rv_t+9', 'lg_return_t+9', 'rv_t+10', 'lg_return_t+10', 'rv_t+11', 'lg_return_t+11', 'rv_t+12', 'lg_return_t+12', 'rv_t+13', 'lg_return_t+13', 'rv_t+14', 'lg_return_t+14', 'rv_t+15', 'lg_return_t+15', 'rv_t+16', 'lg_return_t+16', 'rv_t+17', 'lg_return_t+17', 'rv_t+18', 'lg_return_t+18', 'rv_t+19', 'lg_return_t+19', 'rv_t+20', 'lg_return_t+20', 'rv_t+21', 'lg_return_t+21', 'rv_t+22', 'lg_return_t+22', 'rv_t+23', 'lg_return_t+23', 'rv_t+24', 'lg_return_t+24', 'rv_t+25', 'lg_return_t+25', 'rv_t+26', 'lg_return_t+26', 'rv_t+27', 'lg_return_t+27', 'rv_t+28', 'lg_return_t+28', 'rv_t+29', 'lg_return_t+29', 'rv_t+30', 'lg_return_t+30', 'rv_t+31', 'lg_return_t+31', 'rv_t+32', 'lg_return_t+32', 'rv_t+33', 'lg_return_t+33', 'rv_t+34', 'lg_return_t+34', 'rv_t+35', 'lg_return_t+35', 'rv_t+36', 'lg_return_t+36', 'rv_t+37', 'lg_return_t+37', 'rv_t+38', 'lg_return_t+38', 'rv_t+39', 'lg_return_t+39', 'rv_t+40', 'lg_return_t+40', 'rv_t+41', 'lg_return_t+41', 'rv_t+42', 'lg_return_t+42', 'rv_t+43', 'lg_return_t+43', 'rv_t+44', 'lg_return_t+44', 'rv_t+45', 'lg_return_t+45', 'rv_t+46', 'lg_return_t+46', 'rv_t+47', 'lg_return_t+47', 'rv_t+48', 'lg_return_t+48', 'rv_t+49', 'lg_return_t+49', 'rv_t+50', 'lg_return_t+50', 'rv_t+51', 'lg_return_t+51', 'rv_t+52', 'lg_return_t+52', 'rv_t+53', 'lg_return_t+53', 'rv_t+54', 'lg_return_t+54', 'rv_t+55', 'lg_return_t+55', 'rv_t+56', 'lg_return_t+56', 'rv_t+57', 'lg_return_t+57', 'rv_t+58', 'lg_return_t+58', 'rv_t+59', 'lg_return_t+59', 'rv_t+60', 'lg_return_t+60']
(3782, 60, 2)
[X_price] shape=(3782, 60, 2) | NaN: 0 | +Inf: 0 | -Inf: 0 | Any Inf: 0
[y] shape=(3782, 60, 2) | NaN: 0 | +Inf: 0 | -Inf: 0 | Any Inf: 0
feature min p01 median p99 max mean \
0 f0 0.000096 0.002543 0.151064 1.340520 5.997252 0.220419
1 f1 -2.683668 -1.248301 0.002834 1.199063 2.855067 -0.004994
std
0 0.287035
1 0.445894
feature min p01 median p99 max mean \
0 f0 0.000096 0.002543 0.151394 1.342547 5.997252 0.221915
1 f1 -2.683668 -1.246691 0.001969 1.242566 2.855067 -0.003349
std
0 0.290001
1 0.447784
In [17]:
aapl10_X_price = structured_data_dict_10["AAPL"].get("X_other")
aapl10_X_time = structured_data_dict_10["AAPL"].get("X_time")
aapl10_y = structured_data_dict_10["AAPL"].get("y")
aapl10_y_columns = structured_data_dict_10["AAPL"].get("y_columns")
aapl10_X_columns = structured_data_dict_10["AAPL"].get("X_other_columns")
Load data v11¶
In [18]:
load_data_object_11_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_11.pkl")
with open(load_data_object_11_file_path, "rb") as f:
structured_data_dict_11 = pickle.load(f)
print("Data dictionary 11 loaded successfully.")
eur11_X_price = structured_data_dict_11["EURUSD"].get("X_other")
eur11_X_time = structured_data_dict_11["EURUSD"].get("X_time")
eur11_y = structured_data_dict_11["EURUSD"].get("y")
eur11_y_columns = structured_data_dict_11["EURUSD"].get("y_columns")
eur11_X_columns = structured_data_dict_11["EURUSD"].get("X_other_columns")
print(eur11_X_columns)
print(eur11_y_columns)
df_xprice = summarize_features(eur11_X_price, eur11_X_columns, name="X_price")
df_y = summarize_features(eur11_y, eur11_y_columns, name="y")
print(df_xprice.head(20))
print(df_y.head(20))
Data dictionary 11 loaded successfully.
['rv_t-59', 'x_best_alpha_1_t-59', 'x_best_beta_1_t-59', 'x_best_omega_t-59', 'x_best_mu_t-59', 'x_best_nu_tgarch_t-59', 'x_best_pred_var_t-59', 'rv_t-58', 'x_best_alpha_1_t-58', 'x_best_beta_1_t-58', 'x_best_omega_t-58', 'x_best_mu_t-58', 'x_best_nu_tgarch_t-58', 'x_best_pred_var_t-58', 'rv_t-57', 'x_best_alpha_1_t-57', 'x_best_beta_1_t-57', 'x_best_omega_t-57', 'x_best_mu_t-57', 'x_best_nu_tgarch_t-57', 'x_best_pred_var_t-57', 'rv_t-56', 'x_best_alpha_1_t-56', 'x_best_beta_1_t-56', 'x_best_omega_t-56', 'x_best_mu_t-56', 'x_best_nu_tgarch_t-56', 'x_best_pred_var_t-56', 'rv_t-55', 'x_best_alpha_1_t-55', 'x_best_beta_1_t-55', 'x_best_omega_t-55', 'x_best_mu_t-55', 'x_best_nu_tgarch_t-55', 'x_best_pred_var_t-55', 'rv_t-54', 'x_best_alpha_1_t-54', 'x_best_beta_1_t-54', 'x_best_omega_t-54', 'x_best_mu_t-54', 'x_best_nu_tgarch_t-54', 'x_best_pred_var_t-54', 'rv_t-53', 'x_best_alpha_1_t-53', 'x_best_beta_1_t-53', 'x_best_omega_t-53', 'x_best_mu_t-53', 'x_best_nu_tgarch_t-53', 'x_best_pred_var_t-53', 'rv_t-52', 'x_best_alpha_1_t-52', 'x_best_beta_1_t-52', 'x_best_omega_t-52', 'x_best_mu_t-52', 'x_best_nu_tgarch_t-52', 'x_best_pred_var_t-52', 'rv_t-51', 'x_best_alpha_1_t-51', 'x_best_beta_1_t-51', 'x_best_omega_t-51', 'x_best_mu_t-51', 'x_best_nu_tgarch_t-51', 'x_best_pred_var_t-51', 'rv_t-50', 'x_best_alpha_1_t-50', 'x_best_beta_1_t-50', 'x_best_omega_t-50', 'x_best_mu_t-50', 'x_best_nu_tgarch_t-50', 'x_best_pred_var_t-50', 'rv_t-49', 'x_best_alpha_1_t-49', 'x_best_beta_1_t-49', 'x_best_omega_t-49', 'x_best_mu_t-49', 'x_best_nu_tgarch_t-49', 'x_best_pred_var_t-49', 'rv_t-48', 'x_best_alpha_1_t-48', 'x_best_beta_1_t-48', 'x_best_omega_t-48', 'x_best_mu_t-48', 'x_best_nu_tgarch_t-48', 'x_best_pred_var_t-48', 'rv_t-47', 'x_best_alpha_1_t-47', 'x_best_beta_1_t-47', 'x_best_omega_t-47', 'x_best_mu_t-47', 'x_best_nu_tgarch_t-47', 'x_best_pred_var_t-47', 'rv_t-46', 'x_best_alpha_1_t-46', 'x_best_beta_1_t-46', 'x_best_omega_t-46', 'x_best_mu_t-46', 'x_best_nu_tgarch_t-46', 'x_best_pred_var_t-46', 'rv_t-45', 'x_best_alpha_1_t-45', 'x_best_beta_1_t-45', 'x_best_omega_t-45', 'x_best_mu_t-45', 'x_best_nu_tgarch_t-45', 'x_best_pred_var_t-45', 'rv_t-44', 'x_best_alpha_1_t-44', 'x_best_beta_1_t-44', 'x_best_omega_t-44', 'x_best_mu_t-44', 'x_best_nu_tgarch_t-44', 'x_best_pred_var_t-44', 'rv_t-43', 'x_best_alpha_1_t-43', 'x_best_beta_1_t-43', 'x_best_omega_t-43', 'x_best_mu_t-43', 'x_best_nu_tgarch_t-43', 'x_best_pred_var_t-43', 'rv_t-42', 'x_best_alpha_1_t-42', 'x_best_beta_1_t-42', 'x_best_omega_t-42', 'x_best_mu_t-42', 'x_best_nu_tgarch_t-42', 'x_best_pred_var_t-42', 'rv_t-41', 'x_best_alpha_1_t-41', 'x_best_beta_1_t-41', 'x_best_omega_t-41', 'x_best_mu_t-41', 'x_best_nu_tgarch_t-41', 'x_best_pred_var_t-41', 'rv_t-40', 'x_best_alpha_1_t-40', 'x_best_beta_1_t-40', 'x_best_omega_t-40', 'x_best_mu_t-40', 'x_best_nu_tgarch_t-40', 'x_best_pred_var_t-40', 'rv_t-39', 'x_best_alpha_1_t-39', 'x_best_beta_1_t-39', 'x_best_omega_t-39', 'x_best_mu_t-39', 'x_best_nu_tgarch_t-39', 'x_best_pred_var_t-39', 'rv_t-38', 'x_best_alpha_1_t-38', 'x_best_beta_1_t-38', 'x_best_omega_t-38', 'x_best_mu_t-38', 'x_best_nu_tgarch_t-38', 'x_best_pred_var_t-38', 'rv_t-37', 'x_best_alpha_1_t-37', 'x_best_beta_1_t-37', 'x_best_omega_t-37', 'x_best_mu_t-37', 'x_best_nu_tgarch_t-37', 'x_best_pred_var_t-37', 'rv_t-36', 'x_best_alpha_1_t-36', 'x_best_beta_1_t-36', 'x_best_omega_t-36', 'x_best_mu_t-36', 'x_best_nu_tgarch_t-36', 'x_best_pred_var_t-36', 'rv_t-35', 'x_best_alpha_1_t-35', 'x_best_beta_1_t-35', 'x_best_omega_t-35', 'x_best_mu_t-35', 'x_best_nu_tgarch_t-35', 'x_best_pred_var_t-35', 'rv_t-34', 'x_best_alpha_1_t-34', 'x_best_beta_1_t-34', 'x_best_omega_t-34', 'x_best_mu_t-34', 'x_best_nu_tgarch_t-34', 'x_best_pred_var_t-34', 'rv_t-33', 'x_best_alpha_1_t-33', 'x_best_beta_1_t-33', 'x_best_omega_t-33', 'x_best_mu_t-33', 'x_best_nu_tgarch_t-33', 'x_best_pred_var_t-33', 'rv_t-32', 'x_best_alpha_1_t-32', 'x_best_beta_1_t-32', 'x_best_omega_t-32', 'x_best_mu_t-32', 'x_best_nu_tgarch_t-32', 'x_best_pred_var_t-32', 'rv_t-31', 'x_best_alpha_1_t-31', 'x_best_beta_1_t-31', 'x_best_omega_t-31', 'x_best_mu_t-31', 'x_best_nu_tgarch_t-31', 'x_best_pred_var_t-31', 'rv_t-30', 'x_best_alpha_1_t-30', 'x_best_beta_1_t-30', 'x_best_omega_t-30', 'x_best_mu_t-30', 'x_best_nu_tgarch_t-30', 'x_best_pred_var_t-30', 'rv_t-29', 'x_best_alpha_1_t-29', 'x_best_beta_1_t-29', 'x_best_omega_t-29', 'x_best_mu_t-29', 'x_best_nu_tgarch_t-29', 'x_best_pred_var_t-29', 'rv_t-28', 'x_best_alpha_1_t-28', 'x_best_beta_1_t-28', 'x_best_omega_t-28', 'x_best_mu_t-28', 'x_best_nu_tgarch_t-28', 'x_best_pred_var_t-28', 'rv_t-27', 'x_best_alpha_1_t-27', 'x_best_beta_1_t-27', 'x_best_omega_t-27', 'x_best_mu_t-27', 'x_best_nu_tgarch_t-27', 'x_best_pred_var_t-27', 'rv_t-26', 'x_best_alpha_1_t-26', 'x_best_beta_1_t-26', 'x_best_omega_t-26', 'x_best_mu_t-26', 'x_best_nu_tgarch_t-26', 'x_best_pred_var_t-26', 'rv_t-25', 'x_best_alpha_1_t-25', 'x_best_beta_1_t-25', 'x_best_omega_t-25', 'x_best_mu_t-25', 'x_best_nu_tgarch_t-25', 'x_best_pred_var_t-25', 'rv_t-24', 'x_best_alpha_1_t-24', 'x_best_beta_1_t-24', 'x_best_omega_t-24', 'x_best_mu_t-24', 'x_best_nu_tgarch_t-24', 'x_best_pred_var_t-24', 'rv_t-23', 'x_best_alpha_1_t-23', 'x_best_beta_1_t-23', 'x_best_omega_t-23', 'x_best_mu_t-23', 'x_best_nu_tgarch_t-23', 'x_best_pred_var_t-23', 'rv_t-22', 'x_best_alpha_1_t-22', 'x_best_beta_1_t-22', 'x_best_omega_t-22', 'x_best_mu_t-22', 'x_best_nu_tgarch_t-22', 'x_best_pred_var_t-22', 'rv_t-21', 'x_best_alpha_1_t-21', 'x_best_beta_1_t-21', 'x_best_omega_t-21', 'x_best_mu_t-21', 'x_best_nu_tgarch_t-21', 'x_best_pred_var_t-21', 'rv_t-20', 'x_best_alpha_1_t-20', 'x_best_beta_1_t-20', 'x_best_omega_t-20', 'x_best_mu_t-20', 'x_best_nu_tgarch_t-20', 'x_best_pred_var_t-20', 'rv_t-19', 'x_best_alpha_1_t-19', 'x_best_beta_1_t-19', 'x_best_omega_t-19', 'x_best_mu_t-19', 'x_best_nu_tgarch_t-19', 'x_best_pred_var_t-19', 'rv_t-18', 'x_best_alpha_1_t-18', 'x_best_beta_1_t-18', 'x_best_omega_t-18', 'x_best_mu_t-18', 'x_best_nu_tgarch_t-18', 'x_best_pred_var_t-18', 'rv_t-17', 'x_best_alpha_1_t-17', 'x_best_beta_1_t-17', 'x_best_omega_t-17', 'x_best_mu_t-17', 'x_best_nu_tgarch_t-17', 'x_best_pred_var_t-17', 'rv_t-16', 'x_best_alpha_1_t-16', 'x_best_beta_1_t-16', 'x_best_omega_t-16', 'x_best_mu_t-16', 'x_best_nu_tgarch_t-16', 'x_best_pred_var_t-16', 'rv_t-15', 'x_best_alpha_1_t-15', 'x_best_beta_1_t-15', 'x_best_omega_t-15', 'x_best_mu_t-15', 'x_best_nu_tgarch_t-15', 'x_best_pred_var_t-15', 'rv_t-14', 'x_best_alpha_1_t-14', 'x_best_beta_1_t-14', 'x_best_omega_t-14', 'x_best_mu_t-14', 'x_best_nu_tgarch_t-14', 'x_best_pred_var_t-14', 'rv_t-13', 'x_best_alpha_1_t-13', 'x_best_beta_1_t-13', 'x_best_omega_t-13', 'x_best_mu_t-13', 'x_best_nu_tgarch_t-13', 'x_best_pred_var_t-13', 'rv_t-12', 'x_best_alpha_1_t-12', 'x_best_beta_1_t-12', 'x_best_omega_t-12', 'x_best_mu_t-12', 'x_best_nu_tgarch_t-12', 'x_best_pred_var_t-12', 'rv_t-11', 'x_best_alpha_1_t-11', 'x_best_beta_1_t-11', 'x_best_omega_t-11', 'x_best_mu_t-11', 'x_best_nu_tgarch_t-11', 'x_best_pred_var_t-11', 'rv_t-10', 'x_best_alpha_1_t-10', 'x_best_beta_1_t-10', 'x_best_omega_t-10', 'x_best_mu_t-10', 'x_best_nu_tgarch_t-10', 'x_best_pred_var_t-10', 'rv_t-9', 'x_best_alpha_1_t-9', 'x_best_beta_1_t-9', 'x_best_omega_t-9', 'x_best_mu_t-9', 'x_best_nu_tgarch_t-9', 'x_best_pred_var_t-9', 'rv_t-8', 'x_best_alpha_1_t-8', 'x_best_beta_1_t-8', 'x_best_omega_t-8', 'x_best_mu_t-8', 'x_best_nu_tgarch_t-8', 'x_best_pred_var_t-8', 'rv_t-7', 'x_best_alpha_1_t-7', 'x_best_beta_1_t-7', 'x_best_omega_t-7', 'x_best_mu_t-7', 'x_best_nu_tgarch_t-7', 'x_best_pred_var_t-7', 'rv_t-6', 'x_best_alpha_1_t-6', 'x_best_beta_1_t-6', 'x_best_omega_t-6', 'x_best_mu_t-6', 'x_best_nu_tgarch_t-6', 'x_best_pred_var_t-6', 'rv_t-5', 'x_best_alpha_1_t-5', 'x_best_beta_1_t-5', 'x_best_omega_t-5', 'x_best_mu_t-5', 'x_best_nu_tgarch_t-5', 'x_best_pred_var_t-5', 'rv_t-4', 'x_best_alpha_1_t-4', 'x_best_beta_1_t-4', 'x_best_omega_t-4', 'x_best_mu_t-4', 'x_best_nu_tgarch_t-4', 'x_best_pred_var_t-4', 'rv_t-3', 'x_best_alpha_1_t-3', 'x_best_beta_1_t-3', 'x_best_omega_t-3', 'x_best_mu_t-3', 'x_best_nu_tgarch_t-3', 'x_best_pred_var_t-3', 'rv_t-2', 'x_best_alpha_1_t-2', 'x_best_beta_1_t-2', 'x_best_omega_t-2', 'x_best_mu_t-2', 'x_best_nu_tgarch_t-2', 'x_best_pred_var_t-2', 'rv_t-1', 'x_best_alpha_1_t-1', 'x_best_beta_1_t-1', 'x_best_omega_t-1', 'x_best_mu_t-1', 'x_best_nu_tgarch_t-1', 'x_best_pred_var_t-1', 'rv_t-0', 'x_best_alpha_1_t-0', 'x_best_beta_1_t-0', 'x_best_omega_t-0', 'x_best_mu_t-0', 'x_best_nu_tgarch_t-0', 'x_best_pred_var_t-0']
['rv_t+1', 'rv_t+2', 'rv_t+3', 'rv_t+4', 'rv_t+5', 'rv_t+6', 'rv_t+7', 'rv_t+8', 'rv_t+9', 'rv_t+10', 'rv_t+11', 'rv_t+12', 'rv_t+13', 'rv_t+14', 'rv_t+15', 'rv_t+16', 'rv_t+17', 'rv_t+18', 'rv_t+19', 'rv_t+20', 'rv_t+21', 'rv_t+22', 'rv_t+23', 'rv_t+24', 'rv_t+25', 'rv_t+26', 'rv_t+27', 'rv_t+28', 'rv_t+29', 'rv_t+30', 'rv_t+31', 'rv_t+32', 'rv_t+33', 'rv_t+34', 'rv_t+35', 'rv_t+36', 'rv_t+37', 'rv_t+38', 'rv_t+39', 'rv_t+40', 'rv_t+41', 'rv_t+42', 'rv_t+43', 'rv_t+44', 'rv_t+45', 'rv_t+46', 'rv_t+47', 'rv_t+48', 'rv_t+49', 'rv_t+50', 'rv_t+51', 'rv_t+52', 'rv_t+53', 'rv_t+54', 'rv_t+55', 'rv_t+56', 'rv_t+57', 'rv_t+58', 'rv_t+59', 'rv_t+60']
[X_price] shape=(3782, 60, 7) | NaN: 0 | +Inf: 0 | -Inf: 0 | Any Inf: 0
[y] shape=(3782, 60, 1) | NaN: 0 | +Inf: 0 | -Inf: 0 | Any Inf: 0
feature min p01 median p99 max mean \
0 f5 0.000000 3.031849e+00 5.034044 106.962327 345.033304 7.723787
1 f6 -3.193956 -3.013444e+00 -1.744387 -0.294217 23.025851 -1.694000
2 f0 0.000096 2.542676e-03 0.151064 1.340520 5.997252 0.220419
3 f2 0.000000 0.000000e+00 0.954145 1.000000 1.000000 0.817385
4 f3 0.000000 9.629686e-10 0.003832 0.219574 0.424595 0.028887
5 f1 0.000000 0.000000e+00 0.006112 0.136720 0.205071 0.024899
6 f4 -0.071164 -6.033247e-02 -0.002668 0.037148 0.043256 -0.003317
std
0 18.160206
1 0.709077
2 0.287035
3 0.315141
4 0.052974
5 0.034081
6 0.020569
feature min p01 median p99 max mean \
0 f0 0.000096 0.002543 0.151394 1.342547 5.997252 0.221915
std
0 0.290001
GARCH baseline¶
GARCH wrapper¶
In [26]:
import warnings
from typing import Dict, List, Optional, Sequence, Tuple
import numpy as np
from arch import arch_model
def _norm_dist_name(d):
d = str(d).lower()
aliases = {"gaussian": "normal", "norm": "normal", "student": "t", "studentt": "t"}
return aliases.get(d, d)
def _parse_cfg(cfg: Sequence):
if len(cfg) == 4:
mt, p, q, dist = cfg
return str(mt).upper(), int(p), 0, int(q), _norm_dist_name(dist)
elif len(cfg) == 5:
mt, p, o, q, dist = cfg
return str(mt).upper(), int(p), int(o), int(q), _norm_dist_name(dist)
raise ValueError(f"cfg must be len 4 or 5; got {cfg}")
class VolBICWrapper:
def __init__(self, mean_mode: str = "Zero", verbose: bool = False,
candidates: Optional[List[Sequence]] = None):
self.candidates = candidates or [
("GARCH", 1, 1, "t"),
("GARCH", 2, 2, "t"),
("GARCH", 1, 1, "normal"),
("GJRGARCH", 1, 1, "t"),
("GJRGARCH", 1, 1, "normal"),
("EGARCH", 1, 1, "t"),
("EGARCH", 1, 1, 1, "t"),
("EGARCH", 1, 1, "normal"),
]
self.mean_mode = mean_mode
self.verbose = verbose
self.best_config: Optional[Tuple] = None
self.best_result = None
self.best_params: Optional[Dict] = None
self.best_bic: float = np.inf
def _build(self, x: np.ndarray, cfg: Sequence):
mt, p, o, q, dist = _parse_cfg(cfg)
mean_str = str(self.mean_mode).lower()
if mt == "GARCH":
return arch_model(x, mean=mean_str, vol="GARCH", p=p, q=q, dist=dist)
if mt in ("GJRGARCH", "GJR-GARCH", "GJR"):
o_ = 1 if o == 0 else o
return arch_model(x, mean=mean_str, vol="GARCH", p=p, o=o_, q=q, dist=dist)
if mt == "ARCH":
return arch_model(x, mean=mean_str, vol="ARCH", p=p, dist=dist)
if mt == "EGARCH":
o_ = 1 if o == 0 else o
return arch_model(x, mean=mean_str, vol="EGARCH", p=p, o=o_, q=q, dist=dist)
raise ValueError(f"Unknown model type: {mt}")
def _check_fitted(self):
if self.best_result is None or self.best_config is None or self.best_params is None:
raise RuntimeError("Model is not fitted. Call fit(x_fit) first.")
def fit(self, x_fit: np.ndarray):
x = np.asarray(x_fit, dtype=float).ravel()
if x.size < 20:
raise ValueError("x_fit is too short; need at least ~20 obs.")
best_res, best_cfg, best_bic = None, None, np.inf
for cfg in self.candidates:
try:
with warnings.catch_warnings():
warnings.simplefilter("ignore")
m = self._build(x, cfg)
res = m.fit(disp="off")
bic = float(res.bic)
if np.isfinite(bic) and bic < best_bic:
best_res, best_cfg, best_bic = res, cfg, bic
except Exception as e:
if self.verbose:
print(f"[fit] {cfg} failed: {e}")
if best_res is None:
raise RuntimeError("All candidate models failed to fit.")
self.best_result = best_res
self.best_config = best_cfg
self.best_bic = best_bic
self.best_params = best_res.params.to_dict()
if self.verbose:
print(f"[fit] Best by BIC: {self.best_config}, BIC={self.best_bic:.3f}")
return self
def _forecast_variance(self, res, H: int, sim_paths: int = 0, random_state: Optional[int] = None):
try:
fc = res.forecast(horizon=int(H), reindex=False)
return np.asarray(fc.variance.values[-1, :H], dtype=float)
except Exception as e:
if self.verbose:
print(f"[forecast] analytic failed ({e}); trying simulation…")
paths = int(sim_paths) if sim_paths and sim_paths > 0 else 1000
last_err = None
for kwargs in (
dict(method="simulation", simulations=paths, reindex=False, random_state=random_state),
dict(method="simulation", simulations=paths, reindex=False),
dict(method="simulation", reps=paths, reindex=False),
):
try:
fc = res.forecast(horizon=int(H), **kwargs)
return np.asarray(fc.variance.values[-1, :H], dtype=float)
except Exception as e:
last_err = e
raise RuntimeError(f"simulation forecast failed: {last_err}")
def forecast_from_fit(self, steps: int, *, sim_paths: int = 0, random_state: Optional[int] = None) -> np.ndarray:
self._check_fitted()
return self._forecast_variance(self.best_result, steps, sim_paths=sim_paths, random_state=random_state)
def forecast_on_new(self, x_new: np.ndarray, steps: int, *, sim_paths: int = 0, random_state: Optional[int] = None) -> np.ndarray:
self._check_fitted()
x_new = np.asarray(x_new, dtype=float).ravel()
with warnings.catch_warnings():
warnings.simplefilter("ignore")
m_new = self._build(x_new, self.best_config)
res_fixed = m_new.fix(self.best_result.params)
return self._forecast_variance(res_fixed, steps, sim_paths=sim_paths, random_state=random_state)
def forecast_panel_on_new(self, x_new: np.ndarray, steps: int, start_anchor: int,
*, sim_paths: int = 0, random_state: Optional[int] = None) -> np.ndarray:
self._check_fitted()
x_new = np.asarray(x_new, dtype=float).ravel()
H = int(steps); T = x_new.shape[0]
if start_anchor < 0 or start_anchor >= T:
raise ValueError(f"start_anchor must be in [0, {T-1}]")
with warnings.catch_warnings():
warnings.simplefilter("ignore")
m_new = self._build(x_new, self.best_config)
res_fixed = m_new.fix(self.best_result.params)
try:
fc = res_fixed.forecast(horizon=H, start=start_anchor, reindex=False)
arr = np.asarray(fc.variance.values, dtype=float)
if arr.shape[0] > (T - start_anchor):
arr = arr[-(T - start_anchor):, :]
return arr[:, :H]
except Exception:
pass
for kwargs in (
dict(method="simulation", simulations=(sim_paths or 1000), start=start_anchor, reindex=False, random_state=random_state),
dict(method="simulation", simulations=(sim_paths or 1000), start=start_anchor, reindex=False),
dict(method="simulation", reps=(sim_paths or 1000), start=start_anchor, reindex=False),
):
try:
fc = res_fixed.forecast(horizon=H, **kwargs)
arr = np.asarray(fc.variance.values, dtype=float)
if arr.shape[0] > (T - start_anchor):
arr = arr[-(T - start_anchor):, :]
return arr[:, :H]
except Exception:
continue
n_anchors = T - start_anchor
out = np.empty((n_anchors, H), dtype=float)
for k, t in enumerate(range(start_anchor, T)):
sub = x_new[:t + 1]
with warnings.catch_warnings():
warnings.simplefilter("ignore")
m_t = self._build(sub, self.best_config)
res_t = m_t.fix(self.best_result.params)
out[k, :] = self._forecast_variance(res_t, H, sim_paths=sim_paths, random_state=random_state)
return out
def get_best_config(self):
return self.best_config
def get_best_params(self) -> Dict:
return dict(self.best_params) if self.best_params is not None else {}
def state_dict(self) -> Dict:
return {
"mean_mode": str(self.mean_mode).lower(),
"best_config": self.best_config,
"best_params": self.get_best_params(),
"best_bic": float(self.best_bic),
}
GARCH evaluator¶
In [27]:
import numpy as np
import pandas as pd
def evaluate_vol_on_anchors_rolling_split(
X_anchors,
Y_fwd,
horizon,
*,
split_ratio=0.8,
roll_window=250,
mean_mode="Zero",
candidates=None,
sim_paths=2000,
horizons=(1, 3, 5, 10, 20, -1),
epsilon=1e-12,
verbose=True,
pretty_print=True,
qlike_mode="ratio",
qlike_floor="auto",
qlike_calibrate=True,
):
from sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score
x = np.asarray(X_anchors, dtype=float).ravel()
Y = np.asarray(Y_fwd, dtype=float)
if x.ndim != 1:
raise ValueError("X_anchors must be 1D (B,).")
if Y.ndim != 2:
raise ValueError("Y_fwd must be 2D (B, T_fwd).")
B = x.shape[0]
if Y.shape[0] != B:
raise ValueError(f"len(X)={B} != len(Y)={Y.shape[0]}.")
if horizon < 1:
raise ValueError("horizon must be >= 1.")
if Y.shape[1] < horizon:
raise ValueError("Y_fwd too short for horizon.")
if roll_window < 20:
raise ValueError("roll_window too small.")
if roll_window > B:
raise ValueError("roll_window > sample size.")
n_train = max(20, int(np.floor(split_ratio * B)))
if n_train >= B:
raise ValueError(f"split_ratio too large: n_train={n_train} >= B={B}.")
t0 = max(n_train - 1, roll_window - 1)
n_oos = B - t0
if verbose:
print(f"[vol rolling] B={B}, split={split_ratio} -> n_train={n_train}, "
f"t0={t0}, anchors={n_oos}, H={horizon}, roll_window={roll_window}")
y_pred = np.full((n_oos, horizon), np.nan, dtype=float)
y_true = Y[t0:, :horizon].astype(float)
def _auto_floor(arr):
arr = np.asarray(arr, dtype=np.float64)
pos = arr[np.isfinite(arr) & (arr > 0)]
if pos.size == 0:
return float(epsilon)
base = np.percentile(pos, 5.0)
return float(max(epsilon, 1e-6 * base))
def _metric_block(yt_raw, yp_raw):
mask = np.isfinite(yt_raw) & np.isfinite(yp_raw)
if not np.any(mask):
return np.nan, np.nan, np.nan, np.nan, np.nan
yt = yt_raw[mask].astype(np.float64)
yp = yp_raw[mask].astype(np.float64)
mae = mean_absolute_error(yt, yp)
rmse = mean_squared_error(yt, yp) ** 0.5
r2 = r2_score(yt, yp)
ytm, ypm = yt - yt.mean(), yp - yp.mean()
denom = np.sqrt((ytm**2).mean() * (ypm**2).mean())
pr = float((ytm*ypm).mean() / denom) if denom > epsilon else np.nan
floor = _auto_floor(yt) if qlike_floor == "auto" else max(float(qlike_floor), float(epsilon))
yt_pos = np.clip(yt, floor, None)
yp_pos = np.clip(yp, floor, None)
if qlike_calibrate:
c = float(np.mean(yt_pos / yp_pos))
yp_pos = np.clip(yp_pos * c, floor, None)
if qlike_mode == "ratio":
r = yt_pos / yp_pos
qlike = float(np.mean(r - np.log(r) - 1.0))
elif qlike_mode == "log":
qlike = float(np.mean(np.log(yp_pos) + (yt_pos / yp_pos)))
else:
raise ValueError("qlike_mode must be 'ratio' or 'log'")
return float(mae), float(rmse), float(r2), pr, qlike
for k, t in enumerate(range(t0, B)):
left = t - roll_window + 1
win = x[left : t + 1]
mu = float(np.mean(win))
sd = float(np.std(win))
if not np.isfinite(sd) or sd <= 0:
sd = 1.0
win_std = (win - mu) / sd
try:
vw = VolBICWrapper(mean_mode=mean_mode, verbose=False, candidates=candidates).fit(win_std)
v_std = vw.forecast_from_fit(horizon, sim_paths=sim_paths)
y_pred[k, :] = (sd ** 2) * np.asarray(v_std, dtype=float)
except Exception as e:
if verbose:
print(f" anchor {t}: fit/forecast failed ({e}); skipping")
if verbose and (k % max(1, n_oos // 5) == 0):
show = y_pred[k, 0] if np.isfinite(y_pred[k, 0]) else np.nan
print(f" anchor {t}/{B-1}: sd={sd:.6g}, h1_pred={show:.6g}")
results = {}
max_h = horizon
for h in horizons:
label = f"{h} day(s)" if h > 0 else "full horizon"
h_slice = h if h > 0 else max_h
if h_slice > max_h:
continue
yt_slice = y_true[:, :h_slice].ravel()
yp_slice = y_pred[:, :h_slice].ravel()
mae, rmse, r2, pr, qlike = _metric_block(yt_slice, yp_slice)
results[f"{label} MAE"] = mae
results[f"{label} RMSE"] = rmse
results[f"{label} R2"] = r2
results[f"{label} Pearson r"] = pr
results[f"{label} QLIKE"] = qlike
anchors = np.arange(t0, B, dtype=int)
Hcol = np.arange(1, horizon + 1, dtype=int)
A, Hh = np.meshgrid(anchors, Hcol, indexing="ij")
panel_df = pd.DataFrame({
"anchor": A.ravel(),
"horizon": Hh.ravel(),
"y_true": y_true.ravel(),
"y_pred": np.clip(y_pred, epsilon, None).ravel(),
})
if verbose and pretty_print:
metric_order = ["MAE", "RMSE", "R2", "Pearson r", "QLIKE"]
labels = [f"{h} day(s)" if h > 0 else "full horizon"
for h in horizons if (h > 0 and h <= max_h) or h == -1]
print("\n[metrics — vol rolling (ML-aligned split)]")
for lbl in labels:
print(f"\n{lbl}")
for m in metric_order:
key = f"{lbl} {m}"
val = results.get(key, np.nan)
print(f"{m:<10}: {val:.6f}" if np.isfinite(val) else f"{m:<10}: {val}")
y_pair = np.stack([y_true, np.clip(y_pred, epsilon, None)], axis=-1)
return results, None, None, panel_df, y_pair
EURUSD GARCH Test¶
GARCH data¶
In [29]:
print(eur10_X_price.shape)
anchor_feature_idx = 1
X_garch = eur10_X_price[:, 1, anchor_feature_idx]
print(X_garch.shape)
print(eur10_y.shape)
y_garch = eur10_y[:, :, 1]
print(y_garch.shape)
(3782, 60, 2) (3782,) (3782, 60, 2) (3782, 60)
GARCH evaluation¶
In [ ]:
H=20
res, _, _, panel, y_pair = evaluate_vol_on_anchors_rolling_split(
X_anchors=X_garch,
Y_fwd=y_garch,
horizon=H,
split_ratio=0.8,
roll_window=250,
mean_mode="zero",
candidates=[("GARCH", 1, 1, "t")],
sim_paths=2000,
verbose=True
)
[vol rolling] B=3782, split=0.8 -> n_train=3025, t0=3024, anchors=758, H=20, roll_window=250 anchor 3024/3781: sd=0.504062, h1_pred=0.343572 anchor 3175/3781: sd=0.589105, h1_pred=0.384396 anchor 3326/3781: sd=0.432523, h1_pred=0.155239 anchor 3477/3781: sd=0.369827, h1_pred=0.144132 anchor 3628/3781: sd=0.336784, h1_pred=0.108408 anchor 3779/3781: sd=0.372649, h1_pred=0.214029 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.357293 RMSE : 0.471094 R2 : -0.261556 Pearson r : 0.027083 QLIKE : 7.959451 3 day(s) MAE : 0.356553 RMSE : 0.470698 R2 : -0.262371 Pearson r : 0.032715 QLIKE : 7.951059 5 day(s) MAE : 0.356405 RMSE : 0.470518 R2 : -0.267211 Pearson r : 0.033994 QLIKE : 7.955660 10 day(s) MAE : 0.357679 RMSE : 0.472385 R2 : -0.279290 Pearson r : 0.027797 QLIKE : 7.978474 20 day(s) MAE : 0.360014 RMSE : 0.476171 R2 : -0.295258 Pearson r : 0.019077 QLIKE : 7.995353 full horizon MAE : 0.360014 RMSE : 0.476171 R2 : -0.295258 Pearson r : 0.019077 QLIKE : 7.995353
GARCH APPL10 data¶
In [40]:
print(aapl10_X_price.shape)
X_garch = aapl10_X_price[:, 55, 1]
print(X_garch.shape)
print(aapl10_y.shape)
y_garch = aapl10_y[:, :, 1]
print(y_garch.shape)
(2018, 60, 2) (2018,) (2018, 60, 2) (2018, 60)
GARCH APPL10 test¶
In [41]:
H=20
res, _, _, panel, y_pair = evaluate_vol_on_anchors_rolling_split(
X_anchors=X_garch,
Y_fwd=y_garch,
horizon=H,
split_ratio=0.8,
roll_window=200,
mean_mode="zero",
candidates=[("GARCH", 1, 1, "t")],
sim_paths=2000,
verbose=True
)
[vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=20, roll_window=200 anchor 1613/2017: sd=1.59174, h1_pred=1.16246 anchor 1694/2017: sd=1.19384, h1_pred=1.37501 anchor 1775/2017: sd=1.2563, h1_pred=2.00446 anchor 1856/2017: sd=1.46078, h1_pred=2.26103 anchor 1937/2017: sd=1.46966, h1_pred=1.93182 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.194304 RMSE : 2.857805 R2 : -1.652279 Pearson r : 0.006932 QLIKE : 7.205541 3 day(s) MAE : 2.163011 RMSE : 2.763339 R2 : -1.474063 Pearson r : 0.030918 QLIKE : 7.200926 5 day(s) MAE : 2.157527 RMSE : 2.761844 R2 : -1.462521 Pearson r : -0.000670 QLIKE : 7.219132 10 day(s) MAE : 2.142096 RMSE : 2.736135 R2 : -1.409446 Pearson r : -0.004432 QLIKE : 7.215343 20 day(s) MAE : 2.129420 RMSE : 2.719979 R2 : -1.367259 Pearson r : -0.018326 QLIKE : 7.182576 full horizon MAE : 2.129420 RMSE : 2.719979 R2 : -1.367259 Pearson r : -0.018326 QLIKE : 7.182576
GARCH Predicted data test¶
In [ ]:
y_pair
Out[ ]:
array([[[ 0.11717087, 1.16245878],
[ 0.84276862, 1.16074113],
[ 0.12659565, 1.15905942],
...,
[-2.36765456, 1.13767022],
[-0.89372364, 1.13647119],
[ 0.15243905, 1.13529724]],
[[ 0.84276862, 1.17721259],
[ 0.12659565, 1.17586587],
[-3.64496484, 1.17454993],
...,
[-0.89372364, 1.15806247],
[ 0.15243905, 1.15715348],
[ 0.30418274, 1.15626527]],
[[ 0.12659565, 1.17326081],
[-3.64496484, 1.17214911],
[-2.96856486, 1.1710646 ],
...,
[ 0.15243905, 1.15764422],
[ 0.30418274, 1.15691451],
[ 1.47266112, 1.15620266]],
...,
[[14.26174109, 2.42282546],
[-4.33186128, 2.41168568],
[ 3.97921608, 2.40112041],
...,
[-3.19639323, 2.29463115],
[-0.19124314, 2.29010283],
[-1.145012 , 2.28580805]],
[[-4.33186128, 6.6472081 ],
[ 3.97921608, 6.5599066 ],
[ 2.18143275, 6.47474973],
...,
[-0.19124314, 5.42181881],
[-1.145012 , 5.36461985],
[ 0.62985935, 5.30882602]],
[[ 3.97921608, 12.92110991],
[ 2.18143275, 12.6706829 ],
[-0.18781205, 12.42726777],
...,
[-1.145012 , 9.49614457],
[ 0.62985935, 9.34161567],
[ 0.52522721, 9.19141355]]], shape=(405, 20, 2))
NN model architectures¶
MLP models¶
Vanila MLP¶
Vanilla MLP architecture¶
In [119]:
import torch
import torch.nn as nn
class SimpleMLP(nn.Module):
def __init__(self, input_dim, output_dim, hidden_layers=6, hidden_dim=64, dropout=0.0, no_tasks=1):
super().__init__()
layers = []
layers.append(nn.Linear(input_dim, hidden_dim))
layers.append(nn.LayerNorm(hidden_dim))
layers.append(nn.GELU())
layers.append(nn.Dropout(dropout))
for _ in range(hidden_layers):
layers.append(nn.Linear(hidden_dim, hidden_dim))
layers.append(nn.LayerNorm(hidden_dim))
layers.append(nn.GELU())
layers.append(nn.Dropout(dropout))
layers.append(nn.Linear(hidden_dim, output_dim))
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
Vanilla MLP wrapper¶
In [120]:
from sklearn.base import BaseEstimator, RegressorMixin
import math
import torch
import torch.nn as nn
from torch.optim import Adam
class SimpleMLPWrapper(BaseEstimator, RegressorMixin):
def __init__(
self,
input_dim,
output_dim,
*,
dropout=0.0,
lr=3e-4,
epochs=50,
batch_size=64,
device=None,
verbose=True,
hidden_layers=3,
no_tasks=1,
l2_weight=1e-6,
patience=10,
min_epochs=30,
min_delta=1e-4,
hidden_dim=256,
loss_type="gauss_nll_var",
target_is_logvar=False,
nll_eps=1e-12,
clamp_logvar_min=-20.0,
clamp_logvar_max=20.0,
student_df=5.0,
):
self.input_dim = int(input_dim)
self.output_dim = int(output_dim)
self.dropout = float(dropout)
self.lr = float(lr)
self.epochs = int(epochs)
self.batch_size = int(batch_size)
self.device = device or (
"mps" if torch.backends.mps.is_available()
else "cuda" if torch.cuda.is_available()
else "cpu"
)
self.verbose = bool(verbose)
self.hidden_layers = int(hidden_layers)
self.no_tasks = int(no_tasks)
self.l2_weight = float(l2_weight)
self.patience = int(patience)
self.min_epochs = int(min_epochs)
self.min_delta = float(min_delta)
self.hidden_dim = int(hidden_dim)
self.loss_type = str(loss_type)
self.target_is_logvar = bool(target_is_logvar)
self.nll_eps = float(nll_eps)
self.clamp_logvar_min = float(clamp_logvar_min)
self.clamp_logvar_max = float(clamp_logvar_max)
self.student_df = float(student_df)
self._build()
if self.verbose:
print(f"Using device: {self.device}")
def _build(self):
self.model = SimpleMLP(
input_dim=self.input_dim,
output_dim=self.output_dim,
hidden_layers=self.hidden_layers,
hidden_dim=self.hidden_dim,
dropout=self.dropout,
no_tasks=self.no_tasks,
).to(self.device)
self.optimizer = Adam(self.model.parameters(), lr=self.lr, weight_decay=self.l2_weight)
self._mse = nn.MSELoss()
self._extras = {}
def _batches(self, X, y, bs):
n = X.shape[0]
for i in range(0, n, bs):
yield X[i:i + bs], y[i:i + bs]
def _compute_loss(self, y_hat, y_true):
if self.loss_type == "mse":
return self._mse(y_hat, y_true)
z = torch.clamp(y_hat, self.clamp_logvar_min, self.clamp_logvar_max)
if self.loss_type == "gauss_nll_var":
if self.target_is_logvar:
v = torch.exp(y_true)
else:
v = y_true
v = torch.clamp(v, min=self.nll_eps)
loss = v * torch.exp(-z) + z
return loss.mean()
elif self.loss_type == "student_t_nll_var":
if self.target_is_logvar:
v = torch.exp(y_true)
else:
v = y_true
v = torch.clamp(v, min=self.nll_eps)
nu = torch.tensor(self.student_df, device=z.device, dtype=z.dtype)
loss = 0.5*(nu + 1.0) * torch.log1p(v / (nu * torch.exp(z))) + 0.5*z
return loss.mean()
else:
raise ValueError(f"Unknown loss_type: {self.loss_type}")
def fit(self, X_train, y_train, X_val=None, y_val=None):
X_train = torch.as_tensor(X_train, dtype=torch.float32, device=self.device)
y_train = torch.as_tensor(y_train, dtype=torch.float32, device=self.device)
if X_val is not None:
X_val = torch.as_tensor(X_val, dtype=torch.float32, device=self.device)
y_val = torch.as_tensor(y_val, dtype=torch.float32, device=self.device)
best_val = math.inf
best_state = None
epochs_no_improve = 0
for epoch in range(1, self.epochs + 1):
self.model.train()
train_loss = 0.0
nb = 0
for xb, yb in self._batches(X_train, y_train, self.batch_size):
self.optimizer.zero_grad(set_to_none=True)
y_hat = self.model(xb)
loss = self._compute_loss(y_hat, yb)
loss.backward()
self.optimizer.step()
train_loss += float(loss.detach().cpu())
nb += 1
train_loss /= max(1, nb)
if X_val is not None:
self.model.eval()
with torch.no_grad():
y_hat_v = self.model(X_val)
val_loss = float(self._compute_loss(y_hat_v, y_val).detach().cpu())
if self.verbose:
print(f"Epoch {epoch}: Train {self.loss_type} = {train_loss:.4f} | "
f"Val {self.loss_type} = {val_loss:.4f}")
improved = (best_val - val_loss) > self.min_delta
if improved:
best_val = val_loss
epochs_no_improve = 0
best_state = {k: v.detach().cpu() for k, v in self.model.state_dict().items()}
else:
epochs_no_improve += 1
if (epoch >= self.min_epochs) and (epochs_no_improve >= self.patience):
if self.verbose:
print(f"Early stopping triggered at epoch {epoch}.")
break
else:
if self.verbose:
print(f"Epoch {epoch}: Train {self.loss_type} = {train_loss:.4f}")
if best_state is not None:
self.model.load_state_dict(best_state)
return self
def predict(self, X):
self.model.eval()
X = torch.as_tensor(X, dtype=torch.float32, device=self.device)
with torch.no_grad():
return self.model(X).detach().cpu().numpy()
def get_params(self, deep=True):
return dict(
input_dim=self.input_dim,
output_dim=self.output_dim,
dropout=self.dropout,
lr=self.lr,
epochs=self.epochs,
batch_size=self.batch_size,
device=self.device,
verbose=self.verbose,
hidden_layers=self.hidden_layers,
no_tasks=self.no_tasks,
l2_weight=self.l2_weight,
patience=self.patience,
min_epochs=self.min_epochs,
min_delta=self.min_delta,
hidden_dim=self.hidden_dim,
loss_type=self.loss_type,
target_is_logvar=self.target_is_logvar,
nll_eps=self.nll_eps,
clamp_logvar_min=self.clamp_logvar_min,
clamp_logvar_max=self.clamp_logvar_max,
student_df=self.student_df,
)
def set_params(self, **params):
arch_keys = {
"input_dim", "output_dim", "hidden_layers", "hidden_dim",
"dropout", "no_tasks", "device"
}
need_rebuild = any(k in arch_keys for k in params)
for k, v in params.items():
if hasattr(self, k):
setattr(self, k, v)
if need_rebuild:
self._build()
else:
for g in self.optimizer.param_groups:
g["lr"] = self.lr
g["weight_decay"] = self.l2_weight
return self
Test 1 task EURUSD Vanilla MLP without CV¶
In [117]:
import os
save_test_SMLP_model_EURUSD_1_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SMLP_model_1_task.pkl"
)
t1_EURUSD_SMLP_results, t1_EURUSD_SMLP_nested_results, t1_EURUSD_SMLP_best_model, t1_EURUSD_SMLP_best_params, _ = train_and_evaluate_model(
model_type="Simple_MLP",
X_price=eur2_X_price,
X_time=eur2_X_time,
y=eur2_y,
no_tasks=1,
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
save_model_path=save_test_SMLP_model_EURUSD_1_file_path,
lr=1e-4,
epochs=50,
batch_size=64,
verbose=True,
time_horizon=30,
dropout=0,
l2_weight=1e-6,
patience=10,
min_epochs=90,
hidden_dim=32,
hidden_layers=3
)
[mode=log_var_ratio] loss_type=mse, target_is_logvar=False, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 30 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525526 Min value: -3.1812809750988515 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_var_ratio scaled): Shape: (2723, 30, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.6622325934971935 Min value: -4.5885956391503315 Checking X_price_val: Shape: (302, 60, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.40892116517157 Min value: -2.633090998920803 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_var_ratio scaled): Shape: (302, 30, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.461737368643011 Min value: -4.144793470690054 Checking X_price_test: Shape: (757, 60, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144347 Min value: -2.633090998920803 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_var_ratio scaled): Shape: (757, 30, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.7626499407155074 Min value: -4.740813716646197 Epoch 1: Train mse = 1.1009 | Val mse = 1.0928 Epoch 2: Train mse = 1.0430 | Val mse = 1.0542 Epoch 3: Train mse = 1.0071 | Val mse = 1.0202 Epoch 4: Train mse = 0.9798 | Val mse = 0.9923 Epoch 5: Train mse = 0.9588 | Val mse = 0.9696 Epoch 6: Train mse = 0.9419 | Val mse = 0.9486 Epoch 7: Train mse = 0.9269 | Val mse = 0.9258 Epoch 8: Train mse = 0.9124 | Val mse = 0.9030 Epoch 9: Train mse = 0.8967 | Val mse = 0.8735 Epoch 10: Train mse = 0.8822 | Val mse = 0.8593 Epoch 11: Train mse = 0.8666 | Val mse = 0.8250 Epoch 12: Train mse = 0.8532 | Val mse = 0.8324 Epoch 13: Train mse = 0.8389 | Val mse = 0.7840 Epoch 14: Train mse = 0.8210 | Val mse = 0.8002 Epoch 15: Train mse = 0.8091 | Val mse = 0.7487 Epoch 16: Train mse = 0.7933 | Val mse = 0.7750 Epoch 17: Train mse = 0.7853 | Val mse = 0.7197 Epoch 18: Train mse = 0.7691 | Val mse = 0.7584 Epoch 19: Train mse = 0.7572 | Val mse = 0.6871 Epoch 20: Train mse = 0.7338 | Val mse = 0.7018 Epoch 21: Train mse = 0.7215 | Val mse = 0.6619 Epoch 22: Train mse = 0.7060 | Val mse = 0.6727 Epoch 23: Train mse = 0.6922 | Val mse = 0.6452 Epoch 24: Train mse = 0.6774 | Val mse = 0.6439 Epoch 25: Train mse = 0.6651 | Val mse = 0.6378 Epoch 26: Train mse = 0.6544 | Val mse = 0.6195 Epoch 27: Train mse = 0.6393 | Val mse = 0.6150 Epoch 28: Train mse = 0.6257 | Val mse = 0.6195 Epoch 29: Train mse = 0.6172 | Val mse = 0.5998 Epoch 30: Train mse = 0.6042 | Val mse = 0.5991 Epoch 31: Train mse = 0.5898 | Val mse = 0.5764 Epoch 32: Train mse = 0.5771 | Val mse = 0.5550 Epoch 33: Train mse = 0.5694 | Val mse = 0.5465 Epoch 34: Train mse = 0.5615 | Val mse = 0.5594 Epoch 35: Train mse = 0.5460 | Val mse = 0.5335 Epoch 36: Train mse = 0.5388 | Val mse = 0.5931 Epoch 37: Train mse = 0.5487 | Val mse = 0.5139 Epoch 38: Train mse = 0.5199 | Val mse = 0.5494 Epoch 39: Train mse = 0.5128 | Val mse = 0.4941 Epoch 40: Train mse = 0.5014 | Val mse = 0.5370 Epoch 41: Train mse = 0.4916 | Val mse = 0.4822 Epoch 42: Train mse = 0.4782 | Val mse = 0.5232 Epoch 43: Train mse = 0.4726 | Val mse = 0.4816 Epoch 44: Train mse = 0.4611 | Val mse = 0.4796 Epoch 45: Train mse = 0.4513 | Val mse = 0.5156 Epoch 46: Train mse = 0.4541 | Val mse = 0.4462 Epoch 47: Train mse = 0.4412 | Val mse = 0.4921 Epoch 48: Train mse = 0.4324 | Val mse = 0.4671 Epoch 49: Train mse = 0.4329 | Val mse = 0.4328 Epoch 50: Train mse = 0.4546 | Val mse = 0.5877 Parameters used in the single-fit model: input_dim: 360 output_dim: 30 dropout: 0.00000000 lr: 0.00010000 epochs: 50 batch_size: 64 device: mps verbose: True hidden_layers: 3 no_tasks: 1 l2_weight: 0.00000100 patience: 10 min_epochs: 90 min_delta: 0.00010000 hidden_dim: 32 loss_type: mse target_is_logvar: False nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.13181230 1 day(s) RMSE : 0.22407982 1 day(s) R2 : -0.10008904 1 day(s) Pearson r : 0.33309319 1 day(s) QLIKE : 0.57519066 3 day(s) MAE : 0.13826405 3 day(s) RMSE : 0.23401201 3 day(s) R2 : -0.20902772 3 day(s) Pearson r : 0.29530543 3 day(s) QLIKE : 0.59422919 5 day(s) MAE : 0.13404594 5 day(s) RMSE : 0.22901049 5 day(s) R2 : -0.17105693 5 day(s) Pearson r : 0.31226600 5 day(s) QLIKE : 0.58462768 10 day(s) MAE : 0.13612855 10 day(s) RMSE : 0.23167728 10 day(s) R2 : -0.20992958 10 day(s) Pearson r : 0.29192773 10 day(s) QLIKE : 0.61735417 20 day(s) MAE : 0.13834597 20 day(s) RMSE : 0.23553788 20 day(s) R2 : -0.26533896 20 day(s) Pearson r : 0.25850175 20 day(s) QLIKE : 0.63388486 full horizon MAE : 0.13999686 full horizon RMSE : 0.23741028 full horizon R2 : -0.29367034 full horizon Pearson r : 0.24743317 full horizon QLIKE : 0.65184732 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/test_EURUSD_SMLP_model_1_task.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00270254, max=1.47617
Test 1 task EURUSD 3 Vanilla MLP without CV¶
In [ ]:
import os
save_test_SMLP_model_EURUSD_1_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SMLP_model_1_task.pkl"
)
t1_EURUSD_SMLP_results, t1_EURUSD_SMLP_nested_results, t1_EURUSD_SMLP_best_model, t1_EURUSD_SMLP_best_params, yy = train_and_evaluate_model(
model_type="Simple_MLP",
X_price=eur4_X_price,
X_time=eur4_X_time,
y=eur4_y,
no_tasks=1,
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
y_scale_type="standard",
save_model_path=save_test_SMLP_model_EURUSD_1_file_path,
lr=1e-4,
epochs=50,
batch_size=4,
verbose=True,
time_horizon=20,
dropout=0.0,
l2_weight=1e-4,
patience=10,
min_epochs=30,
hidden_dim=128,
hidden_layers=2,
min_delta=1e-4,
single_holdout=False,
target_mode="log_var_ratio",
baseline_feature_idx=0,
baseline_window=2
)
Test EURUSD 5¶
In [ ]:
import os
save_test_SMLP_model_EURUSD_5_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SMLP_model_5_task.pkl"
)
t5_EURUSD_SMLP_results, t5_EURUSD_SMLP_nested_results, t5_EURUSD_SMLP_best_model, t5_EURUSD_SMLP_best_params, yy = train_and_evaluate_model(
model_type="Simple_MLP",
X_price=eur5_X_price,
X_time=eur5_X_time,
y=eur5_y,
no_tasks=1,
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
y_scale_type="standard",
save_model_path=save_test_SMLP_model_EURUSD_5_file_path,
lr=5e-4,
epochs=50,
batch_size=64,
verbose=True,
time_horizon=20,
dropout=0.0,
l2_weight=1e-4,
patience=10,
min_epochs=30,
hidden_dim=512,
hidden_layers=3,
min_delta=1e-4,
single_holdout=False,
target_mode="log_mse",
baseline_feature_idx=0,
baseline_window=20
)
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.998957169490066 Min value: -3.7200073694899434 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.40793072075585246 Min value: 0.00022460106458896233 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_val (log_mse scaled): Shape: (302, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3212202110774225 Min value: -2.9768947428361368 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4442211143925813 Min value: -0.00012220230225973657 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_test (log_mse scaled): Shape: (757, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737162 Min value: -5.3523385822659755 Size of the X data for training (core): (2723, 60) Size of the Y data for training (core): (2723, 20) Epoch 1: Train mse = 1.1085 | Val mse = 1.1654 Epoch 2: Train mse = 0.8407 | Val mse = 1.1265 Epoch 3: Train mse = 0.6316 | Val mse = 0.7571 Epoch 4: Train mse = 0.5505 | Val mse = 0.8073 Epoch 5: Train mse = 0.5419 | Val mse = 0.8201 Epoch 6: Train mse = 0.4995 | Val mse = 0.6648 Epoch 7: Train mse = 0.4223 | Val mse = 0.5509 Epoch 8: Train mse = 0.3904 | Val mse = 0.4808 Epoch 9: Train mse = 0.3724 | Val mse = 0.4628 Epoch 10: Train mse = 0.3573 | Val mse = 0.4462 Epoch 11: Train mse = 0.3450 | Val mse = 0.4241 Epoch 12: Train mse = 0.3405 | Val mse = 0.4235 Epoch 13: Train mse = 0.3347 | Val mse = 0.4174 Epoch 14: Train mse = 0.3245 | Val mse = 0.3961 Epoch 15: Train mse = 0.3183 | Val mse = 0.4418 Epoch 16: Train mse = 0.3332 | Val mse = 0.4608 Epoch 17: Train mse = 0.3381 | Val mse = 0.4467 Epoch 18: Train mse = 0.3329 | Val mse = 0.4721 Epoch 19: Train mse = 0.3308 | Val mse = 0.4239 Epoch 20: Train mse = 0.3217 | Val mse = 0.4788 Epoch 21: Train mse = 0.3211 | Val mse = 0.3851 Epoch 22: Train mse = 0.3074 | Val mse = 0.4192 Epoch 23: Train mse = 0.3066 | Val mse = 0.4409 Epoch 24: Train mse = 0.2972 | Val mse = 0.3917 Epoch 25: Train mse = 0.2893 | Val mse = 0.4470 Epoch 26: Train mse = 0.2938 | Val mse = 0.3526 Epoch 27: Train mse = 0.2867 | Val mse = 0.4251 Epoch 28: Train mse = 0.2967 | Val mse = 0.3474 Epoch 29: Train mse = 0.2943 | Val mse = 0.4090 Epoch 30: Train mse = 0.2970 | Val mse = 0.3967 Epoch 31: Train mse = 0.2788 | Val mse = 0.3907 Epoch 32: Train mse = 0.2721 | Val mse = 0.3829 Epoch 33: Train mse = 0.2623 | Val mse = 0.3965 Epoch 34: Train mse = 0.2578 | Val mse = 0.4017 Epoch 35: Train mse = 0.2617 | Val mse = 0.3679 Epoch 36: Train mse = 0.2700 | Val mse = 0.3643 Epoch 37: Train mse = 0.2832 | Val mse = 0.4121 Epoch 38: Train mse = 0.2714 | Val mse = 0.3745 Early stopping triggered at epoch 38. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 dropout: 0.00000000 lr: 0.00050000 epochs: 50 batch_size: 64 device: mps verbose: True hidden_layers: 3 no_tasks: 1 l2_weight: 0.00010000 patience: 10 min_epochs: 30 min_delta: 0.00010000 hidden_dim: 512 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.09464960 1 day(s) RMSE : 0.16672542 1 day(s) R2 : 0.39098781 1 day(s) Pearson r : 0.64844177 1 day(s) QLIKE : 0.45368584 3 day(s) MAE : 0.09935203 3 day(s) RMSE : 0.17911570 3 day(s) R2 : 0.29168359 3 day(s) Pearson r : 0.58907491 3 day(s) QLIKE : 0.46721591 5 day(s) MAE : 0.09917879 5 day(s) RMSE : 0.18545364 5 day(s) R2 : 0.23204108 5 day(s) Pearson r : 0.54123295 5 day(s) QLIKE : 0.46941656 10 day(s) MAE : 0.09952519 10 day(s) RMSE : 0.19201849 10 day(s) R2 : 0.16885026 10 day(s) Pearson r : 0.47829144 10 day(s) QLIKE : 0.49290335 20 day(s) MAE : 0.09735010 20 day(s) RMSE : 0.19308096 20 day(s) R2 : 0.14971558 20 day(s) Pearson r : 0.43720653 20 day(s) QLIKE : 0.50702214 full horizon MAE : 0.09735010 full horizon RMSE : 0.19308096 full horizon R2 : 0.14971558 full horizon Pearson r : 0.43720653 full horizon QLIKE : 0.50702214 Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00419776, max=1.6561
Test EURUSD 6¶
In [ ]:
import os
save_test_SMLP_model_EURUSD_6_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SMLP_model_6_task.pkl"
)
t6_EURUSD_SMLP_results, t6_EURUSD_SMLP_nested_results, t6_EURUSD_SMLP_best_model, t6_EURUSD_SMLP_best_params, yy = train_and_evaluate_model(
model_type="Simple_MLP",
X_price=eur6_X_price,
X_time=eur6_X_time,
y=eur6_y,
no_tasks=1,
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
y_scale_type="standard",
save_model_path=save_test_SMLP_model_EURUSD_6_file_path,
lr=5e-4,
epochs=50,
batch_size=32,
verbose=True,
time_horizon=20,
dropout=0.0,
l2_weight=1e-4,
patience=10,
min_epochs=30,
hidden_dim=64,
hidden_layers=3,
min_delta=1e-4,
single_holdout=False,
target_mode="log_var_ratio",
baseline_feature_idx=0,
baseline_window=2
)
[mode=log_var_ratio] loss_type=mse, target_is_logvar=False, normalize_y=True Using device: mps Batch size for y: 3781 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2722, 60, 8) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0000000000000002 Min value: 0.0 Checking X_time_train_core: Shape: (2722, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking y_train_core (log_var_ratio scaled): Shape: (2722, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.047438323525787 Min value: -5.29816439091353 Checking X_price_val: Shape: (302, 60, 8) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.1249999999999998 Min value: 0.0 Checking y_val (log_var_ratio scaled): Shape: (302, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.8046793379658457 Min value: -4.56473299178836 Checking X_price_test: Shape: (757, 60, 8) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.4141668678880164 Min value: -0.00012220230225973657 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.4999999999999996 Min value: 0.0 Checking y_test (log_var_ratio scaled): Shape: (757, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.109080553652343 Min value: -5.056254299402706 Epoch 1: Train mse = 1.0254 | Val mse = 1.1843 Epoch 2: Train mse = 0.9898 | Val mse = 1.1796 Epoch 3: Train mse = 0.9693 | Val mse = 1.1996 Epoch 4: Train mse = 0.9482 | Val mse = 1.1369 Epoch 5: Train mse = 0.9325 | Val mse = 0.9834 Epoch 6: Train mse = 0.9414 | Val mse = 1.0421 Epoch 7: Train mse = 0.9236 | Val mse = 0.8652 Epoch 8: Train mse = 0.9231 | Val mse = 1.0115 Epoch 9: Train mse = 0.8766 | Val mse = 0.8263 Epoch 10: Train mse = 0.8985 | Val mse = 1.0142 Epoch 11: Train mse = 0.8885 | Val mse = 0.8011 Epoch 12: Train mse = 0.8513 | Val mse = 0.7981 Epoch 13: Train mse = 0.8393 | Val mse = 1.1614 Epoch 14: Train mse = 0.8228 | Val mse = 0.8322 Epoch 15: Train mse = 0.8356 | Val mse = 1.5081 Epoch 16: Train mse = 0.9409 | Val mse = 0.8507 Epoch 17: Train mse = 0.8573 | Val mse = 0.8267 Epoch 18: Train mse = 0.8133 | Val mse = 0.9469 Epoch 19: Train mse = 0.7925 | Val mse = 0.8443 Epoch 20: Train mse = 0.7744 | Val mse = 0.7599 Epoch 21: Train mse = 0.7423 | Val mse = 0.8201 Epoch 22: Train mse = 0.7565 | Val mse = 0.8120 Epoch 23: Train mse = 0.7031 | Val mse = 0.6744 Epoch 24: Train mse = 0.7012 | Val mse = 0.8145 Epoch 25: Train mse = 0.7354 | Val mse = 0.8004 Epoch 26: Train mse = 0.7427 | Val mse = 0.9090 Epoch 27: Train mse = 0.6923 | Val mse = 0.7742 Epoch 28: Train mse = 0.6434 | Val mse = 0.6592 Epoch 29: Train mse = 0.6639 | Val mse = 0.6840 Epoch 30: Train mse = 0.6599 | Val mse = 0.9014 Epoch 31: Train mse = 0.6814 | Val mse = 0.7118 Epoch 32: Train mse = 0.6656 | Val mse = 0.8019 Epoch 33: Train mse = 0.7925 | Val mse = 0.7838 Epoch 34: Train mse = 0.7089 | Val mse = 0.7336 Epoch 35: Train mse = 0.6505 | Val mse = 0.6627 Epoch 36: Train mse = 0.6276 | Val mse = 0.6601 Epoch 37: Train mse = 0.6359 | Val mse = 0.6143 Epoch 38: Train mse = 0.6031 | Val mse = 0.7480 Epoch 39: Train mse = 0.6087 | Val mse = 0.5434 Epoch 40: Train mse = 0.6665 | Val mse = 0.7992 Epoch 41: Train mse = 0.6333 | Val mse = 0.6767 Epoch 42: Train mse = 0.6035 | Val mse = 0.5965 Epoch 43: Train mse = 0.5626 | Val mse = 0.5183 Epoch 44: Train mse = 0.5821 | Val mse = 0.5816 Epoch 45: Train mse = 0.6578 | Val mse = 0.5533 Epoch 46: Train mse = 0.5494 | Val mse = 0.8119 Epoch 47: Train mse = 0.6156 | Val mse = 0.5541 Epoch 48: Train mse = 0.6141 | Val mse = 0.5380 Epoch 49: Train mse = 0.6093 | Val mse = 0.6393 Epoch 50: Train mse = 0.5170 | Val mse = 0.5141 Parameters used in the single-fit model: input_dim: 480 output_dim: 20 dropout: 0.00000000 lr: 0.00050000 epochs: 50 batch_size: 32 device: mps verbose: True hidden_layers: 3 no_tasks: 1 l2_weight: 0.00010000 patience: 10 min_epochs: 30 min_delta: 0.00010000 hidden_dim: 64 loss_type: mse target_is_logvar: False nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.28022477 1 day(s) RMSE : 0.64311327 1 day(s) R2 : -8.06144123 1 day(s) Pearson r : 0.23981665 1 day(s) QLIKE : 0.70240040 3 day(s) MAE : 0.28926402 3 day(s) RMSE : 0.71138957 3 day(s) R2 : -10.17313269 3 day(s) Pearson r : 0.16843885 3 day(s) QLIKE : 0.75433149 5 day(s) MAE : 0.26941983 5 day(s) RMSE : 0.65337632 5 day(s) R2 : -8.53221966 5 day(s) Pearson r : 0.16472426 5 day(s) QLIKE : 0.83573974 10 day(s) MAE : 0.28015105 10 day(s) RMSE : 0.68665716 10 day(s) R2 : -9.62852744 10 day(s) Pearson r : 0.12698617 10 day(s) QLIKE : 0.86454987 20 day(s) MAE : 0.30356013 20 day(s) RMSE : 0.76471899 20 day(s) R2 : -12.33795021 20 day(s) Pearson r : 0.09339941 20 day(s) QLIKE : 0.93895486 full horizon MAE : 0.30356013 full horizon RMSE : 0.76471899 full horizon R2 : -12.33795021 full horizon Pearson r : 0.09339941 full horizon QLIKE : 0.93895486 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_EURUSD_SMLP_model_6_task.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00190013, max=19.7457
Test EURUSD 7¶
In [ ]:
import os
save_test_SMLP_model_EURUSD_7_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SMLP_model_7_task.pkl"
)
t7_EURUSD_SMLP_results, t7_EURUSD_SMLP_nested_results, t7_EURUSD_SMLP_best_model, t7_EURUSD_SMLP_best_params, yy = train_and_evaluate_model(
model_type="Simple_MLP",
X_price=eur7_X_price,
X_time=eur7_X_time,
y=eur7_y,
no_tasks=1,
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
y_scale_type="standard",
save_model_path=save_test_SMLP_model_EURUSD_7_file_path,
lr=1e-3,
epochs=50,
batch_size=32,
verbose=True,
time_horizon=20,
dropout=0.0,
l2_weight=1e-5,
patience=10,
min_epochs=30,
hidden_dim=1024,
hidden_layers=6,
min_delta=1e-4,
single_holdout=False,
target_mode="log_mse",
baseline_feature_idx=0,
baseline_window=20
)
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0000000000000002 Min value: 0.0 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.998957169490066 Min value: -3.7200073694899434 Checking X_price_val: Shape: (302, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7917474776.440305 Min value: -0.007013492710980488 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.1249999999999998 Min value: 0.0 Checking y_val (log_mse scaled): Shape: (302, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3212202110774225 Min value: -2.9768947428361368 Checking X_price_test: Shape: (757, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.4141668678880164 Min value: -0.00012220230225973657 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.4999999999999996 Min value: 0.0 Checking y_test (log_mse scaled): Shape: (757, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737162 Min value: -5.3523385822659755 Epoch 1: Train mse = 1.1017 | Val mse = 2.1282 Epoch 2: Train mse = 1.1071 | Val mse = 1.3940 Epoch 3: Train mse = 1.0810 | Val mse = 1.3169 Epoch 4: Train mse = 1.0838 | Val mse = 1.2321 Epoch 5: Train mse = 1.0787 | Val mse = 1.1729 Epoch 6: Train mse = 1.0663 | Val mse = 1.2682 Epoch 7: Train mse = 1.0365 | Val mse = 1.4274 Epoch 8: Train mse = 1.0295 | Val mse = 1.4302 Epoch 9: Train mse = 1.0287 | Val mse = 1.4285 Epoch 10: Train mse = 1.0230 | Val mse = 1.4716 Epoch 11: Train mse = 1.0216 | Val mse = 1.4979 Epoch 12: Train mse = 1.0204 | Val mse = 1.5198 Epoch 13: Train mse = 1.0189 | Val mse = 1.5163 Epoch 14: Train mse = 1.0197 | Val mse = 1.5220 Epoch 15: Train mse = 1.0178 | Val mse = 1.5161 Epoch 16: Train mse = 1.0188 | Val mse = 1.5152 Epoch 17: Train mse = 1.0179 | Val mse = 1.5126 Epoch 18: Train mse = 1.0180 | Val mse = 1.5109 Epoch 19: Train mse = 1.0180 | Val mse = 1.5110 Epoch 20: Train mse = 1.0174 | Val mse = 1.5089 Epoch 21: Train mse = 1.0178 | Val mse = 1.5112 Epoch 22: Train mse = 1.0169 | Val mse = 1.5070 Epoch 23: Train mse = 1.0179 | Val mse = 1.5109 Epoch 24: Train mse = 1.0161 | Val mse = 1.5062 Epoch 25: Train mse = 1.0175 | Val mse = 1.5118 Epoch 26: Train mse = 1.0158 | Val mse = 1.5072 Epoch 27: Train mse = 1.0171 | Val mse = 1.5120 Epoch 28: Train mse = 1.0157 | Val mse = 1.5085 Epoch 29: Train mse = 1.0169 | Val mse = 1.5132 Epoch 30: Train mse = 1.0156 | Val mse = 1.5082 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 20 dropout: 0.00000000 lr: 0.00100000 epochs: 50 batch_size: 32 device: mps verbose: True hidden_layers: 6 no_tasks: 1 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 hidden_dim: 1024 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.12405876 1 day(s) RMSE : 0.22466625 1 day(s) R2 : -0.10585455 1 day(s) Pearson r : -0.23082149 1 day(s) QLIKE : 0.58506520 3 day(s) MAE : 0.12372473 3 day(s) RMSE : 0.22406257 3 day(s) R2 : -0.10840534 3 day(s) Pearson r : -0.00037619 3 day(s) QLIKE : 0.58378936 5 day(s) MAE : 0.12295376 5 day(s) RMSE : 0.22247751 5 day(s) R2 : -0.10519648 5 day(s) Pearson r : -0.00265316 5 day(s) QLIKE : 0.58168654 10 day(s) MAE : 0.12222232 10 day(s) RMSE : 0.22107074 10 day(s) R2 : -0.10168063 10 day(s) Pearson r : -0.00250193 10 day(s) QLIKE : 0.57942102 20 day(s) MAE : 0.12154976 20 day(s) RMSE : 0.21998599 20 day(s) R2 : -0.10376191 20 day(s) Pearson r : 0.00125410 20 day(s) QLIKE : 0.57495934 full horizon MAE : 0.12154976 full horizon RMSE : 0.21998599 full horizon R2 : -0.10376191 full horizon Pearson r : 0.00125410 full horizon QLIKE : 0.57495934 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_EURUSD_SMLP_model_7_task.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.115914, max=0.126551
Test 8¶
In [576]:
import os
save_test_SMLP_model_EURUSD_8_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SMLP_model_8_task.pkl"
)
t8_EURUSD_SMLP_results, t8_EURUSD_SMLP_nested_results, t8_EURUSD_SMLP_best_model, t8_EURUSD_SMLP_best_params, yy = train_and_evaluate_model(
model_type="Simple_MLP",
X_price=eur8_X_price,
X_time=eur8_X_time,
y=eur8_y,
no_tasks=1,
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
y_scale_type="standard",
save_model_path=save_test_SMLP_model_EURUSD_8_file_path,
lr=5e-4,
epochs=100,
batch_size=64,
verbose=True,
time_horizon=20,
dropout=0.0,
l2_weight=1e-4,
patience=10,
min_epochs=30,
hidden_dim=64,
hidden_layers=3,
min_delta=1e-4,
single_holdout=False,
target_mode="log_mse",
baseline_feature_idx=0, # set to the feature that carries your returns
baseline_window=2
)
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525025 Min value: -0.7148707238463011 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.998957169490066 Min value: -3.7200073694899434 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.408921165171427 Min value: -0.7103978753601863 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3212202110774225 Min value: -2.9768947428361368 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144182 Min value: -0.7173043382357188 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737162 Min value: -5.3523385822659755 Epoch 1: Train mse = 0.9477 | Val mse = 0.9289 Epoch 2: Train mse = 0.6873 | Val mse = 0.7190 Epoch 3: Train mse = 0.5437 | Val mse = 0.5929 Epoch 4: Train mse = 0.4629 | Val mse = 0.5233 Epoch 5: Train mse = 0.4166 | Val mse = 0.4886 Epoch 6: Train mse = 0.3930 | Val mse = 0.4673 Epoch 7: Train mse = 0.3774 | Val mse = 0.4519 Epoch 8: Train mse = 0.3649 | Val mse = 0.4401 Epoch 9: Train mse = 0.3538 | Val mse = 0.4308 Epoch 10: Train mse = 0.3435 | Val mse = 0.4238 Epoch 11: Train mse = 0.3344 | Val mse = 0.4185 Epoch 12: Train mse = 0.3268 | Val mse = 0.4142 Epoch 13: Train mse = 0.3202 | Val mse = 0.4105 Epoch 14: Train mse = 0.3142 | Val mse = 0.4072 Epoch 15: Train mse = 0.3089 | Val mse = 0.4044 Epoch 16: Train mse = 0.3040 | Val mse = 0.4021 Epoch 17: Train mse = 0.2997 | Val mse = 0.4003 Epoch 18: Train mse = 0.2957 | Val mse = 0.3987 Epoch 19: Train mse = 0.2920 | Val mse = 0.3973 Epoch 20: Train mse = 0.2885 | Val mse = 0.3962 Epoch 21: Train mse = 0.2852 | Val mse = 0.3953 Epoch 22: Train mse = 0.2821 | Val mse = 0.3946 Epoch 23: Train mse = 0.2791 | Val mse = 0.3941 Epoch 24: Train mse = 0.2762 | Val mse = 0.3939 Epoch 25: Train mse = 0.2734 | Val mse = 0.3939 Epoch 26: Train mse = 0.2707 | Val mse = 0.3940 Epoch 27: Train mse = 0.2681 | Val mse = 0.3943 Epoch 28: Train mse = 0.2656 | Val mse = 0.3947 Epoch 29: Train mse = 0.2631 | Val mse = 0.3953 Epoch 30: Train mse = 0.2607 | Val mse = 0.3960 Epoch 31: Train mse = 0.2583 | Val mse = 0.3968 Epoch 32: Train mse = 0.2560 | Val mse = 0.3977 Epoch 33: Train mse = 0.2537 | Val mse = 0.3986 Epoch 34: Train mse = 0.2515 | Val mse = 0.3994 Early stopping triggered at epoch 34. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 dropout: 0.00000000 lr: 0.00050000 epochs: 100 batch_size: 64 device: mps verbose: True hidden_layers: 3 no_tasks: 1 l2_weight: 0.00010000 patience: 10 min_epochs: 30 min_delta: 0.00010000 hidden_dim: 64 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.08745844 1 day(s) RMSE : 0.17746631 1 day(s) R2 : 0.30999192 1 day(s) Pearson r : 0.57966146 1 day(s) QLIKE : 0.43444844 3 day(s) MAE : 0.09123118 3 day(s) RMSE : 0.18515062 3 day(s) R2 : 0.24314910 3 day(s) Pearson r : 0.52439783 3 day(s) QLIKE : 0.46276500 5 day(s) MAE : 0.09204460 5 day(s) RMSE : 0.18804839 5 day(s) R2 : 0.21040116 5 day(s) Pearson r : 0.49463939 5 day(s) QLIKE : 0.46525915 10 day(s) MAE : 0.09347147 10 day(s) RMSE : 0.19281354 10 day(s) R2 : 0.16195323 10 day(s) Pearson r : 0.44891100 10 day(s) QLIKE : 0.47208852 20 day(s) MAE : 0.09463232 20 day(s) RMSE : 0.19519930 20 day(s) R2 : 0.13095581 20 day(s) Pearson r : 0.41578511 20 day(s) QLIKE : 0.48778836 full horizon MAE : 0.09463232 full horizon RMSE : 0.19519930 full horizon R2 : 0.13095581 full horizon Pearson r : 0.41578511 full horizon QLIKE : 0.48778836 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_EURUSD_SMLP_model_8_task.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00411695, max=1.19432
Check y¶
In [507]:
y_true_t0 = yy[:, 0, 0]
y_pred_t0 = yy[:, 0, 1]
print(f"y_true @t=0 std={np.nanstd(y_true_t0):.6g}, max={np.nanmax(y_true_t0):.6g}, min={np.nanmin(y_true_t0):.6g}")
print(f"y_pred @t=0 std={np.nanstd(y_pred_t0):.6g}, max={np.nanmax(y_pred_t0):.6g}, min={np.nanmin(y_pred_t0):.6g}")
y_true @t=0 std=0.213643, max=2.66457, min=9.56189e-05 y_pred @t=0 std=0.520235, max=4.87784, min=0.00883726
Vanila MLP 1 task different tests¶
In [ ]:
import os
import numpy as np
save_test_SMLP_model_AAPL_1_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_AAPL_SMLP_model_1_task.pkl"
)
# appl4_y2 = appl4_y.copy()
# appl4_y2[:,:,0] = np.log(np.clip(appl4_y2[:,:,0], eps, None))
t1_AAPL_SMLP_results, t1_AAPL_SMLP_nested_results, t1_AAPL_SMLP_best_model, t1_AAPL_SMLP_best_params, yy = train_and_evaluate_model(
model_type="Simple_MLP",
X_price=appl8_X_price,
X_time=appl8_X_time,
#y=appl4_y2,
y=appl8_y,
no_tasks=1,
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
y_scale_type="standard",
save_model_path=save_test_SMLP_model_AAPL_1_file_path,
lr=5e-5,
epochs=50,
batch_size=4,
verbose=True,
time_horizon=5,
dropout=0.0,
l2_weight=1e-4,
patience=10,
min_epochs=20,
hidden_dim=32,
hidden_layers=2,
min_delta=1e-4,
target_mode="log_mse",
baseline_feature_idx=0,
baseline_window=60
)
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714204 Min value: -0.03434294798653995 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.140231129276094 Min value: -2.358314580069225 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.051920323212192546 Min value: -0.0338203458440787 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.907938252663739 Min value: -1.632926464574096 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.19306601030720755 Min value: -0.03421247164703902 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.896926346992255 Min value: -2.123830418076347 Epoch 1: Train mse = 1.0106 | Val mse = 0.6708 Epoch 2: Train mse = 1.0124 | Val mse = 0.6782 Epoch 3: Train mse = 1.0021 | Val mse = 0.6762 Epoch 4: Train mse = 0.9895 | Val mse = 0.6797 Epoch 5: Train mse = 0.9760 | Val mse = 0.6904 Epoch 6: Train mse = 0.9610 | Val mse = 0.7086 Epoch 7: Train mse = 0.9441 | Val mse = 0.7328 Epoch 8: Train mse = 0.9248 | Val mse = 0.7592 Epoch 9: Train mse = 0.9026 | Val mse = 0.7834 Epoch 10: Train mse = 0.8778 | Val mse = 0.8010 Epoch 11: Train mse = 0.8516 | Val mse = 0.8064 Epoch 12: Train mse = 0.8258 | Val mse = 0.7960 Epoch 13: Train mse = 0.8018 | Val mse = 0.7726 Epoch 14: Train mse = 0.7806 | Val mse = 0.7432 Epoch 15: Train mse = 0.7623 | Val mse = 0.7138 Epoch 16: Train mse = 0.7468 | Val mse = 0.6883 Epoch 17: Train mse = 0.7337 | Val mse = 0.6680 Epoch 18: Train mse = 0.7228 | Val mse = 0.6529 Epoch 19: Train mse = 0.7135 | Val mse = 0.6421 Epoch 20: Train mse = 0.7058 | Val mse = 0.6347 Epoch 21: Train mse = 0.6992 | Val mse = 0.6294 Epoch 22: Train mse = 0.6935 | Val mse = 0.6254 Epoch 23: Train mse = 0.6886 | Val mse = 0.6222 Epoch 24: Train mse = 0.6843 | Val mse = 0.6192 Epoch 25: Train mse = 0.6805 | Val mse = 0.6163 Epoch 26: Train mse = 0.6770 | Val mse = 0.6135 Epoch 27: Train mse = 0.6739 | Val mse = 0.6106 Epoch 28: Train mse = 0.6710 | Val mse = 0.6078 Epoch 29: Train mse = 0.6683 | Val mse = 0.6050 Epoch 30: Train mse = 0.6657 | Val mse = 0.6021 Epoch 31: Train mse = 0.6633 | Val mse = 0.5994 Epoch 32: Train mse = 0.6610 | Val mse = 0.5966 Epoch 33: Train mse = 0.6589 | Val mse = 0.5940 Epoch 34: Train mse = 0.6568 | Val mse = 0.5914 Epoch 35: Train mse = 0.6548 | Val mse = 0.5889 Epoch 36: Train mse = 0.6528 | Val mse = 0.5864 Epoch 37: Train mse = 0.6509 | Val mse = 0.5840 Epoch 38: Train mse = 0.6491 | Val mse = 0.5817 Epoch 39: Train mse = 0.6473 | Val mse = 0.5795 Epoch 40: Train mse = 0.6456 | Val mse = 0.5773 Epoch 41: Train mse = 0.6439 | Val mse = 0.5752 Epoch 42: Train mse = 0.6423 | Val mse = 0.5731 Epoch 43: Train mse = 0.6407 | Val mse = 0.5711 Epoch 44: Train mse = 0.6392 | Val mse = 0.5691 Epoch 45: Train mse = 0.6377 | Val mse = 0.5671 Epoch 46: Train mse = 0.6363 | Val mse = 0.5652 Epoch 47: Train mse = 0.6348 | Val mse = 0.5633 Epoch 48: Train mse = 0.6335 | Val mse = 0.5614 Epoch 49: Train mse = 0.6321 | Val mse = 0.5596 Epoch 50: Train mse = 0.6308 | Val mse = 0.5577 Parameters used in the single-fit model: input_dim: 60 output_dim: 5 dropout: 0.00000000 lr: 0.00005000 epochs: 50 batch_size: 4 device: mps verbose: True hidden_layers: 2 no_tasks: 1 l2_weight: 0.00010000 patience: 10 min_epochs: 20 min_delta: 0.00010000 hidden_dim: 32 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.58473997 1 day(s) RMSE : 8.00624713 1 day(s) R2 : 0.14183473 1 day(s) Pearson r : 0.45541136 1 day(s) QLIKE : 0.32111413 3 day(s) MAE : 2.64977552 3 day(s) RMSE : 8.35105197 3 day(s) R2 : 0.11752366 3 day(s) Pearson r : 0.40812205 3 day(s) QLIKE : 0.36502756 5 day(s) MAE : 2.71750510 5 day(s) RMSE : 8.53601870 5 day(s) R2 : 0.08817267 5 day(s) Pearson r : 0.33117256 5 day(s) QLIKE : 0.40021435 full horizon MAE : 2.71750510 full horizon RMSE : 8.53601870 full horizon R2 : 0.08817267 full horizon Pearson r : 0.33117256 full horizon QLIKE : 0.40021435 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_AAPL_SMLP_model_1_task.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=1.45567, max=14.5826
Check output y¶
In [560]:
y_true_t0 = yy[:, 0, 0]
y_pred_t0 = yy[:, 0, 1]
print(f"y_true @t=0 std={np.nanstd(y_true_t0):.6g}, max={np.nanmax(y_true_t0):.6g}, min={np.nanmin(y_true_t0):.6g}")
print(f"y_pred @t=0 std={np.nanstd(y_pred_t0):.6g}, max={np.nanmax(y_pred_t0):.6g}, min={np.nanmin(y_pred_t0):.6g}")
y_true @t=0 std=8.64258, max=112.408, min=0.314451 y_pred @t=0 std=2.35492, max=19.8502, min=1.52246
Test 1 task with Vanila MLP with CV¶
In [142]:
import os
save_checkpoint_SMLP_model_1_CV_file_path = os.path.join(
root_folder,
objects_relative_path,
"checkpoint_SMLP_model_1_CV.pkl"
)
save_test_SMLP_model_1_CV_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_SMLP_model_1_CV.pkl"
)
param_grid_test_SMLP_1_CV_aapl_cv = {
"dropout": [0.2],
"lr": [3e-5],
"batch_size": [128, 256],
"epochs": [3],
"resume": [True],
"verbose": [True]
}
t1CV_SMLP_results, t1CV_SMLP_nested_results, t1CV_SMLP_best_model, t1CV_SMLP_best_params, _ = train_and_evaluate_model(
model_type="Simple_MLP",
X_price=aapl_X_price,
X_time=aapl_X_time,
y=aapl_y,
no_tasks=1,
use_nested_cv=True,
flatten=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
save_model_path=save_test_SMLP_model_1_CV_file_path,
lr=3e-6,
epochs=150,
batch_size=64,
verbose=True,
hidden_layers=3,
outer_folds=2,
inner_folds=2,
param_grid=param_grid_test_SMLP_1_CV_aapl_cv,
checkpoint_path= save_checkpoint_SMLP_model_1_CV_file_path,
hidden_dim=128
)
model parameter(s) from the grid will overwrite any overlapping parameters provided directly to this function Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 1 Nested cross-validation with grid search: Outer fold 1: Using device: mps Size of the X data for training in the inner fold 1: (454, 1380) Size of the Y data for training in the inner fold 1: (454, 1) Epoch 1: Train loss = 0.3332, Val loss = 0.4807 Epoch 2: Train loss = 0.3200, Val loss = 0.4678 Epoch 3: Train loss = 0.3094, Val loss = 0.4559 Using device: mps Size of the X data for training in the inner fold 2: (455, 1380) Size of the Y data for training in the inner fold 2: (455, 1) Epoch 1: Train loss = 0.3982, Val loss = 0.3572 Epoch 2: Train loss = 0.3926, Val loss = 0.3430 Epoch 3: Train loss = 0.3811, Val loss = 0.3282 Using device: mps Size of the X data for training in the inner fold 1: (454, 1380) Size of the Y data for training in the inner fold 1: (454, 1) Epoch 1: Train loss = 0.2928, Val loss = 0.4498 Epoch 2: Train loss = 0.2900, Val loss = 0.4431 Epoch 3: Train loss = 0.2851, Val loss = 0.4366 Using device: mps Size of the X data for training in the inner fold 2: (455, 1380) Size of the Y data for training in the inner fold 2: (455, 1) Epoch 1: Train loss = 0.4360, Val loss = 0.3942 Epoch 2: Train loss = 0.4318, Val loss = 0.3870 Epoch 3: Train loss = 0.4253, Val loss = 0.3796 Size of the X data for training in the outer fold: (908, 1380) Size of the Y data for training in the outer fold: (908, 1) Using device: mps Epoch 1: Train loss = 0.2409, Val loss = 0.2667 Epoch 2: Train loss = 0.2322, Val loss = 0.2549 Epoch 3: Train loss = 0.2207, Val loss = 0.2424 Outer Fold 1 — Task 1 Metrics: 1 day(s) MAE (log-var) : 1.2729 1 day(s) RMSE (log-var) : 1.5876 1 day(s) R2 (log-var) : -1.4775 1 day(s) MAE (var) : 3.8590 1 day(s) RMSE (var) : 10.3278 1 day(s) R2 (var) : -0.1593 1 day(s) QLIKE (var) : 0.6606 full horizon MAE (log-var) : 1.2729 full horizon RMSE (log-var) : 1.5876 full horizon R2 (log-var) : -1.4775 full horizon MAE (var) : 3.8590 full horizon RMSE (var) : 10.3278 full horizon R2 (var) : -0.1593 full horizon QLIKE (var) : 0.6606 Outer fold 2: Using device: mps Size of the X data for training in the inner fold 1: (454, 1380) Size of the Y data for training in the inner fold 1: (454, 1) Epoch 1: Train loss = 0.1575, Val loss = 0.1674 Epoch 2: Train loss = 0.1438, Val loss = 0.1506 Epoch 3: Train loss = 0.1335, Val loss = 0.1335 Using device: mps Size of the X data for training in the inner fold 2: (455, 1380) Size of the Y data for training in the inner fold 2: (455, 1) Epoch 1: Train loss = 0.2713, Val loss = 0.2705 Epoch 2: Train loss = 0.2607, Val loss = 0.2520 Epoch 3: Train loss = 0.2477, Val loss = 0.2349 Using device: mps Size of the X data for training in the inner fold 1: (454, 1380) Size of the Y data for training in the inner fold 1: (454, 1) Epoch 1: Train loss = 0.2942, Val loss = 0.3100 Epoch 2: Train loss = 0.2901, Val loss = 0.3045 Epoch 3: Train loss = 0.2860, Val loss = 0.2997 Using device: mps Size of the X data for training in the inner fold 2: (455, 1380) Size of the Y data for training in the inner fold 2: (455, 1) Epoch 1: Train loss = 0.3083, Val loss = 0.3238 Epoch 2: Train loss = 0.3024, Val loss = 0.3144 Epoch 3: Train loss = 0.2943, Val loss = 0.3036 Size of the X data for training in the outer fold: (908, 1380) Size of the Y data for training in the outer fold: (908, 1) Using device: mps Epoch 1: Train loss = 0.2239, Val loss = 0.2457 Epoch 2: Train loss = 0.2025, Val loss = 0.2139 Epoch 3: Train loss = 0.1748, Val loss = 0.1794 Outer Fold 2 — Task 1 Metrics: 1 day(s) MAE (log-var) : 1.6916 1 day(s) RMSE (log-var) : 1.8694 1 day(s) R2 (log-var) : -4.3799 1 day(s) MAE (var) : 3.1747 1 day(s) RMSE (var) : 7.0885 1 day(s) R2 (var) : -0.2496 1 day(s) QLIKE (var) : 0.4148 full horizon MAE (log-var) : 1.6916 full horizon RMSE (log-var) : 1.8694 full horizon R2 (log-var) : -4.3799 full horizon MAE (var) : 3.1747 full horizon RMSE (var) : 7.0885 full horizon R2 (var) : -0.2496 full horizon QLIKE (var) : 0.4148 Nested CV Average Metrics with 95% CI: Nested 1 day(s) MAE (log-var) : 1.4822 Nested 1 day(s) MAE (log-var) 95% CI : (-1.1780, 4.1425) Nested 1 day(s) MAE (var) : 3.5169 Nested 1 day(s) MAE (var) 95% CI : (-0.8311, 7.8648) Nested 1 day(s) QLIKE (var) : 0.5377 Nested 1 day(s) QLIKE (var) 95% CI : (-1.0238, 2.0993) Nested 1 day(s) R2 (log-var) : -2.9287 Nested 1 day(s) R2 (log-var) 95% CI : (-21.3679, 15.5104) Nested 1 day(s) R2 (var) : -0.2045 Nested 1 day(s) R2 (var) 95% CI : (-0.7781, 0.3692) Nested 1 day(s) RMSE (log-var) : 1.7285 Nested 1 day(s) RMSE (log-var) 95% CI : (-0.0614, 3.5185) Nested 1 day(s) RMSE (var) : 8.7081 Nested 1 day(s) RMSE (var) 95% CI : (-11.8710, 29.2873) Nested full horizon MAE (log-var) : 1.4822 Nested full horizon MAE (log-var) 95% CI : (-1.1780, 4.1425) Nested full horizon MAE (var) : 3.5169 Nested full horizon MAE (var) 95% CI : (-0.8311, 7.8648) Nested full horizon QLIKE (var) : 0.5377 Nested full horizon QLIKE (var) 95% CI : (-1.0238, 2.0993) Nested full horizon R2 (log-var) : -2.9287 Nested full horizon R2 (log-var) 95% CI : (-21.3679, 15.5104) Nested full horizon R2 (var) : -0.2045 Nested full horizon R2 (var) 95% CI : (-0.7781, 0.3692) Nested full horizon RMSE (log-var) : 1.7285 Nested full horizon RMSE (log-var) 95% CI : (-0.0614, 3.5185) Nested full horizon RMSE (var) : 8.7081 Nested full horizon RMSE (var) 95% CI : (-11.8710, 29.2873) Best parameters found via nested CV: batch_size: 128 dropout: 0.2 epochs: 3 lr: 3e-05 resume: True verbose: True Refitting the final model with the best parameters found in the nested CV: Using device: mps Size of the X data for training: (1453, 1380) Size of the Y data for training: (1453, 1) Final refit model parameters (after nested CV): input_dim: 1380 output_dim: 1 hidden_layers: 3 dropout: 0.20000000 lr: 0.00003000 epochs: 3 batch_size: 128 device: mps verbose: True no_tasks: 1 l2_weight: 0.00000000 patience: 10 min_epochs: 50 min_delta: 0.00010000 hidden_dim: 128 Epoch 1: Train loss = 0.0901, Val loss = 0.0448 Epoch 2: Train loss = 0.0747, Val loss = 0.0461 Epoch 3: Train loss = 0.0686, Val loss = 0.0571 Results after refitting the final model: Task 1 Final Metrics: 1 day(s) MAE (log-var) : 0.6673 1 day(s) RMSE (log-var) : 0.8634 1 day(s) R2 (log-var) : -0.1150 1 day(s) MAE (var) : 2.5451 1 day(s) RMSE (var) : 8.6897 1 day(s) R2 (var) : -0.0109 1 day(s) QLIKE (var) : 0.5363 full horizon MAE (log-var) : 0.6673 full horizon RMSE (log-var) : 0.8634 full horizon R2 (log-var) : -0.1150 full horizon MAE (var) : 2.5451 full horizon RMSE (var) : 8.6897 full horizon R2 (var) : -0.0109 full horizon QLIKE (var) : 0.5363 Final nested-CV model saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_SMLP_model_1_CV.pkl
OHO with parameter search - Vanila MLP - 1 task - data 3 - horizon 1¶
In [ ]:
import os
save_test_OHO_SMLP_model_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_OHO_SMLP_model.pkl"
)
param_grid = {
"lr": [5e-5, 5e-4],
"dropout": [0, 0.1],
"l2_weight": [5e-5, 5e-4],
"batch_size": [256, 512],
"hidden_layers": [2, 4],
"hidden_dim": [64, 256]
}
t1d3_OHO_SMLP_results, t1d3_OHO_SMLP_nested_results, t1d3_OHO_SMLP_best_model, t1d3_OHO_SMLP_best_params, t1d3_OHO_SMLP_y_data = train_and_evaluate_model(
param_grid=param_grid,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Simple_MLP",
# data for each scenario
X_price=eur3_X_price,
X_time=eur3_X_time,
y=eur3_y,
# specify these for each data
no_tasks=1,
flatten=True,
time_horizon=1,
# these have to be adjusted for some models
patience=10,
epochs=100,
min_epochs=50,
# saving model
save_model_path=save_test_OHO_SMLP_model_file_path,
# these go into the parameter grid
lr=1e-5,
dropout=0,
l2_weight=1e-4,
batch_size=64,
hidden_layers=3,
hidden_dim=256
)
OHO with parameter search - Vanila MLP - 1 task - data 3 - horizon 28¶
In [ ]:
import os
save_test_OHO_SMLP_model_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_OHO_SMLP_model.pkl"
)
param_grid = {
"lr": [5e-5, 5e-4],
"dropout": [0, 0.1],
"l2_weight": [5e-5, 5e-4],
"batch_size": [256, 512],
"hidden_layers": [2, 4],
"hidden_dim": [64, 256]
}
t1d3_OHO_SMLP_results, t1d3_OHO_SMLP_nested_results, t1d3_OHO_SMLP_best_model, t1d3_OHO_SMLP_best_params, t1d3_OHO_SMLP_y_data = train_and_evaluate_model(
param_grid=param_grid,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Simple_MLP",
# data for each scenario
X_price=eur3_X_price,
X_time=eur3_X_time,
y=eur3_y,
# specify these for each data
no_tasks=1,
flatten=True,
time_horizon=28,
# these have to be adjusted for some models
patience=10,
epochs=100,
min_epochs=50,
# saving model
save_model_path=save_test_OHO_SMLP_model_file_path,
# these go into the parameter grid
lr=1e-5,
dropout=0,
l2_weight=1e-4,
batch_size=64,
hidden_layers=3,
hidden_dim=256
)
OHO with APPL4 Vanila LSTM¶
In [336]:
import os
save_test_OHO_SMLP_model_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_OHO_SMLP_model.pkl"
)
param_grid = {
"lr": [5e-5, 5e-4],
"dropout": [0, 0.1],
"l2_weight": [5e-5, 5e-4],
"batch_size": [256, 512],
"hidden_layers": [2, 4],
"hidden_dim": [64, 256]
}
t1d3_OHO_SMLP_results, t1d3_OHO_SMLP_nested_results, t1d3_OHO_SMLP_best_model, t1d3_OHO_SMLP_best_params, t1d3_OHO_SMLP_y_data = train_and_evaluate_model(
param_grid=param_grid,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Simple_MLP",
# data for each scenario
X_price=appl4_X_price,
X_time=appl4_X_time,
y=appl4_y,
# specify these for each data
no_tasks=1,
flatten=True,
time_horizon=28,
# these have to be adjusted for some models
patience=10,
epochs=100,
min_epochs=50,
# saving model
save_model_path=save_test_OHO_SMLP_model_file_path,
# these go into the parameter grid
lr=1e-5,
dropout=0,
l2_weight=1e-4,
batch_size=64,
hidden_layers=3,
hidden_dim=256
)
model parameter(s) from the grid will overwrite any overlapping parameters provided directly to this function Using device: mps Batch size for y: 2018 Time steps for y: 28 Features for y: 1 Single holdout: one train/test split, param search on a single train/val (early-stop) split. Using device: mps Epoch 1: Train loss = 0.0384, Val loss = 0.0213 Epoch 2: Train loss = 0.0344, Val loss = 0.0186 Epoch 3: Train loss = 0.0312, Val loss = 0.0163 Epoch 4: Train loss = 0.0281, Val loss = 0.0141 Epoch 5: Train loss = 0.0253, Val loss = 0.0124 Epoch 6: Train loss = 0.0226, Val loss = 0.0115 Epoch 7: Train loss = 0.0207, Val loss = 0.0110 Epoch 8: Train loss = 0.0190, Val loss = 0.0107 Epoch 9: Train loss = 0.0175, Val loss = 0.0104 Epoch 10: Train loss = 0.0162, Val loss = 0.0098 Epoch 11: Train loss = 0.0149, Val loss = 0.0091 Epoch 12: Train loss = 0.0138, Val loss = 0.0084 Epoch 13: Train loss = 0.0126, Val loss = 0.0078 Epoch 14: Train loss = 0.0117, Val loss = 0.0074 Epoch 15: Train loss = 0.0107, Val loss = 0.0072 Epoch 16: Train loss = 0.0100, Val loss = 0.0073 Epoch 17: Train loss = 0.0094, Val loss = 0.0073 Epoch 18: Train loss = 0.0089, Val loss = 0.0073 Epoch 19: Train loss = 0.0085, Val loss = 0.0072 Epoch 20: Train loss = 0.0081, Val loss = 0.0071 Epoch 21: Train loss = 0.0079, Val loss = 0.0070 Epoch 22: Train loss = 0.0076, Val loss = 0.0070 Epoch 23: Train loss = 0.0074, Val loss = 0.0070 Epoch 24: Train loss = 0.0072, Val loss = 0.0071 Epoch 25: Train loss = 0.0071, Val loss = 0.0072 Epoch 26: Train loss = 0.0070, Val loss = 0.0072 Epoch 27: Train loss = 0.0069, Val loss = 0.0073 Epoch 28: Train loss = 0.0069, Val loss = 0.0074 Epoch 29: Train loss = 0.0069, Val loss = 0.0075 Epoch 30: Train loss = 0.0068, Val loss = 0.0076 Epoch 31: Train loss = 0.0068, Val loss = 0.0077 Epoch 32: Train loss = 0.0067, Val loss = 0.0077 Epoch 33: Train loss = 0.0067, Val loss = 0.0078 Epoch 34: Train loss = 0.0067, Val loss = 0.0078 Epoch 35: Train loss = 0.0067, Val loss = 0.0079 Epoch 36: Train loss = 0.0067, Val loss = 0.0080 Epoch 37: Train loss = 0.0067, Val loss = 0.0080 Epoch 38: Train loss = 0.0066, Val loss = 0.0081 Epoch 39: Train loss = 0.0066, Val loss = 0.0082 Epoch 40: Train loss = 0.0066, Val loss = 0.0081 Epoch 41: Train loss = 0.0066, Val loss = 0.0081 Epoch 42: Train loss = 0.0066, Val loss = 0.0083 Epoch 43: Train loss = 0.0065, Val loss = 0.0084 Epoch 44: Train loss = 0.0065, Val loss = 0.0084 Epoch 45: Train loss = 0.0065, Val loss = 0.0083 Epoch 46: Train loss = 0.0065, Val loss = 0.0084 Epoch 47: Train loss = 0.0065, Val loss = 0.0085 Epoch 48: Train loss = 0.0065, Val loss = 0.0087 Epoch 49: Train loss = 0.0065, Val loss = 0.0085 Epoch 50: Train loss = 0.0065, Val loss = 0.0086 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0439, Val loss = 0.0217 Epoch 2: Train loss = 0.0249, Val loss = 0.0201 Epoch 3: Train loss = 0.0150, Val loss = 0.0125 Epoch 4: Train loss = 0.0096, Val loss = 0.0110 Epoch 5: Train loss = 0.0077, Val loss = 0.0101 Epoch 6: Train loss = 0.0068, Val loss = 0.0092 Epoch 7: Train loss = 0.0068, Val loss = 0.0093 Epoch 8: Train loss = 0.0067, Val loss = 0.0095 Epoch 9: Train loss = 0.0066, Val loss = 0.0093 Epoch 10: Train loss = 0.0065, Val loss = 0.0100 Epoch 11: Train loss = 0.0065, Val loss = 0.0095 Epoch 12: Train loss = 0.0065, Val loss = 0.0099 Epoch 13: Train loss = 0.0064, Val loss = 0.0094 Epoch 14: Train loss = 0.0064, Val loss = 0.0097 Epoch 15: Train loss = 0.0064, Val loss = 0.0095 Epoch 16: Train loss = 0.0063, Val loss = 0.0095 Epoch 17: Train loss = 0.0063, Val loss = 0.0097 Epoch 18: Train loss = 0.0063, Val loss = 0.0093 Epoch 19: Train loss = 0.0063, Val loss = 0.0101 Epoch 20: Train loss = 0.0063, Val loss = 0.0094 Epoch 21: Train loss = 0.0063, Val loss = 0.0100 Epoch 22: Train loss = 0.0062, Val loss = 0.0099 Epoch 23: Train loss = 0.0062, Val loss = 0.0089 Epoch 24: Train loss = 0.0062, Val loss = 0.0103 Epoch 25: Train loss = 0.0062, Val loss = 0.0092 Epoch 26: Train loss = 0.0062, Val loss = 0.0091 Epoch 27: Train loss = 0.0062, Val loss = 0.0103 Epoch 28: Train loss = 0.0062, Val loss = 0.0088 Epoch 29: Train loss = 0.0061, Val loss = 0.0097 Epoch 30: Train loss = 0.0061, Val loss = 0.0103 Epoch 31: Train loss = 0.0061, Val loss = 0.0089 Epoch 32: Train loss = 0.0061, Val loss = 0.0095 Epoch 33: Train loss = 0.0060, Val loss = 0.0100 Epoch 34: Train loss = 0.0060, Val loss = 0.0096 Epoch 35: Train loss = 0.0060, Val loss = 0.0089 Epoch 36: Train loss = 0.0059, Val loss = 0.0090 Epoch 37: Train loss = 0.0060, Val loss = 0.0097 Epoch 38: Train loss = 0.0059, Val loss = 0.0100 Epoch 39: Train loss = 0.0059, Val loss = 0.0092 Epoch 40: Train loss = 0.0059, Val loss = 0.0089 Epoch 41: Train loss = 0.0059, Val loss = 0.0095 Epoch 42: Train loss = 0.0059, Val loss = 0.0103 Epoch 43: Train loss = 0.0058, Val loss = 0.0092 Epoch 44: Train loss = 0.0058, Val loss = 0.0079 Epoch 45: Train loss = 0.0058, Val loss = 0.0090 Epoch 46: Train loss = 0.0058, Val loss = 0.0095 Epoch 47: Train loss = 0.0057, Val loss = 0.0086 Epoch 48: Train loss = 0.0058, Val loss = 0.0092 Epoch 49: Train loss = 0.0057, Val loss = 0.0089 Epoch 50: Train loss = 0.0057, Val loss = 0.0086 Epoch 51: Train loss = 0.0057, Val loss = 0.0083 Epoch 52: Train loss = 0.0057, Val loss = 0.0078 Epoch 53: Train loss = 0.0056, Val loss = 0.0076 Epoch 54: Train loss = 0.0056, Val loss = 0.0071 Epoch 55: Train loss = 0.0056, Val loss = 0.0068 Epoch 56: Train loss = 0.0056, Val loss = 0.0069 Epoch 57: Train loss = 0.0056, Val loss = 0.0063 Epoch 58: Train loss = 0.0055, Val loss = 0.0063 Epoch 59: Train loss = 0.0055, Val loss = 0.0069 Epoch 60: Train loss = 0.0054, Val loss = 0.0069 Epoch 61: Train loss = 0.0055, Val loss = 0.0070 Epoch 62: Train loss = 0.0054, Val loss = 0.0064 Epoch 63: Train loss = 0.0056, Val loss = 0.0083 Epoch 64: Train loss = 0.0055, Val loss = 0.0079 Epoch 65: Train loss = 0.0054, Val loss = 0.0075 Epoch 66: Train loss = 0.0054, Val loss = 0.0061 Epoch 67: Train loss = 0.0053, Val loss = 0.0059 Epoch 68: Train loss = 0.0053, Val loss = 0.0053 Epoch 69: Train loss = 0.0053, Val loss = 0.0055 Epoch 70: Train loss = 0.0053, Val loss = 0.0058 Epoch 71: Train loss = 0.0052, Val loss = 0.0068 Epoch 72: Train loss = 0.0053, Val loss = 0.0057 Epoch 73: Train loss = 0.0052, Val loss = 0.0048 Epoch 74: Train loss = 0.0053, Val loss = 0.0052 Epoch 75: Train loss = 0.0052, Val loss = 0.0066 Epoch 76: Train loss = 0.0052, Val loss = 0.0062 Epoch 77: Train loss = 0.0052, Val loss = 0.0063 Epoch 78: Train loss = 0.0051, Val loss = 0.0054 Epoch 79: Train loss = 0.0051, Val loss = 0.0060 Epoch 80: Train loss = 0.0051, Val loss = 0.0058 Epoch 81: Train loss = 0.0052, Val loss = 0.0055 Epoch 82: Train loss = 0.0051, Val loss = 0.0068 Epoch 83: Train loss = 0.0051, Val loss = 0.0060 Early stopping triggered at epoch 83. Using device: mps Epoch 1: Train loss = 0.0505, Val loss = 0.0313 Epoch 2: Train loss = 0.0479, Val loss = 0.0297 Epoch 3: Train loss = 0.0460, Val loss = 0.0282 Epoch 4: Train loss = 0.0440, Val loss = 0.0268 Epoch 5: Train loss = 0.0422, Val loss = 0.0254 Epoch 6: Train loss = 0.0403, Val loss = 0.0240 Epoch 7: Train loss = 0.0380, Val loss = 0.0225 Epoch 8: Train loss = 0.0356, Val loss = 0.0208 Epoch 9: Train loss = 0.0331, Val loss = 0.0188 Epoch 10: Train loss = 0.0302, Val loss = 0.0169 Epoch 11: Train loss = 0.0274, Val loss = 0.0151 Epoch 12: Train loss = 0.0248, Val loss = 0.0137 Epoch 13: Train loss = 0.0224, Val loss = 0.0126 Epoch 14: Train loss = 0.0202, Val loss = 0.0117 Epoch 15: Train loss = 0.0183, Val loss = 0.0108 Epoch 16: Train loss = 0.0163, Val loss = 0.0100 Epoch 17: Train loss = 0.0148, Val loss = 0.0093 Epoch 18: Train loss = 0.0132, Val loss = 0.0088 Epoch 19: Train loss = 0.0120, Val loss = 0.0087 Epoch 20: Train loss = 0.0111, Val loss = 0.0087 Epoch 21: Train loss = 0.0103, Val loss = 0.0088 Epoch 22: Train loss = 0.0096, Val loss = 0.0088 Epoch 23: Train loss = 0.0091, Val loss = 0.0088 Epoch 24: Train loss = 0.0088, Val loss = 0.0087 Epoch 25: Train loss = 0.0084, Val loss = 0.0087 Epoch 26: Train loss = 0.0081, Val loss = 0.0088 Epoch 27: Train loss = 0.0079, Val loss = 0.0088 Epoch 28: Train loss = 0.0077, Val loss = 0.0088 Epoch 29: Train loss = 0.0076, Val loss = 0.0087 Epoch 30: Train loss = 0.0074, Val loss = 0.0088 Epoch 31: Train loss = 0.0072, Val loss = 0.0088 Epoch 32: Train loss = 0.0072, Val loss = 0.0087 Epoch 33: Train loss = 0.0070, Val loss = 0.0088 Epoch 34: Train loss = 0.0069, Val loss = 0.0089 Epoch 35: Train loss = 0.0069, Val loss = 0.0090 Epoch 36: Train loss = 0.0068, Val loss = 0.0089 Epoch 37: Train loss = 0.0068, Val loss = 0.0090 Epoch 38: Train loss = 0.0068, Val loss = 0.0091 Epoch 39: Train loss = 0.0067, Val loss = 0.0091 Epoch 40: Train loss = 0.0067, Val loss = 0.0092 Epoch 41: Train loss = 0.0066, Val loss = 0.0091 Epoch 42: Train loss = 0.0066, Val loss = 0.0091 Epoch 43: Train loss = 0.0066, Val loss = 0.0092 Epoch 44: Train loss = 0.0066, Val loss = 0.0093 Epoch 45: Train loss = 0.0066, Val loss = 0.0093 Epoch 46: Train loss = 0.0065, Val loss = 0.0093 Epoch 47: Train loss = 0.0066, Val loss = 0.0093 Epoch 48: Train loss = 0.0066, Val loss = 0.0094 Epoch 49: Train loss = 0.0065, Val loss = 0.0095 Epoch 50: Train loss = 0.0065, Val loss = 0.0094 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0470, Val loss = 0.0237 Epoch 2: Train loss = 0.0300, Val loss = 0.0254 Epoch 3: Train loss = 0.0200, Val loss = 0.0140 Epoch 4: Train loss = 0.0127, Val loss = 0.0098 Epoch 5: Train loss = 0.0089, Val loss = 0.0110 Epoch 6: Train loss = 0.0073, Val loss = 0.0090 Epoch 7: Train loss = 0.0069, Val loss = 0.0089 Epoch 8: Train loss = 0.0068, Val loss = 0.0099 Epoch 9: Train loss = 0.0066, Val loss = 0.0084 Epoch 10: Train loss = 0.0065, Val loss = 0.0101 Epoch 11: Train loss = 0.0065, Val loss = 0.0090 Epoch 12: Train loss = 0.0065, Val loss = 0.0098 Epoch 13: Train loss = 0.0064, Val loss = 0.0098 Epoch 14: Train loss = 0.0064, Val loss = 0.0100 Epoch 15: Train loss = 0.0063, Val loss = 0.0098 Epoch 16: Train loss = 0.0063, Val loss = 0.0096 Epoch 17: Train loss = 0.0063, Val loss = 0.0099 Epoch 18: Train loss = 0.0063, Val loss = 0.0097 Epoch 19: Train loss = 0.0063, Val loss = 0.0099 Epoch 20: Train loss = 0.0062, Val loss = 0.0097 Epoch 21: Train loss = 0.0063, Val loss = 0.0102 Epoch 22: Train loss = 0.0062, Val loss = 0.0094 Epoch 23: Train loss = 0.0062, Val loss = 0.0101 Epoch 24: Train loss = 0.0062, Val loss = 0.0095 Epoch 25: Train loss = 0.0062, Val loss = 0.0106 Epoch 26: Train loss = 0.0062, Val loss = 0.0086 Epoch 27: Train loss = 0.0062, Val loss = 0.0109 Epoch 28: Train loss = 0.0061, Val loss = 0.0093 Epoch 29: Train loss = 0.0061, Val loss = 0.0098 Epoch 30: Train loss = 0.0060, Val loss = 0.0097 Epoch 31: Train loss = 0.0061, Val loss = 0.0089 Epoch 32: Train loss = 0.0061, Val loss = 0.0090 Epoch 33: Train loss = 0.0060, Val loss = 0.0099 Epoch 34: Train loss = 0.0060, Val loss = 0.0078 Epoch 35: Train loss = 0.0060, Val loss = 0.0101 Epoch 36: Train loss = 0.0060, Val loss = 0.0105 Epoch 37: Train loss = 0.0060, Val loss = 0.0087 Epoch 38: Train loss = 0.0059, Val loss = 0.0075 Epoch 39: Train loss = 0.0059, Val loss = 0.0087 Epoch 40: Train loss = 0.0059, Val loss = 0.0099 Epoch 41: Train loss = 0.0059, Val loss = 0.0098 Epoch 42: Train loss = 0.0058, Val loss = 0.0091 Epoch 43: Train loss = 0.0058, Val loss = 0.0082 Epoch 44: Train loss = 0.0058, Val loss = 0.0076 Epoch 45: Train loss = 0.0058, Val loss = 0.0080 Epoch 46: Train loss = 0.0058, Val loss = 0.0096 Epoch 47: Train loss = 0.0057, Val loss = 0.0086 Epoch 48: Train loss = 0.0057, Val loss = 0.0093 Epoch 49: Train loss = 0.0057, Val loss = 0.0084 Epoch 50: Train loss = 0.0057, Val loss = 0.0084 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0603, Val loss = 0.0412 Epoch 2: Train loss = 0.0592, Val loss = 0.0403 Epoch 3: Train loss = 0.0583, Val loss = 0.0395 Epoch 4: Train loss = 0.0573, Val loss = 0.0387 Epoch 5: Train loss = 0.0562, Val loss = 0.0379 Epoch 6: Train loss = 0.0556, Val loss = 0.0369 Epoch 7: Train loss = 0.0541, Val loss = 0.0357 Epoch 8: Train loss = 0.0527, Val loss = 0.0344 Epoch 9: Train loss = 0.0515, Val loss = 0.0330 Epoch 10: Train loss = 0.0497, Val loss = 0.0315 Epoch 11: Train loss = 0.0478, Val loss = 0.0298 Epoch 12: Train loss = 0.0459, Val loss = 0.0280 Epoch 13: Train loss = 0.0435, Val loss = 0.0261 Epoch 14: Train loss = 0.0411, Val loss = 0.0241 Epoch 15: Train loss = 0.0384, Val loss = 0.0221 Epoch 16: Train loss = 0.0359, Val loss = 0.0203 Epoch 17: Train loss = 0.0329, Val loss = 0.0189 Epoch 18: Train loss = 0.0303, Val loss = 0.0179 Epoch 19: Train loss = 0.0279, Val loss = 0.0171 Epoch 20: Train loss = 0.0257, Val loss = 0.0164 Epoch 21: Train loss = 0.0237, Val loss = 0.0158 Epoch 22: Train loss = 0.0218, Val loss = 0.0151 Epoch 23: Train loss = 0.0201, Val loss = 0.0144 Epoch 24: Train loss = 0.0185, Val loss = 0.0137 Epoch 25: Train loss = 0.0171, Val loss = 0.0131 Epoch 26: Train loss = 0.0158, Val loss = 0.0126 Epoch 27: Train loss = 0.0147, Val loss = 0.0121 Epoch 28: Train loss = 0.0138, Val loss = 0.0117 Epoch 29: Train loss = 0.0129, Val loss = 0.0112 Epoch 30: Train loss = 0.0121, Val loss = 0.0109 Epoch 31: Train loss = 0.0114, Val loss = 0.0107 Epoch 32: Train loss = 0.0108, Val loss = 0.0103 Epoch 33: Train loss = 0.0103, Val loss = 0.0100 Epoch 34: Train loss = 0.0099, Val loss = 0.0097 Epoch 35: Train loss = 0.0094, Val loss = 0.0096 Epoch 36: Train loss = 0.0090, Val loss = 0.0096 Epoch 37: Train loss = 0.0086, Val loss = 0.0095 Epoch 38: Train loss = 0.0083, Val loss = 0.0093 Epoch 39: Train loss = 0.0081, Val loss = 0.0092 Epoch 40: Train loss = 0.0078, Val loss = 0.0091 Epoch 41: Train loss = 0.0076, Val loss = 0.0090 Epoch 42: Train loss = 0.0074, Val loss = 0.0090 Epoch 43: Train loss = 0.0072, Val loss = 0.0090 Epoch 44: Train loss = 0.0072, Val loss = 0.0090 Epoch 45: Train loss = 0.0070, Val loss = 0.0090 Epoch 46: Train loss = 0.0069, Val loss = 0.0090 Epoch 47: Train loss = 0.0069, Val loss = 0.0089 Epoch 48: Train loss = 0.0068, Val loss = 0.0090 Epoch 49: Train loss = 0.0068, Val loss = 0.0090 Epoch 50: Train loss = 0.0067, Val loss = 0.0090 Epoch 51: Train loss = 0.0067, Val loss = 0.0090 Epoch 52: Train loss = 0.0067, Val loss = 0.0090 Epoch 53: Train loss = 0.0066, Val loss = 0.0089 Epoch 54: Train loss = 0.0067, Val loss = 0.0089 Epoch 55: Train loss = 0.0067, Val loss = 0.0091 Epoch 56: Train loss = 0.0066, Val loss = 0.0090 Early stopping triggered at epoch 56. Using device: mps Epoch 1: Train loss = 0.0473, Val loss = 0.0259 Epoch 2: Train loss = 0.0379, Val loss = 0.0178 Epoch 3: Train loss = 0.0260, Val loss = 0.0127 Epoch 4: Train loss = 0.0162, Val loss = 0.0174 Epoch 5: Train loss = 0.0111, Val loss = 0.0099 Epoch 6: Train loss = 0.0083, Val loss = 0.0074 Epoch 7: Train loss = 0.0074, Val loss = 0.0099 Epoch 8: Train loss = 0.0070, Val loss = 0.0078 Epoch 9: Train loss = 0.0069, Val loss = 0.0081 Epoch 10: Train loss = 0.0067, Val loss = 0.0092 Epoch 11: Train loss = 0.0067, Val loss = 0.0078 Epoch 12: Train loss = 0.0066, Val loss = 0.0091 Epoch 13: Train loss = 0.0066, Val loss = 0.0088 Epoch 14: Train loss = 0.0066, Val loss = 0.0091 Epoch 15: Train loss = 0.0065, Val loss = 0.0095 Epoch 16: Train loss = 0.0065, Val loss = 0.0092 Epoch 17: Train loss = 0.0065, Val loss = 0.0098 Epoch 18: Train loss = 0.0065, Val loss = 0.0095 Epoch 19: Train loss = 0.0065, Val loss = 0.0096 Epoch 20: Train loss = 0.0065, Val loss = 0.0099 Epoch 21: Train loss = 0.0064, Val loss = 0.0096 Epoch 22: Train loss = 0.0065, Val loss = 0.0106 Epoch 23: Train loss = 0.0064, Val loss = 0.0092 Epoch 24: Train loss = 0.0064, Val loss = 0.0110 Epoch 25: Train loss = 0.0063, Val loss = 0.0096 Epoch 26: Train loss = 0.0063, Val loss = 0.0107 Epoch 27: Train loss = 0.0063, Val loss = 0.0109 Epoch 28: Train loss = 0.0063, Val loss = 0.0096 Epoch 29: Train loss = 0.0063, Val loss = 0.0106 Epoch 30: Train loss = 0.0062, Val loss = 0.0109 Epoch 31: Train loss = 0.0063, Val loss = 0.0102 Epoch 32: Train loss = 0.0062, Val loss = 0.0096 Epoch 33: Train loss = 0.0062, Val loss = 0.0109 Epoch 34: Train loss = 0.0062, Val loss = 0.0097 Epoch 35: Train loss = 0.0061, Val loss = 0.0098 Epoch 36: Train loss = 0.0061, Val loss = 0.0114 Epoch 37: Train loss = 0.0061, Val loss = 0.0107 Epoch 38: Train loss = 0.0060, Val loss = 0.0097 Epoch 39: Train loss = 0.0060, Val loss = 0.0097 Epoch 40: Train loss = 0.0060, Val loss = 0.0094 Epoch 41: Train loss = 0.0059, Val loss = 0.0093 Epoch 42: Train loss = 0.0059, Val loss = 0.0091 Epoch 43: Train loss = 0.0059, Val loss = 0.0079 Epoch 44: Train loss = 0.0060, Val loss = 0.0093 Epoch 45: Train loss = 0.0058, Val loss = 0.0088 Epoch 46: Train loss = 0.0058, Val loss = 0.0101 Epoch 47: Train loss = 0.0058, Val loss = 0.0091 Epoch 48: Train loss = 0.0058, Val loss = 0.0077 Epoch 49: Train loss = 0.0058, Val loss = 0.0072 Epoch 50: Train loss = 0.0058, Val loss = 0.0063 Epoch 51: Train loss = 0.0057, Val loss = 0.0070 Epoch 52: Train loss = 0.0058, Val loss = 0.0077 Epoch 53: Train loss = 0.0057, Val loss = 0.0079 Epoch 54: Train loss = 0.0057, Val loss = 0.0081 Epoch 55: Train loss = 0.0057, Val loss = 0.0082 Epoch 56: Train loss = 0.0056, Val loss = 0.0082 Epoch 57: Train loss = 0.0056, Val loss = 0.0081 Epoch 58: Train loss = 0.0055, Val loss = 0.0076 Epoch 59: Train loss = 0.0055, Val loss = 0.0069 Epoch 60: Train loss = 0.0055, Val loss = 0.0065 Early stopping triggered at epoch 60. Using device: mps Epoch 1: Train loss = 0.0558, Val loss = 0.0369 Epoch 2: Train loss = 0.0549, Val loss = 0.0361 Epoch 3: Train loss = 0.0536, Val loss = 0.0351 Epoch 4: Train loss = 0.0526, Val loss = 0.0341 Epoch 5: Train loss = 0.0516, Val loss = 0.0332 Epoch 6: Train loss = 0.0508, Val loss = 0.0324 Epoch 7: Train loss = 0.0495, Val loss = 0.0315 Epoch 8: Train loss = 0.0483, Val loss = 0.0305 Epoch 9: Train loss = 0.0474, Val loss = 0.0293 Epoch 10: Train loss = 0.0458, Val loss = 0.0281 Epoch 11: Train loss = 0.0444, Val loss = 0.0268 Epoch 12: Train loss = 0.0426, Val loss = 0.0253 Epoch 13: Train loss = 0.0409, Val loss = 0.0239 Epoch 14: Train loss = 0.0391, Val loss = 0.0223 Epoch 15: Train loss = 0.0367, Val loss = 0.0206 Epoch 16: Train loss = 0.0344, Val loss = 0.0188 Epoch 17: Train loss = 0.0320, Val loss = 0.0171 Epoch 18: Train loss = 0.0293, Val loss = 0.0155 Epoch 19: Train loss = 0.0266, Val loss = 0.0141 Epoch 20: Train loss = 0.0238, Val loss = 0.0131 Epoch 21: Train loss = 0.0214, Val loss = 0.0124 Epoch 22: Train loss = 0.0191, Val loss = 0.0120 Epoch 23: Train loss = 0.0171, Val loss = 0.0117 Epoch 24: Train loss = 0.0154, Val loss = 0.0114 Epoch 25: Train loss = 0.0139, Val loss = 0.0109 Epoch 26: Train loss = 0.0126, Val loss = 0.0103 Epoch 27: Train loss = 0.0115, Val loss = 0.0097 Epoch 28: Train loss = 0.0105, Val loss = 0.0093 Epoch 29: Train loss = 0.0097, Val loss = 0.0091 Epoch 30: Train loss = 0.0091, Val loss = 0.0091 Epoch 31: Train loss = 0.0086, Val loss = 0.0090 Epoch 32: Train loss = 0.0082, Val loss = 0.0090 Epoch 33: Train loss = 0.0079, Val loss = 0.0090 Epoch 34: Train loss = 0.0076, Val loss = 0.0089 Epoch 35: Train loss = 0.0074, Val loss = 0.0087 Epoch 36: Train loss = 0.0073, Val loss = 0.0087 Epoch 37: Train loss = 0.0071, Val loss = 0.0089 Epoch 38: Train loss = 0.0070, Val loss = 0.0089 Epoch 39: Train loss = 0.0070, Val loss = 0.0088 Epoch 40: Train loss = 0.0068, Val loss = 0.0088 Epoch 41: Train loss = 0.0069, Val loss = 0.0087 Epoch 42: Train loss = 0.0068, Val loss = 0.0088 Epoch 43: Train loss = 0.0068, Val loss = 0.0088 Epoch 44: Train loss = 0.0067, Val loss = 0.0089 Epoch 45: Train loss = 0.0067, Val loss = 0.0088 Epoch 46: Train loss = 0.0067, Val loss = 0.0088 Epoch 47: Train loss = 0.0067, Val loss = 0.0089 Epoch 48: Train loss = 0.0066, Val loss = 0.0088 Epoch 49: Train loss = 0.0066, Val loss = 0.0088 Epoch 50: Train loss = 0.0066, Val loss = 0.0088 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0544, Val loss = 0.0298 Epoch 2: Train loss = 0.0399, Val loss = 0.0153 Epoch 3: Train loss = 0.0213, Val loss = 0.0217 Epoch 4: Train loss = 0.0134, Val loss = 0.0122 Epoch 5: Train loss = 0.0088, Val loss = 0.0066 Epoch 6: Train loss = 0.0077, Val loss = 0.0101 Epoch 7: Train loss = 0.0072, Val loss = 0.0095 Epoch 8: Train loss = 0.0069, Val loss = 0.0078 Epoch 9: Train loss = 0.0068, Val loss = 0.0094 Epoch 10: Train loss = 0.0067, Val loss = 0.0090 Epoch 11: Train loss = 0.0066, Val loss = 0.0086 Epoch 12: Train loss = 0.0065, Val loss = 0.0095 Epoch 13: Train loss = 0.0065, Val loss = 0.0087 Epoch 14: Train loss = 0.0065, Val loss = 0.0094 Epoch 15: Train loss = 0.0065, Val loss = 0.0096 Epoch 16: Train loss = 0.0065, Val loss = 0.0096 Epoch 17: Train loss = 0.0065, Val loss = 0.0099 Epoch 18: Train loss = 0.0064, Val loss = 0.0095 Epoch 19: Train loss = 0.0064, Val loss = 0.0101 Epoch 20: Train loss = 0.0064, Val loss = 0.0099 Epoch 21: Train loss = 0.0064, Val loss = 0.0098 Epoch 22: Train loss = 0.0064, Val loss = 0.0103 Epoch 23: Train loss = 0.0064, Val loss = 0.0100 Epoch 24: Train loss = 0.0064, Val loss = 0.0104 Epoch 25: Train loss = 0.0063, Val loss = 0.0103 Epoch 26: Train loss = 0.0063, Val loss = 0.0109 Epoch 27: Train loss = 0.0063, Val loss = 0.0098 Epoch 28: Train loss = 0.0063, Val loss = 0.0108 Epoch 29: Train loss = 0.0063, Val loss = 0.0103 Epoch 30: Train loss = 0.0062, Val loss = 0.0108 Epoch 31: Train loss = 0.0062, Val loss = 0.0101 Epoch 32: Train loss = 0.0063, Val loss = 0.0113 Epoch 33: Train loss = 0.0062, Val loss = 0.0101 Epoch 34: Train loss = 0.0062, Val loss = 0.0109 Epoch 35: Train loss = 0.0062, Val loss = 0.0102 Epoch 36: Train loss = 0.0062, Val loss = 0.0109 Epoch 37: Train loss = 0.0061, Val loss = 0.0108 Epoch 38: Train loss = 0.0061, Val loss = 0.0109 Epoch 39: Train loss = 0.0061, Val loss = 0.0112 Epoch 40: Train loss = 0.0061, Val loss = 0.0083 Epoch 41: Train loss = 0.0061, Val loss = 0.0107 Epoch 42: Train loss = 0.0061, Val loss = 0.0112 Epoch 43: Train loss = 0.0061, Val loss = 0.0096 Epoch 44: Train loss = 0.0061, Val loss = 0.0093 Epoch 45: Train loss = 0.0060, Val loss = 0.0093 Epoch 46: Train loss = 0.0060, Val loss = 0.0093 Epoch 47: Train loss = 0.0060, Val loss = 0.0100 Epoch 48: Train loss = 0.0059, Val loss = 0.0100 Epoch 49: Train loss = 0.0059, Val loss = 0.0093 Epoch 50: Train loss = 0.0059, Val loss = 0.0104 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0525, Val loss = 0.0299 Epoch 2: Train loss = 0.0451, Val loss = 0.0240 Epoch 3: Train loss = 0.0387, Val loss = 0.0189 Epoch 4: Train loss = 0.0322, Val loss = 0.0141 Epoch 5: Train loss = 0.0253, Val loss = 0.0103 Epoch 6: Train loss = 0.0186, Val loss = 0.0089 Epoch 7: Train loss = 0.0130, Val loss = 0.0100 Epoch 8: Train loss = 0.0095, Val loss = 0.0114 Epoch 9: Train loss = 0.0080, Val loss = 0.0115 Epoch 10: Train loss = 0.0074, Val loss = 0.0106 Epoch 11: Train loss = 0.0072, Val loss = 0.0099 Epoch 12: Train loss = 0.0071, Val loss = 0.0092 Epoch 13: Train loss = 0.0069, Val loss = 0.0091 Epoch 14: Train loss = 0.0068, Val loss = 0.0092 Epoch 15: Train loss = 0.0067, Val loss = 0.0093 Epoch 16: Train loss = 0.0066, Val loss = 0.0093 Epoch 17: Train loss = 0.0065, Val loss = 0.0092 Epoch 18: Train loss = 0.0065, Val loss = 0.0092 Epoch 19: Train loss = 0.0065, Val loss = 0.0092 Epoch 20: Train loss = 0.0064, Val loss = 0.0098 Epoch 21: Train loss = 0.0065, Val loss = 0.0096 Epoch 22: Train loss = 0.0064, Val loss = 0.0095 Epoch 23: Train loss = 0.0064, Val loss = 0.0095 Epoch 24: Train loss = 0.0064, Val loss = 0.0097 Epoch 25: Train loss = 0.0064, Val loss = 0.0097 Epoch 26: Train loss = 0.0064, Val loss = 0.0099 Epoch 27: Train loss = 0.0063, Val loss = 0.0098 Epoch 28: Train loss = 0.0063, Val loss = 0.0096 Epoch 29: Train loss = 0.0064, Val loss = 0.0098 Epoch 30: Train loss = 0.0063, Val loss = 0.0101 Epoch 31: Train loss = 0.0063, Val loss = 0.0101 Epoch 32: Train loss = 0.0063, Val loss = 0.0099 Epoch 33: Train loss = 0.0063, Val loss = 0.0099 Epoch 34: Train loss = 0.0063, Val loss = 0.0103 Epoch 35: Train loss = 0.0062, Val loss = 0.0101 Epoch 36: Train loss = 0.0063, Val loss = 0.0101 Epoch 37: Train loss = 0.0062, Val loss = 0.0104 Epoch 38: Train loss = 0.0063, Val loss = 0.0100 Epoch 39: Train loss = 0.0062, Val loss = 0.0105 Epoch 40: Train loss = 0.0062, Val loss = 0.0104 Epoch 41: Train loss = 0.0062, Val loss = 0.0101 Epoch 42: Train loss = 0.0062, Val loss = 0.0104 Epoch 43: Train loss = 0.0062, Val loss = 0.0102 Epoch 44: Train loss = 0.0062, Val loss = 0.0104 Epoch 45: Train loss = 0.0061, Val loss = 0.0105 Epoch 46: Train loss = 0.0062, Val loss = 0.0103 Epoch 47: Train loss = 0.0061, Val loss = 0.0104 Epoch 48: Train loss = 0.0061, Val loss = 0.0104 Epoch 49: Train loss = 0.0061, Val loss = 0.0104 Epoch 50: Train loss = 0.0061, Val loss = 0.0107 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0270, Val loss = 0.0189 Epoch 2: Train loss = 0.0085, Val loss = 0.0096 Epoch 3: Train loss = 0.0073, Val loss = 0.0077 Epoch 4: Train loss = 0.0068, Val loss = 0.0116 Epoch 5: Train loss = 0.0067, Val loss = 0.0090 Epoch 6: Train loss = 0.0065, Val loss = 0.0086 Epoch 7: Train loss = 0.0064, Val loss = 0.0125 Epoch 8: Train loss = 0.0064, Val loss = 0.0095 Epoch 9: Train loss = 0.0063, Val loss = 0.0109 Epoch 10: Train loss = 0.0062, Val loss = 0.0100 Epoch 11: Train loss = 0.0062, Val loss = 0.0083 Epoch 12: Train loss = 0.0062, Val loss = 0.0101 Epoch 13: Train loss = 0.0062, Val loss = 0.0119 Epoch 14: Train loss = 0.0061, Val loss = 0.0095 Epoch 15: Train loss = 0.0061, Val loss = 0.0091 Epoch 16: Train loss = 0.0060, Val loss = 0.0092 Epoch 17: Train loss = 0.0060, Val loss = 0.0109 Epoch 18: Train loss = 0.0060, Val loss = 0.0110 Epoch 19: Train loss = 0.0059, Val loss = 0.0106 Epoch 20: Train loss = 0.0059, Val loss = 0.0106 Epoch 21: Train loss = 0.0058, Val loss = 0.0097 Epoch 22: Train loss = 0.0058, Val loss = 0.0109 Epoch 23: Train loss = 0.0058, Val loss = 0.0099 Epoch 24: Train loss = 0.0057, Val loss = 0.0087 Epoch 25: Train loss = 0.0057, Val loss = 0.0075 Epoch 26: Train loss = 0.0057, Val loss = 0.0079 Epoch 27: Train loss = 0.0057, Val loss = 0.0090 Epoch 28: Train loss = 0.0057, Val loss = 0.0098 Epoch 29: Train loss = 0.0056, Val loss = 0.0073 Epoch 30: Train loss = 0.0055, Val loss = 0.0076 Epoch 31: Train loss = 0.0054, Val loss = 0.0084 Epoch 32: Train loss = 0.0054, Val loss = 0.0090 Epoch 33: Train loss = 0.0055, Val loss = 0.0056 Epoch 34: Train loss = 0.0054, Val loss = 0.0063 Epoch 35: Train loss = 0.0053, Val loss = 0.0084 Epoch 36: Train loss = 0.0053, Val loss = 0.0051 Epoch 37: Train loss = 0.0053, Val loss = 0.0085 Epoch 38: Train loss = 0.0053, Val loss = 0.0051 Epoch 39: Train loss = 0.0052, Val loss = 0.0075 Epoch 40: Train loss = 0.0051, Val loss = 0.0061 Epoch 41: Train loss = 0.0051, Val loss = 0.0071 Epoch 42: Train loss = 0.0052, Val loss = 0.0076 Epoch 43: Train loss = 0.0052, Val loss = 0.0041 Epoch 44: Train loss = 0.0053, Val loss = 0.0071 Epoch 45: Train loss = 0.0050, Val loss = 0.0069 Epoch 46: Train loss = 0.0050, Val loss = 0.0043 Epoch 47: Train loss = 0.0050, Val loss = 0.0073 Epoch 48: Train loss = 0.0050, Val loss = 0.0049 Epoch 49: Train loss = 0.0049, Val loss = 0.0054 Epoch 50: Train loss = 0.0049, Val loss = 0.0079 Epoch 51: Train loss = 0.0049, Val loss = 0.0048 Epoch 52: Train loss = 0.0048, Val loss = 0.0063 Epoch 53: Train loss = 0.0047, Val loss = 0.0055 Early stopping triggered at epoch 53. Using device: mps Epoch 1: Train loss = 0.0523, Val loss = 0.0287 Epoch 2: Train loss = 0.0444, Val loss = 0.0215 Epoch 3: Train loss = 0.0364, Val loss = 0.0147 Epoch 4: Train loss = 0.0272, Val loss = 0.0096 Epoch 5: Train loss = 0.0188, Val loss = 0.0093 Epoch 6: Train loss = 0.0131, Val loss = 0.0140 Epoch 7: Train loss = 0.0105, Val loss = 0.0162 Epoch 8: Train loss = 0.0088, Val loss = 0.0132 Epoch 9: Train loss = 0.0076, Val loss = 0.0104 Epoch 10: Train loss = 0.0073, Val loss = 0.0094 Epoch 11: Train loss = 0.0072, Val loss = 0.0099 Epoch 12: Train loss = 0.0070, Val loss = 0.0107 Epoch 13: Train loss = 0.0068, Val loss = 0.0107 Epoch 14: Train loss = 0.0068, Val loss = 0.0102 Epoch 15: Train loss = 0.0067, Val loss = 0.0099 Epoch 16: Train loss = 0.0066, Val loss = 0.0099 Epoch 17: Train loss = 0.0066, Val loss = 0.0100 Epoch 18: Train loss = 0.0066, Val loss = 0.0102 Epoch 19: Train loss = 0.0065, Val loss = 0.0100 Epoch 20: Train loss = 0.0065, Val loss = 0.0099 Epoch 21: Train loss = 0.0064, Val loss = 0.0101 Epoch 22: Train loss = 0.0065, Val loss = 0.0100 Epoch 23: Train loss = 0.0064, Val loss = 0.0102 Epoch 24: Train loss = 0.0064, Val loss = 0.0102 Epoch 25: Train loss = 0.0064, Val loss = 0.0101 Epoch 26: Train loss = 0.0064, Val loss = 0.0100 Epoch 27: Train loss = 0.0064, Val loss = 0.0101 Epoch 28: Train loss = 0.0064, Val loss = 0.0101 Epoch 29: Train loss = 0.0063, Val loss = 0.0103 Epoch 30: Train loss = 0.0063, Val loss = 0.0102 Epoch 31: Train loss = 0.0063, Val loss = 0.0100 Epoch 32: Train loss = 0.0063, Val loss = 0.0103 Epoch 33: Train loss = 0.0063, Val loss = 0.0101 Epoch 34: Train loss = 0.0063, Val loss = 0.0103 Epoch 35: Train loss = 0.0063, Val loss = 0.0101 Epoch 36: Train loss = 0.0062, Val loss = 0.0102 Epoch 37: Train loss = 0.0062, Val loss = 0.0101 Epoch 38: Train loss = 0.0063, Val loss = 0.0104 Epoch 39: Train loss = 0.0062, Val loss = 0.0100 Epoch 40: Train loss = 0.0062, Val loss = 0.0102 Epoch 41: Train loss = 0.0062, Val loss = 0.0103 Epoch 42: Train loss = 0.0063, Val loss = 0.0103 Epoch 43: Train loss = 0.0062, Val loss = 0.0103 Epoch 44: Train loss = 0.0062, Val loss = 0.0102 Epoch 45: Train loss = 0.0062, Val loss = 0.0099 Epoch 46: Train loss = 0.0062, Val loss = 0.0104 Epoch 47: Train loss = 0.0062, Val loss = 0.0100 Epoch 48: Train loss = 0.0062, Val loss = 0.0100 Epoch 49: Train loss = 0.0062, Val loss = 0.0105 Epoch 50: Train loss = 0.0061, Val loss = 0.0099 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0250, Val loss = 0.0248 Epoch 2: Train loss = 0.0092, Val loss = 0.0068 Epoch 3: Train loss = 0.0076, Val loss = 0.0116 Epoch 4: Train loss = 0.0069, Val loss = 0.0085 Epoch 5: Train loss = 0.0066, Val loss = 0.0099 Epoch 6: Train loss = 0.0065, Val loss = 0.0103 Epoch 7: Train loss = 0.0064, Val loss = 0.0099 Epoch 8: Train loss = 0.0064, Val loss = 0.0103 Epoch 9: Train loss = 0.0064, Val loss = 0.0104 Epoch 10: Train loss = 0.0063, Val loss = 0.0098 Epoch 11: Train loss = 0.0063, Val loss = 0.0101 Epoch 12: Train loss = 0.0062, Val loss = 0.0098 Epoch 13: Train loss = 0.0062, Val loss = 0.0111 Epoch 14: Train loss = 0.0061, Val loss = 0.0113 Epoch 15: Train loss = 0.0061, Val loss = 0.0090 Epoch 16: Train loss = 0.0061, Val loss = 0.0106 Epoch 17: Train loss = 0.0061, Val loss = 0.0113 Epoch 18: Train loss = 0.0060, Val loss = 0.0093 Epoch 19: Train loss = 0.0060, Val loss = 0.0091 Epoch 20: Train loss = 0.0059, Val loss = 0.0105 Epoch 21: Train loss = 0.0059, Val loss = 0.0087 Epoch 22: Train loss = 0.0058, Val loss = 0.0093 Epoch 23: Train loss = 0.0058, Val loss = 0.0085 Epoch 24: Train loss = 0.0058, Val loss = 0.0073 Epoch 25: Train loss = 0.0058, Val loss = 0.0086 Epoch 26: Train loss = 0.0057, Val loss = 0.0084 Epoch 27: Train loss = 0.0057, Val loss = 0.0068 Epoch 28: Train loss = 0.0058, Val loss = 0.0084 Epoch 29: Train loss = 0.0056, Val loss = 0.0091 Epoch 30: Train loss = 0.0056, Val loss = 0.0075 Epoch 31: Train loss = 0.0055, Val loss = 0.0072 Epoch 32: Train loss = 0.0054, Val loss = 0.0077 Epoch 33: Train loss = 0.0054, Val loss = 0.0068 Epoch 34: Train loss = 0.0054, Val loss = 0.0060 Epoch 35: Train loss = 0.0054, Val loss = 0.0093 Epoch 36: Train loss = 0.0054, Val loss = 0.0046 Epoch 37: Train loss = 0.0055, Val loss = 0.0079 Epoch 38: Train loss = 0.0053, Val loss = 0.0063 Epoch 39: Train loss = 0.0052, Val loss = 0.0061 Epoch 40: Train loss = 0.0052, Val loss = 0.0065 Epoch 41: Train loss = 0.0052, Val loss = 0.0065 Epoch 42: Train loss = 0.0051, Val loss = 0.0064 Epoch 43: Train loss = 0.0052, Val loss = 0.0051 Epoch 44: Train loss = 0.0051, Val loss = 0.0086 Epoch 45: Train loss = 0.0053, Val loss = 0.0047 Epoch 46: Train loss = 0.0051, Val loss = 0.0084 Epoch 47: Train loss = 0.0050, Val loss = 0.0051 Epoch 48: Train loss = 0.0049, Val loss = 0.0072 Epoch 49: Train loss = 0.0048, Val loss = 0.0062 Epoch 50: Train loss = 0.0049, Val loss = 0.0070 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0536, Val loss = 0.0334 Epoch 2: Train loss = 0.0511, Val loss = 0.0310 Epoch 3: Train loss = 0.0481, Val loss = 0.0279 Epoch 4: Train loss = 0.0444, Val loss = 0.0236 Epoch 5: Train loss = 0.0388, Val loss = 0.0179 Epoch 6: Train loss = 0.0313, Val loss = 0.0112 Epoch 7: Train loss = 0.0221, Val loss = 0.0064 Epoch 8: Train loss = 0.0135, Val loss = 0.0098 Epoch 9: Train loss = 0.0102, Val loss = 0.0174 Epoch 10: Train loss = 0.0091, Val loss = 0.0136 Epoch 11: Train loss = 0.0075, Val loss = 0.0085 Epoch 12: Train loss = 0.0072, Val loss = 0.0073 Epoch 13: Train loss = 0.0072, Val loss = 0.0085 Epoch 14: Train loss = 0.0070, Val loss = 0.0100 Epoch 15: Train loss = 0.0068, Val loss = 0.0099 Epoch 16: Train loss = 0.0067, Val loss = 0.0088 Epoch 17: Train loss = 0.0067, Val loss = 0.0085 Epoch 18: Train loss = 0.0066, Val loss = 0.0092 Epoch 19: Train loss = 0.0066, Val loss = 0.0094 Epoch 20: Train loss = 0.0066, Val loss = 0.0090 Epoch 21: Train loss = 0.0066, Val loss = 0.0090 Epoch 22: Train loss = 0.0065, Val loss = 0.0092 Epoch 23: Train loss = 0.0064, Val loss = 0.0097 Epoch 24: Train loss = 0.0065, Val loss = 0.0088 Epoch 25: Train loss = 0.0064, Val loss = 0.0091 Epoch 26: Train loss = 0.0065, Val loss = 0.0094 Epoch 27: Train loss = 0.0064, Val loss = 0.0097 Epoch 28: Train loss = 0.0064, Val loss = 0.0093 Epoch 29: Train loss = 0.0064, Val loss = 0.0093 Epoch 30: Train loss = 0.0064, Val loss = 0.0098 Epoch 31: Train loss = 0.0064, Val loss = 0.0096 Epoch 32: Train loss = 0.0064, Val loss = 0.0094 Epoch 33: Train loss = 0.0064, Val loss = 0.0099 Epoch 34: Train loss = 0.0064, Val loss = 0.0095 Epoch 35: Train loss = 0.0063, Val loss = 0.0097 Epoch 36: Train loss = 0.0063, Val loss = 0.0098 Epoch 37: Train loss = 0.0064, Val loss = 0.0094 Epoch 38: Train loss = 0.0063, Val loss = 0.0102 Epoch 39: Train loss = 0.0063, Val loss = 0.0097 Epoch 40: Train loss = 0.0063, Val loss = 0.0097 Epoch 41: Train loss = 0.0063, Val loss = 0.0103 Epoch 42: Train loss = 0.0063, Val loss = 0.0100 Epoch 43: Train loss = 0.0063, Val loss = 0.0097 Epoch 44: Train loss = 0.0063, Val loss = 0.0101 Epoch 45: Train loss = 0.0063, Val loss = 0.0098 Epoch 46: Train loss = 0.0063, Val loss = 0.0101 Epoch 47: Train loss = 0.0063, Val loss = 0.0099 Epoch 48: Train loss = 0.0063, Val loss = 0.0100 Epoch 49: Train loss = 0.0062, Val loss = 0.0102 Epoch 50: Train loss = 0.0062, Val loss = 0.0099 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0414, Val loss = 0.0092 Epoch 2: Train loss = 0.0125, Val loss = 0.0054 Epoch 3: Train loss = 0.0094, Val loss = 0.0100 Epoch 4: Train loss = 0.0079, Val loss = 0.0072 Epoch 5: Train loss = 0.0070, Val loss = 0.0096 Epoch 6: Train loss = 0.0068, Val loss = 0.0081 Epoch 7: Train loss = 0.0066, Val loss = 0.0101 Epoch 8: Train loss = 0.0065, Val loss = 0.0082 Epoch 9: Train loss = 0.0065, Val loss = 0.0105 Epoch 10: Train loss = 0.0064, Val loss = 0.0090 Epoch 11: Train loss = 0.0064, Val loss = 0.0100 Epoch 12: Train loss = 0.0064, Val loss = 0.0106 Epoch 13: Train loss = 0.0064, Val loss = 0.0102 Epoch 14: Train loss = 0.0063, Val loss = 0.0095 Epoch 15: Train loss = 0.0064, Val loss = 0.0098 Epoch 16: Train loss = 0.0063, Val loss = 0.0109 Epoch 17: Train loss = 0.0063, Val loss = 0.0090 Epoch 18: Train loss = 0.0063, Val loss = 0.0092 Epoch 19: Train loss = 0.0062, Val loss = 0.0116 Epoch 20: Train loss = 0.0062, Val loss = 0.0102 Epoch 21: Train loss = 0.0062, Val loss = 0.0079 Epoch 22: Train loss = 0.0062, Val loss = 0.0095 Epoch 23: Train loss = 0.0063, Val loss = 0.0124 Epoch 24: Train loss = 0.0062, Val loss = 0.0107 Epoch 25: Train loss = 0.0061, Val loss = 0.0086 Epoch 26: Train loss = 0.0061, Val loss = 0.0094 Epoch 27: Train loss = 0.0061, Val loss = 0.0115 Epoch 28: Train loss = 0.0060, Val loss = 0.0109 Epoch 29: Train loss = 0.0061, Val loss = 0.0074 Epoch 30: Train loss = 0.0060, Val loss = 0.0088 Epoch 31: Train loss = 0.0060, Val loss = 0.0101 Epoch 32: Train loss = 0.0060, Val loss = 0.0106 Epoch 33: Train loss = 0.0059, Val loss = 0.0105 Epoch 34: Train loss = 0.0059, Val loss = 0.0087 Epoch 35: Train loss = 0.0058, Val loss = 0.0098 Epoch 36: Train loss = 0.0058, Val loss = 0.0082 Epoch 37: Train loss = 0.0058, Val loss = 0.0101 Epoch 38: Train loss = 0.0058, Val loss = 0.0091 Epoch 39: Train loss = 0.0057, Val loss = 0.0089 Epoch 40: Train loss = 0.0056, Val loss = 0.0079 Epoch 41: Train loss = 0.0056, Val loss = 0.0074 Epoch 42: Train loss = 0.0056, Val loss = 0.0090 Epoch 43: Train loss = 0.0055, Val loss = 0.0063 Epoch 44: Train loss = 0.0055, Val loss = 0.0073 Epoch 45: Train loss = 0.0054, Val loss = 0.0058 Epoch 46: Train loss = 0.0056, Val loss = 0.0065 Epoch 47: Train loss = 0.0054, Val loss = 0.0063 Epoch 48: Train loss = 0.0053, Val loss = 0.0056 Epoch 49: Train loss = 0.0052, Val loss = 0.0058 Epoch 50: Train loss = 0.0052, Val loss = 0.0057 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0520, Val loss = 0.0323 Epoch 2: Train loss = 0.0495, Val loss = 0.0298 Epoch 3: Train loss = 0.0465, Val loss = 0.0268 Epoch 4: Train loss = 0.0425, Val loss = 0.0231 Epoch 5: Train loss = 0.0377, Val loss = 0.0181 Epoch 6: Train loss = 0.0309, Val loss = 0.0123 Epoch 7: Train loss = 0.0225, Val loss = 0.0085 Epoch 8: Train loss = 0.0150, Val loss = 0.0108 Epoch 9: Train loss = 0.0112, Val loss = 0.0147 Epoch 10: Train loss = 0.0091, Val loss = 0.0116 Epoch 11: Train loss = 0.0077, Val loss = 0.0082 Epoch 12: Train loss = 0.0072, Val loss = 0.0078 Epoch 13: Train loss = 0.0071, Val loss = 0.0089 Epoch 14: Train loss = 0.0070, Val loss = 0.0096 Epoch 15: Train loss = 0.0068, Val loss = 0.0090 Epoch 16: Train loss = 0.0068, Val loss = 0.0084 Epoch 17: Train loss = 0.0067, Val loss = 0.0088 Epoch 18: Train loss = 0.0067, Val loss = 0.0091 Epoch 19: Train loss = 0.0066, Val loss = 0.0091 Epoch 20: Train loss = 0.0066, Val loss = 0.0088 Epoch 21: Train loss = 0.0065, Val loss = 0.0089 Epoch 22: Train loss = 0.0066, Val loss = 0.0091 Epoch 23: Train loss = 0.0065, Val loss = 0.0091 Epoch 24: Train loss = 0.0065, Val loss = 0.0092 Epoch 25: Train loss = 0.0065, Val loss = 0.0092 Epoch 26: Train loss = 0.0064, Val loss = 0.0096 Epoch 27: Train loss = 0.0065, Val loss = 0.0093 Epoch 28: Train loss = 0.0064, Val loss = 0.0095 Epoch 29: Train loss = 0.0064, Val loss = 0.0095 Epoch 30: Train loss = 0.0064, Val loss = 0.0094 Epoch 31: Train loss = 0.0064, Val loss = 0.0097 Epoch 32: Train loss = 0.0064, Val loss = 0.0098 Epoch 33: Train loss = 0.0064, Val loss = 0.0095 Epoch 34: Train loss = 0.0064, Val loss = 0.0098 Epoch 35: Train loss = 0.0064, Val loss = 0.0100 Epoch 36: Train loss = 0.0064, Val loss = 0.0099 Epoch 37: Train loss = 0.0063, Val loss = 0.0100 Epoch 38: Train loss = 0.0064, Val loss = 0.0099 Epoch 39: Train loss = 0.0064, Val loss = 0.0102 Epoch 40: Train loss = 0.0064, Val loss = 0.0100 Epoch 41: Train loss = 0.0064, Val loss = 0.0100 Epoch 42: Train loss = 0.0063, Val loss = 0.0104 Epoch 43: Train loss = 0.0063, Val loss = 0.0101 Epoch 44: Train loss = 0.0063, Val loss = 0.0099 Epoch 45: Train loss = 0.0064, Val loss = 0.0105 Epoch 46: Train loss = 0.0063, Val loss = 0.0105 Epoch 47: Train loss = 0.0063, Val loss = 0.0100 Epoch 48: Train loss = 0.0063, Val loss = 0.0109 Epoch 49: Train loss = 0.0063, Val loss = 0.0105 Epoch 50: Train loss = 0.0062, Val loss = 0.0103 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0342, Val loss = 0.0093 Epoch 2: Train loss = 0.0113, Val loss = 0.0045 Epoch 3: Train loss = 0.0086, Val loss = 0.0112 Epoch 4: Train loss = 0.0075, Val loss = 0.0064 Epoch 5: Train loss = 0.0068, Val loss = 0.0102 Epoch 6: Train loss = 0.0066, Val loss = 0.0079 Epoch 7: Train loss = 0.0065, Val loss = 0.0097 Epoch 8: Train loss = 0.0064, Val loss = 0.0091 Epoch 9: Train loss = 0.0065, Val loss = 0.0106 Epoch 10: Train loss = 0.0064, Val loss = 0.0101 Epoch 11: Train loss = 0.0064, Val loss = 0.0095 Epoch 12: Train loss = 0.0064, Val loss = 0.0105 Epoch 13: Train loss = 0.0063, Val loss = 0.0106 Epoch 14: Train loss = 0.0064, Val loss = 0.0126 Epoch 15: Train loss = 0.0063, Val loss = 0.0073 Epoch 16: Train loss = 0.0064, Val loss = 0.0123 Epoch 17: Train loss = 0.0063, Val loss = 0.0110 Epoch 18: Train loss = 0.0063, Val loss = 0.0103 Epoch 19: Train loss = 0.0063, Val loss = 0.0076 Epoch 20: Train loss = 0.0063, Val loss = 0.0099 Epoch 21: Train loss = 0.0062, Val loss = 0.0114 Epoch 22: Train loss = 0.0061, Val loss = 0.0106 Epoch 23: Train loss = 0.0061, Val loss = 0.0100 Epoch 24: Train loss = 0.0061, Val loss = 0.0101 Epoch 25: Train loss = 0.0061, Val loss = 0.0097 Epoch 26: Train loss = 0.0060, Val loss = 0.0109 Epoch 27: Train loss = 0.0060, Val loss = 0.0116 Epoch 28: Train loss = 0.0060, Val loss = 0.0109 Epoch 29: Train loss = 0.0060, Val loss = 0.0105 Epoch 30: Train loss = 0.0060, Val loss = 0.0107 Epoch 31: Train loss = 0.0061, Val loss = 0.0101 Epoch 32: Train loss = 0.0059, Val loss = 0.0097 Epoch 33: Train loss = 0.0058, Val loss = 0.0090 Epoch 34: Train loss = 0.0058, Val loss = 0.0079 Epoch 35: Train loss = 0.0058, Val loss = 0.0082 Epoch 36: Train loss = 0.0058, Val loss = 0.0093 Epoch 37: Train loss = 0.0058, Val loss = 0.0079 Epoch 38: Train loss = 0.0057, Val loss = 0.0091 Epoch 39: Train loss = 0.0057, Val loss = 0.0087 Epoch 40: Train loss = 0.0057, Val loss = 0.0056 Epoch 41: Train loss = 0.0058, Val loss = 0.0044 Epoch 42: Train loss = 0.0058, Val loss = 0.0049 Epoch 43: Train loss = 0.0057, Val loss = 0.0053 Epoch 44: Train loss = 0.0057, Val loss = 0.0072 Epoch 45: Train loss = 0.0056, Val loss = 0.0079 Epoch 46: Train loss = 0.0055, Val loss = 0.0086 Epoch 47: Train loss = 0.0056, Val loss = 0.0088 Epoch 48: Train loss = 0.0054, Val loss = 0.0049 Epoch 49: Train loss = 0.0055, Val loss = 0.0052 Epoch 50: Train loss = 0.0053, Val loss = 0.0053 Epoch 51: Train loss = 0.0054, Val loss = 0.0061 Early stopping triggered at epoch 51. Using device: mps Epoch 1: Train loss = 0.0592, Val loss = 0.0387 Epoch 2: Train loss = 0.0561, Val loss = 0.0364 Epoch 3: Train loss = 0.0543, Val loss = 0.0348 Epoch 4: Train loss = 0.0526, Val loss = 0.0331 Epoch 5: Train loss = 0.0508, Val loss = 0.0312 Epoch 6: Train loss = 0.0484, Val loss = 0.0289 Epoch 7: Train loss = 0.0460, Val loss = 0.0265 Epoch 8: Train loss = 0.0434, Val loss = 0.0242 Epoch 9: Train loss = 0.0407, Val loss = 0.0220 Epoch 10: Train loss = 0.0381, Val loss = 0.0199 Epoch 11: Train loss = 0.0357, Val loss = 0.0181 Epoch 12: Train loss = 0.0333, Val loss = 0.0165 Epoch 13: Train loss = 0.0314, Val loss = 0.0151 Epoch 14: Train loss = 0.0294, Val loss = 0.0139 Epoch 15: Train loss = 0.0278, Val loss = 0.0127 Epoch 16: Train loss = 0.0262, Val loss = 0.0118 Epoch 17: Train loss = 0.0248, Val loss = 0.0108 Epoch 18: Train loss = 0.0235, Val loss = 0.0099 Epoch 19: Train loss = 0.0224, Val loss = 0.0092 Epoch 20: Train loss = 0.0214, Val loss = 0.0087 Epoch 21: Train loss = 0.0202, Val loss = 0.0082 Epoch 22: Train loss = 0.0199, Val loss = 0.0077 Epoch 23: Train loss = 0.0189, Val loss = 0.0073 Epoch 24: Train loss = 0.0181, Val loss = 0.0069 Epoch 25: Train loss = 0.0175, Val loss = 0.0065 Epoch 26: Train loss = 0.0169, Val loss = 0.0062 Epoch 27: Train loss = 0.0164, Val loss = 0.0061 Epoch 28: Train loss = 0.0159, Val loss = 0.0059 Epoch 29: Train loss = 0.0153, Val loss = 0.0058 Epoch 30: Train loss = 0.0150, Val loss = 0.0057 Epoch 31: Train loss = 0.0147, Val loss = 0.0056 Epoch 32: Train loss = 0.0143, Val loss = 0.0056 Epoch 33: Train loss = 0.0142, Val loss = 0.0055 Epoch 34: Train loss = 0.0136, Val loss = 0.0055 Epoch 35: Train loss = 0.0134, Val loss = 0.0055 Epoch 36: Train loss = 0.0130, Val loss = 0.0057 Epoch 37: Train loss = 0.0131, Val loss = 0.0059 Epoch 38: Train loss = 0.0129, Val loss = 0.0059 Epoch 39: Train loss = 0.0125, Val loss = 0.0059 Epoch 40: Train loss = 0.0122, Val loss = 0.0059 Epoch 41: Train loss = 0.0124, Val loss = 0.0059 Epoch 42: Train loss = 0.0123, Val loss = 0.0059 Epoch 43: Train loss = 0.0123, Val loss = 0.0060 Epoch 44: Train loss = 0.0118, Val loss = 0.0058 Epoch 45: Train loss = 0.0116, Val loss = 0.0057 Epoch 46: Train loss = 0.0115, Val loss = 0.0060 Epoch 47: Train loss = 0.0116, Val loss = 0.0060 Epoch 48: Train loss = 0.0113, Val loss = 0.0060 Epoch 49: Train loss = 0.0112, Val loss = 0.0060 Epoch 50: Train loss = 0.0114, Val loss = 0.0059 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0314, Val loss = 0.0147 Epoch 2: Train loss = 0.0234, Val loss = 0.0103 Epoch 3: Train loss = 0.0174, Val loss = 0.0076 Epoch 4: Train loss = 0.0138, Val loss = 0.0065 Epoch 5: Train loss = 0.0113, Val loss = 0.0056 Epoch 6: Train loss = 0.0100, Val loss = 0.0056 Epoch 7: Train loss = 0.0095, Val loss = 0.0057 Epoch 8: Train loss = 0.0090, Val loss = 0.0061 Epoch 9: Train loss = 0.0085, Val loss = 0.0053 Epoch 10: Train loss = 0.0084, Val loss = 0.0073 Epoch 11: Train loss = 0.0080, Val loss = 0.0063 Epoch 12: Train loss = 0.0080, Val loss = 0.0065 Epoch 13: Train loss = 0.0077, Val loss = 0.0072 Epoch 14: Train loss = 0.0077, Val loss = 0.0059 Epoch 15: Train loss = 0.0077, Val loss = 0.0059 Epoch 16: Train loss = 0.0076, Val loss = 0.0067 Epoch 17: Train loss = 0.0076, Val loss = 0.0088 Epoch 18: Train loss = 0.0074, Val loss = 0.0078 Epoch 19: Train loss = 0.0073, Val loss = 0.0059 Epoch 20: Train loss = 0.0072, Val loss = 0.0072 Epoch 21: Train loss = 0.0071, Val loss = 0.0070 Epoch 22: Train loss = 0.0071, Val loss = 0.0072 Epoch 23: Train loss = 0.0070, Val loss = 0.0071 Epoch 24: Train loss = 0.0069, Val loss = 0.0064 Epoch 25: Train loss = 0.0069, Val loss = 0.0072 Epoch 26: Train loss = 0.0068, Val loss = 0.0073 Epoch 27: Train loss = 0.0067, Val loss = 0.0064 Epoch 28: Train loss = 0.0067, Val loss = 0.0076 Epoch 29: Train loss = 0.0067, Val loss = 0.0077 Epoch 30: Train loss = 0.0065, Val loss = 0.0049 Epoch 31: Train loss = 0.0066, Val loss = 0.0052 Epoch 32: Train loss = 0.0064, Val loss = 0.0058 Epoch 33: Train loss = 0.0064, Val loss = 0.0061 Epoch 34: Train loss = 0.0064, Val loss = 0.0051 Epoch 35: Train loss = 0.0063, Val loss = 0.0068 Epoch 36: Train loss = 0.0065, Val loss = 0.0050 Epoch 37: Train loss = 0.0063, Val loss = 0.0050 Epoch 38: Train loss = 0.0062, Val loss = 0.0047 Epoch 39: Train loss = 0.0062, Val loss = 0.0061 Epoch 40: Train loss = 0.0062, Val loss = 0.0060 Epoch 41: Train loss = 0.0061, Val loss = 0.0083 Epoch 42: Train loss = 0.0063, Val loss = 0.0056 Epoch 43: Train loss = 0.0060, Val loss = 0.0049 Epoch 44: Train loss = 0.0062, Val loss = 0.0049 Epoch 45: Train loss = 0.0060, Val loss = 0.0063 Epoch 46: Train loss = 0.0060, Val loss = 0.0063 Epoch 47: Train loss = 0.0060, Val loss = 0.0072 Epoch 48: Train loss = 0.0059, Val loss = 0.0052 Epoch 49: Train loss = 0.0060, Val loss = 0.0048 Epoch 50: Train loss = 0.0060, Val loss = 0.0044 Epoch 51: Train loss = 0.0060, Val loss = 0.0045 Epoch 52: Train loss = 0.0059, Val loss = 0.0051 Epoch 53: Train loss = 0.0059, Val loss = 0.0054 Epoch 54: Train loss = 0.0058, Val loss = 0.0055 Epoch 55: Train loss = 0.0057, Val loss = 0.0055 Epoch 56: Train loss = 0.0059, Val loss = 0.0047 Epoch 57: Train loss = 0.0058, Val loss = 0.0062 Epoch 58: Train loss = 0.0058, Val loss = 0.0057 Epoch 59: Train loss = 0.0058, Val loss = 0.0059 Epoch 60: Train loss = 0.0057, Val loss = 0.0050 Early stopping triggered at epoch 60. Using device: mps Epoch 1: Train loss = 0.0572, Val loss = 0.0368 Epoch 2: Train loss = 0.0545, Val loss = 0.0343 Epoch 3: Train loss = 0.0524, Val loss = 0.0325 Epoch 4: Train loss = 0.0501, Val loss = 0.0309 Epoch 5: Train loss = 0.0480, Val loss = 0.0289 Epoch 6: Train loss = 0.0460, Val loss = 0.0268 Epoch 7: Train loss = 0.0435, Val loss = 0.0249 Epoch 8: Train loss = 0.0412, Val loss = 0.0233 Epoch 9: Train loss = 0.0394, Val loss = 0.0219 Epoch 10: Train loss = 0.0373, Val loss = 0.0206 Epoch 11: Train loss = 0.0355, Val loss = 0.0192 Epoch 12: Train loss = 0.0337, Val loss = 0.0178 Epoch 13: Train loss = 0.0316, Val loss = 0.0166 Epoch 14: Train loss = 0.0301, Val loss = 0.0154 Epoch 15: Train loss = 0.0283, Val loss = 0.0142 Epoch 16: Train loss = 0.0265, Val loss = 0.0132 Epoch 17: Train loss = 0.0250, Val loss = 0.0122 Epoch 18: Train loss = 0.0235, Val loss = 0.0114 Epoch 19: Train loss = 0.0222, Val loss = 0.0107 Epoch 20: Train loss = 0.0211, Val loss = 0.0101 Epoch 21: Train loss = 0.0200, Val loss = 0.0094 Epoch 22: Train loss = 0.0186, Val loss = 0.0087 Epoch 23: Train loss = 0.0181, Val loss = 0.0081 Epoch 24: Train loss = 0.0171, Val loss = 0.0077 Epoch 25: Train loss = 0.0166, Val loss = 0.0074 Epoch 26: Train loss = 0.0162, Val loss = 0.0071 Epoch 27: Train loss = 0.0154, Val loss = 0.0070 Epoch 28: Train loss = 0.0149, Val loss = 0.0067 Epoch 29: Train loss = 0.0147, Val loss = 0.0068 Epoch 30: Train loss = 0.0144, Val loss = 0.0067 Epoch 31: Train loss = 0.0142, Val loss = 0.0065 Epoch 32: Train loss = 0.0135, Val loss = 0.0061 Epoch 33: Train loss = 0.0133, Val loss = 0.0062 Epoch 34: Train loss = 0.0133, Val loss = 0.0065 Epoch 35: Train loss = 0.0129, Val loss = 0.0065 Epoch 36: Train loss = 0.0126, Val loss = 0.0065 Epoch 37: Train loss = 0.0124, Val loss = 0.0062 Epoch 38: Train loss = 0.0124, Val loss = 0.0061 Epoch 39: Train loss = 0.0121, Val loss = 0.0064 Epoch 40: Train loss = 0.0120, Val loss = 0.0063 Epoch 41: Train loss = 0.0119, Val loss = 0.0064 Epoch 42: Train loss = 0.0117, Val loss = 0.0064 Epoch 43: Train loss = 0.0116, Val loss = 0.0063 Epoch 44: Train loss = 0.0115, Val loss = 0.0066 Epoch 45: Train loss = 0.0115, Val loss = 0.0065 Epoch 46: Train loss = 0.0114, Val loss = 0.0063 Epoch 47: Train loss = 0.0111, Val loss = 0.0065 Epoch 48: Train loss = 0.0112, Val loss = 0.0064 Epoch 49: Train loss = 0.0110, Val loss = 0.0063 Epoch 50: Train loss = 0.0109, Val loss = 0.0066 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0455, Val loss = 0.0170 Epoch 2: Train loss = 0.0284, Val loss = 0.0116 Epoch 3: Train loss = 0.0197, Val loss = 0.0066 Epoch 4: Train loss = 0.0149, Val loss = 0.0055 Epoch 5: Train loss = 0.0121, Val loss = 0.0057 Epoch 6: Train loss = 0.0108, Val loss = 0.0052 Epoch 7: Train loss = 0.0102, Val loss = 0.0063 Epoch 8: Train loss = 0.0097, Val loss = 0.0057 Epoch 9: Train loss = 0.0094, Val loss = 0.0069 Epoch 10: Train loss = 0.0090, Val loss = 0.0065 Epoch 11: Train loss = 0.0087, Val loss = 0.0078 Epoch 12: Train loss = 0.0084, Val loss = 0.0072 Epoch 13: Train loss = 0.0085, Val loss = 0.0070 Epoch 14: Train loss = 0.0083, Val loss = 0.0073 Epoch 15: Train loss = 0.0081, Val loss = 0.0070 Epoch 16: Train loss = 0.0079, Val loss = 0.0077 Epoch 17: Train loss = 0.0079, Val loss = 0.0096 Epoch 18: Train loss = 0.0077, Val loss = 0.0062 Epoch 19: Train loss = 0.0077, Val loss = 0.0054 Epoch 20: Train loss = 0.0077, Val loss = 0.0051 Epoch 21: Train loss = 0.0074, Val loss = 0.0047 Epoch 22: Train loss = 0.0074, Val loss = 0.0062 Epoch 23: Train loss = 0.0075, Val loss = 0.0063 Epoch 24: Train loss = 0.0073, Val loss = 0.0071 Epoch 25: Train loss = 0.0073, Val loss = 0.0079 Epoch 26: Train loss = 0.0072, Val loss = 0.0083 Epoch 27: Train loss = 0.0071, Val loss = 0.0088 Epoch 28: Train loss = 0.0071, Val loss = 0.0092 Epoch 29: Train loss = 0.0071, Val loss = 0.0082 Epoch 30: Train loss = 0.0069, Val loss = 0.0068 Epoch 31: Train loss = 0.0069, Val loss = 0.0057 Epoch 32: Train loss = 0.0069, Val loss = 0.0059 Epoch 33: Train loss = 0.0068, Val loss = 0.0065 Epoch 34: Train loss = 0.0069, Val loss = 0.0064 Epoch 35: Train loss = 0.0069, Val loss = 0.0064 Epoch 36: Train loss = 0.0069, Val loss = 0.0069 Epoch 37: Train loss = 0.0068, Val loss = 0.0071 Epoch 38: Train loss = 0.0066, Val loss = 0.0069 Epoch 39: Train loss = 0.0066, Val loss = 0.0057 Epoch 40: Train loss = 0.0066, Val loss = 0.0053 Epoch 41: Train loss = 0.0067, Val loss = 0.0062 Epoch 42: Train loss = 0.0065, Val loss = 0.0070 Epoch 43: Train loss = 0.0063, Val loss = 0.0062 Epoch 44: Train loss = 0.0065, Val loss = 0.0062 Epoch 45: Train loss = 0.0064, Val loss = 0.0060 Epoch 46: Train loss = 0.0063, Val loss = 0.0058 Epoch 47: Train loss = 0.0064, Val loss = 0.0054 Epoch 48: Train loss = 0.0063, Val loss = 0.0067 Epoch 49: Train loss = 0.0063, Val loss = 0.0066 Epoch 50: Train loss = 0.0062, Val loss = 0.0058 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0592, Val loss = 0.0389 Epoch 2: Train loss = 0.0578, Val loss = 0.0378 Epoch 3: Train loss = 0.0566, Val loss = 0.0369 Epoch 4: Train loss = 0.0556, Val loss = 0.0361 Epoch 5: Train loss = 0.0543, Val loss = 0.0352 Epoch 6: Train loss = 0.0533, Val loss = 0.0344 Epoch 7: Train loss = 0.0523, Val loss = 0.0335 Epoch 8: Train loss = 0.0515, Val loss = 0.0326 Epoch 9: Train loss = 0.0505, Val loss = 0.0316 Epoch 10: Train loss = 0.0492, Val loss = 0.0306 Epoch 11: Train loss = 0.0482, Val loss = 0.0294 Epoch 12: Train loss = 0.0466, Val loss = 0.0280 Epoch 13: Train loss = 0.0451, Val loss = 0.0264 Epoch 14: Train loss = 0.0429, Val loss = 0.0248 Epoch 15: Train loss = 0.0412, Val loss = 0.0232 Epoch 16: Train loss = 0.0390, Val loss = 0.0216 Epoch 17: Train loss = 0.0368, Val loss = 0.0201 Epoch 18: Train loss = 0.0346, Val loss = 0.0186 Epoch 19: Train loss = 0.0323, Val loss = 0.0172 Epoch 20: Train loss = 0.0304, Val loss = 0.0158 Epoch 21: Train loss = 0.0284, Val loss = 0.0145 Epoch 22: Train loss = 0.0265, Val loss = 0.0133 Epoch 23: Train loss = 0.0251, Val loss = 0.0121 Epoch 24: Train loss = 0.0236, Val loss = 0.0110 Epoch 25: Train loss = 0.0221, Val loss = 0.0100 Epoch 26: Train loss = 0.0209, Val loss = 0.0091 Epoch 27: Train loss = 0.0196, Val loss = 0.0083 Epoch 28: Train loss = 0.0187, Val loss = 0.0076 Epoch 29: Train loss = 0.0181, Val loss = 0.0070 Epoch 30: Train loss = 0.0168, Val loss = 0.0066 Epoch 31: Train loss = 0.0164, Val loss = 0.0062 Epoch 32: Train loss = 0.0159, Val loss = 0.0060 Epoch 33: Train loss = 0.0155, Val loss = 0.0057 Epoch 34: Train loss = 0.0148, Val loss = 0.0055 Epoch 35: Train loss = 0.0145, Val loss = 0.0053 Epoch 36: Train loss = 0.0141, Val loss = 0.0052 Epoch 37: Train loss = 0.0137, Val loss = 0.0051 Epoch 38: Train loss = 0.0133, Val loss = 0.0052 Epoch 39: Train loss = 0.0132, Val loss = 0.0052 Epoch 40: Train loss = 0.0129, Val loss = 0.0051 Epoch 41: Train loss = 0.0129, Val loss = 0.0051 Epoch 42: Train loss = 0.0128, Val loss = 0.0051 Epoch 43: Train loss = 0.0124, Val loss = 0.0049 Epoch 44: Train loss = 0.0124, Val loss = 0.0048 Epoch 45: Train loss = 0.0123, Val loss = 0.0049 Epoch 46: Train loss = 0.0121, Val loss = 0.0049 Epoch 47: Train loss = 0.0118, Val loss = 0.0050 Epoch 48: Train loss = 0.0119, Val loss = 0.0051 Epoch 49: Train loss = 0.0116, Val loss = 0.0051 Epoch 50: Train loss = 0.0116, Val loss = 0.0051 Epoch 51: Train loss = 0.0115, Val loss = 0.0052 Epoch 52: Train loss = 0.0114, Val loss = 0.0050 Epoch 53: Train loss = 0.0113, Val loss = 0.0052 Early stopping triggered at epoch 53. Using device: mps Epoch 1: Train loss = 0.0427, Val loss = 0.0226 Epoch 2: Train loss = 0.0333, Val loss = 0.0149 Epoch 3: Train loss = 0.0234, Val loss = 0.0115 Epoch 4: Train loss = 0.0167, Val loss = 0.0062 Epoch 5: Train loss = 0.0128, Val loss = 0.0064 Epoch 6: Train loss = 0.0113, Val loss = 0.0047 Epoch 7: Train loss = 0.0105, Val loss = 0.0054 Epoch 8: Train loss = 0.0099, Val loss = 0.0049 Epoch 9: Train loss = 0.0096, Val loss = 0.0059 Epoch 10: Train loss = 0.0094, Val loss = 0.0049 Epoch 11: Train loss = 0.0090, Val loss = 0.0066 Epoch 12: Train loss = 0.0089, Val loss = 0.0052 Epoch 13: Train loss = 0.0086, Val loss = 0.0059 Epoch 14: Train loss = 0.0084, Val loss = 0.0054 Epoch 15: Train loss = 0.0084, Val loss = 0.0064 Epoch 16: Train loss = 0.0081, Val loss = 0.0061 Epoch 17: Train loss = 0.0080, Val loss = 0.0048 Epoch 18: Train loss = 0.0079, Val loss = 0.0051 Epoch 19: Train loss = 0.0077, Val loss = 0.0065 Epoch 20: Train loss = 0.0077, Val loss = 0.0058 Epoch 21: Train loss = 0.0076, Val loss = 0.0050 Epoch 22: Train loss = 0.0073, Val loss = 0.0070 Epoch 23: Train loss = 0.0073, Val loss = 0.0071 Epoch 24: Train loss = 0.0073, Val loss = 0.0061 Epoch 25: Train loss = 0.0071, Val loss = 0.0057 Epoch 26: Train loss = 0.0071, Val loss = 0.0061 Epoch 27: Train loss = 0.0070, Val loss = 0.0060 Epoch 28: Train loss = 0.0070, Val loss = 0.0052 Epoch 29: Train loss = 0.0069, Val loss = 0.0053 Epoch 30: Train loss = 0.0069, Val loss = 0.0070 Epoch 31: Train loss = 0.0070, Val loss = 0.0075 Epoch 32: Train loss = 0.0068, Val loss = 0.0072 Epoch 33: Train loss = 0.0067, Val loss = 0.0067 Epoch 34: Train loss = 0.0067, Val loss = 0.0062 Epoch 35: Train loss = 0.0066, Val loss = 0.0051 Epoch 36: Train loss = 0.0065, Val loss = 0.0061 Epoch 37: Train loss = 0.0066, Val loss = 0.0067 Epoch 38: Train loss = 0.0064, Val loss = 0.0055 Epoch 39: Train loss = 0.0064, Val loss = 0.0057 Epoch 40: Train loss = 0.0064, Val loss = 0.0070 Epoch 41: Train loss = 0.0062, Val loss = 0.0048 Epoch 42: Train loss = 0.0062, Val loss = 0.0050 Epoch 43: Train loss = 0.0062, Val loss = 0.0049 Epoch 44: Train loss = 0.0061, Val loss = 0.0041 Epoch 45: Train loss = 0.0061, Val loss = 0.0044 Epoch 46: Train loss = 0.0061, Val loss = 0.0050 Epoch 47: Train loss = 0.0061, Val loss = 0.0051 Epoch 48: Train loss = 0.0060, Val loss = 0.0052 Epoch 49: Train loss = 0.0061, Val loss = 0.0051 Epoch 50: Train loss = 0.0062, Val loss = 0.0057 Epoch 51: Train loss = 0.0059, Val loss = 0.0050 Epoch 52: Train loss = 0.0058, Val loss = 0.0049 Epoch 53: Train loss = 0.0059, Val loss = 0.0050 Epoch 54: Train loss = 0.0058, Val loss = 0.0056 Early stopping triggered at epoch 54. Using device: mps Epoch 1: Train loss = 0.0476, Val loss = 0.0301 Epoch 2: Train loss = 0.0467, Val loss = 0.0292 Epoch 3: Train loss = 0.0459, Val loss = 0.0284 Epoch 4: Train loss = 0.0449, Val loss = 0.0276 Epoch 5: Train loss = 0.0440, Val loss = 0.0267 Epoch 6: Train loss = 0.0429, Val loss = 0.0258 Epoch 7: Train loss = 0.0419, Val loss = 0.0248 Epoch 8: Train loss = 0.0406, Val loss = 0.0237 Epoch 9: Train loss = 0.0392, Val loss = 0.0227 Epoch 10: Train loss = 0.0379, Val loss = 0.0217 Epoch 11: Train loss = 0.0361, Val loss = 0.0207 Epoch 12: Train loss = 0.0348, Val loss = 0.0196 Epoch 13: Train loss = 0.0333, Val loss = 0.0185 Epoch 14: Train loss = 0.0316, Val loss = 0.0173 Epoch 15: Train loss = 0.0301, Val loss = 0.0161 Epoch 16: Train loss = 0.0283, Val loss = 0.0149 Epoch 17: Train loss = 0.0268, Val loss = 0.0137 Epoch 18: Train loss = 0.0255, Val loss = 0.0126 Epoch 19: Train loss = 0.0240, Val loss = 0.0117 Epoch 20: Train loss = 0.0227, Val loss = 0.0110 Epoch 21: Train loss = 0.0215, Val loss = 0.0102 Epoch 22: Train loss = 0.0204, Val loss = 0.0095 Epoch 23: Train loss = 0.0192, Val loss = 0.0088 Epoch 24: Train loss = 0.0185, Val loss = 0.0082 Epoch 25: Train loss = 0.0174, Val loss = 0.0076 Epoch 26: Train loss = 0.0169, Val loss = 0.0072 Epoch 27: Train loss = 0.0163, Val loss = 0.0068 Epoch 28: Train loss = 0.0154, Val loss = 0.0065 Epoch 29: Train loss = 0.0150, Val loss = 0.0063 Epoch 30: Train loss = 0.0146, Val loss = 0.0061 Epoch 31: Train loss = 0.0141, Val loss = 0.0059 Epoch 32: Train loss = 0.0136, Val loss = 0.0058 Epoch 33: Train loss = 0.0131, Val loss = 0.0057 Epoch 34: Train loss = 0.0131, Val loss = 0.0055 Epoch 35: Train loss = 0.0128, Val loss = 0.0055 Epoch 36: Train loss = 0.0125, Val loss = 0.0055 Epoch 37: Train loss = 0.0124, Val loss = 0.0055 Epoch 38: Train loss = 0.0119, Val loss = 0.0055 Epoch 39: Train loss = 0.0116, Val loss = 0.0055 Epoch 40: Train loss = 0.0116, Val loss = 0.0055 Epoch 41: Train loss = 0.0115, Val loss = 0.0055 Epoch 42: Train loss = 0.0113, Val loss = 0.0055 Epoch 43: Train loss = 0.0112, Val loss = 0.0055 Epoch 44: Train loss = 0.0111, Val loss = 0.0057 Epoch 45: Train loss = 0.0111, Val loss = 0.0056 Epoch 46: Train loss = 0.0110, Val loss = 0.0056 Epoch 47: Train loss = 0.0108, Val loss = 0.0056 Epoch 48: Train loss = 0.0107, Val loss = 0.0057 Epoch 49: Train loss = 0.0107, Val loss = 0.0056 Epoch 50: Train loss = 0.0108, Val loss = 0.0059 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0517, Val loss = 0.0300 Epoch 2: Train loss = 0.0442, Val loss = 0.0222 Epoch 3: Train loss = 0.0343, Val loss = 0.0150 Epoch 4: Train loss = 0.0247, Val loss = 0.0123 Epoch 5: Train loss = 0.0183, Val loss = 0.0074 Epoch 6: Train loss = 0.0138, Val loss = 0.0066 Epoch 7: Train loss = 0.0116, Val loss = 0.0048 Epoch 8: Train loss = 0.0104, Val loss = 0.0050 Epoch 9: Train loss = 0.0097, Val loss = 0.0047 Epoch 10: Train loss = 0.0093, Val loss = 0.0051 Epoch 11: Train loss = 0.0091, Val loss = 0.0047 Epoch 12: Train loss = 0.0087, Val loss = 0.0059 Epoch 13: Train loss = 0.0086, Val loss = 0.0047 Epoch 14: Train loss = 0.0084, Val loss = 0.0051 Epoch 15: Train loss = 0.0084, Val loss = 0.0055 Epoch 16: Train loss = 0.0082, Val loss = 0.0048 Epoch 17: Train loss = 0.0082, Val loss = 0.0056 Epoch 18: Train loss = 0.0081, Val loss = 0.0053 Epoch 19: Train loss = 0.0080, Val loss = 0.0041 Epoch 20: Train loss = 0.0080, Val loss = 0.0061 Epoch 21: Train loss = 0.0079, Val loss = 0.0048 Epoch 22: Train loss = 0.0080, Val loss = 0.0044 Epoch 23: Train loss = 0.0077, Val loss = 0.0050 Epoch 24: Train loss = 0.0076, Val loss = 0.0063 Epoch 25: Train loss = 0.0077, Val loss = 0.0046 Epoch 26: Train loss = 0.0075, Val loss = 0.0053 Epoch 27: Train loss = 0.0074, Val loss = 0.0052 Epoch 28: Train loss = 0.0072, Val loss = 0.0054 Epoch 29: Train loss = 0.0074, Val loss = 0.0056 Epoch 30: Train loss = 0.0072, Val loss = 0.0055 Epoch 31: Train loss = 0.0072, Val loss = 0.0052 Epoch 32: Train loss = 0.0071, Val loss = 0.0049 Epoch 33: Train loss = 0.0071, Val loss = 0.0062 Epoch 34: Train loss = 0.0071, Val loss = 0.0070 Epoch 35: Train loss = 0.0070, Val loss = 0.0060 Epoch 36: Train loss = 0.0070, Val loss = 0.0055 Epoch 37: Train loss = 0.0068, Val loss = 0.0048 Epoch 38: Train loss = 0.0069, Val loss = 0.0046 Epoch 39: Train loss = 0.0070, Val loss = 0.0053 Epoch 40: Train loss = 0.0069, Val loss = 0.0061 Epoch 41: Train loss = 0.0069, Val loss = 0.0077 Epoch 42: Train loss = 0.0069, Val loss = 0.0070 Epoch 43: Train loss = 0.0067, Val loss = 0.0062 Epoch 44: Train loss = 0.0067, Val loss = 0.0048 Epoch 45: Train loss = 0.0066, Val loss = 0.0046 Epoch 46: Train loss = 0.0067, Val loss = 0.0049 Epoch 47: Train loss = 0.0064, Val loss = 0.0051 Epoch 48: Train loss = 0.0065, Val loss = 0.0063 Epoch 49: Train loss = 0.0064, Val loss = 0.0057 Epoch 50: Train loss = 0.0065, Val loss = 0.0059 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0451, Val loss = 0.0246 Epoch 2: Train loss = 0.0394, Val loss = 0.0193 Epoch 3: Train loss = 0.0335, Val loss = 0.0143 Epoch 4: Train loss = 0.0268, Val loss = 0.0099 Epoch 5: Train loss = 0.0204, Val loss = 0.0077 Epoch 6: Train loss = 0.0152, Val loss = 0.0085 Epoch 7: Train loss = 0.0124, Val loss = 0.0100 Epoch 8: Train loss = 0.0110, Val loss = 0.0093 Epoch 9: Train loss = 0.0103, Val loss = 0.0079 Epoch 10: Train loss = 0.0099, Val loss = 0.0070 Epoch 11: Train loss = 0.0097, Val loss = 0.0067 Epoch 12: Train loss = 0.0093, Val loss = 0.0069 Epoch 13: Train loss = 0.0090, Val loss = 0.0071 Epoch 14: Train loss = 0.0089, Val loss = 0.0073 Epoch 15: Train loss = 0.0088, Val loss = 0.0072 Epoch 16: Train loss = 0.0086, Val loss = 0.0073 Epoch 17: Train loss = 0.0085, Val loss = 0.0074 Epoch 18: Train loss = 0.0084, Val loss = 0.0076 Epoch 19: Train loss = 0.0084, Val loss = 0.0076 Epoch 20: Train loss = 0.0082, Val loss = 0.0081 Epoch 21: Train loss = 0.0081, Val loss = 0.0079 Epoch 22: Train loss = 0.0080, Val loss = 0.0080 Epoch 23: Train loss = 0.0079, Val loss = 0.0080 Epoch 24: Train loss = 0.0079, Val loss = 0.0082 Epoch 25: Train loss = 0.0079, Val loss = 0.0082 Epoch 26: Train loss = 0.0079, Val loss = 0.0082 Epoch 27: Train loss = 0.0078, Val loss = 0.0084 Epoch 28: Train loss = 0.0078, Val loss = 0.0089 Epoch 29: Train loss = 0.0076, Val loss = 0.0085 Epoch 30: Train loss = 0.0077, Val loss = 0.0086 Epoch 31: Train loss = 0.0076, Val loss = 0.0089 Epoch 32: Train loss = 0.0074, Val loss = 0.0087 Epoch 33: Train loss = 0.0076, Val loss = 0.0084 Epoch 34: Train loss = 0.0074, Val loss = 0.0090 Epoch 35: Train loss = 0.0074, Val loss = 0.0090 Epoch 36: Train loss = 0.0074, Val loss = 0.0086 Epoch 37: Train loss = 0.0074, Val loss = 0.0089 Epoch 38: Train loss = 0.0074, Val loss = 0.0087 Epoch 39: Train loss = 0.0072, Val loss = 0.0090 Epoch 40: Train loss = 0.0073, Val loss = 0.0087 Epoch 41: Train loss = 0.0072, Val loss = 0.0086 Epoch 42: Train loss = 0.0072, Val loss = 0.0092 Epoch 43: Train loss = 0.0070, Val loss = 0.0083 Epoch 44: Train loss = 0.0072, Val loss = 0.0089 Epoch 45: Train loss = 0.0071, Val loss = 0.0090 Epoch 46: Train loss = 0.0071, Val loss = 0.0085 Epoch 47: Train loss = 0.0070, Val loss = 0.0094 Epoch 48: Train loss = 0.0070, Val loss = 0.0083 Epoch 49: Train loss = 0.0069, Val loss = 0.0090 Epoch 50: Train loss = 0.0069, Val loss = 0.0082 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0263, Val loss = 0.0155 Epoch 2: Train loss = 0.0113, Val loss = 0.0056 Epoch 3: Train loss = 0.0094, Val loss = 0.0081 Epoch 4: Train loss = 0.0086, Val loss = 0.0077 Epoch 5: Train loss = 0.0082, Val loss = 0.0067 Epoch 6: Train loss = 0.0078, Val loss = 0.0108 Epoch 7: Train loss = 0.0077, Val loss = 0.0067 Epoch 8: Train loss = 0.0075, Val loss = 0.0097 Epoch 9: Train loss = 0.0075, Val loss = 0.0089 Epoch 10: Train loss = 0.0072, Val loss = 0.0081 Epoch 11: Train loss = 0.0071, Val loss = 0.0092 Epoch 12: Train loss = 0.0070, Val loss = 0.0080 Epoch 13: Train loss = 0.0068, Val loss = 0.0071 Epoch 14: Train loss = 0.0067, Val loss = 0.0072 Epoch 15: Train loss = 0.0069, Val loss = 0.0082 Epoch 16: Train loss = 0.0068, Val loss = 0.0101 Epoch 17: Train loss = 0.0068, Val loss = 0.0113 Epoch 18: Train loss = 0.0067, Val loss = 0.0097 Epoch 19: Train loss = 0.0066, Val loss = 0.0097 Epoch 20: Train loss = 0.0065, Val loss = 0.0086 Epoch 21: Train loss = 0.0064, Val loss = 0.0094 Epoch 22: Train loss = 0.0062, Val loss = 0.0056 Epoch 23: Train loss = 0.0063, Val loss = 0.0064 Epoch 24: Train loss = 0.0060, Val loss = 0.0067 Epoch 25: Train loss = 0.0061, Val loss = 0.0093 Epoch 26: Train loss = 0.0062, Val loss = 0.0073 Epoch 27: Train loss = 0.0062, Val loss = 0.0049 Epoch 28: Train loss = 0.0063, Val loss = 0.0064 Epoch 29: Train loss = 0.0060, Val loss = 0.0082 Epoch 30: Train loss = 0.0059, Val loss = 0.0091 Epoch 31: Train loss = 0.0058, Val loss = 0.0067 Epoch 32: Train loss = 0.0058, Val loss = 0.0042 Epoch 33: Train loss = 0.0058, Val loss = 0.0054 Epoch 34: Train loss = 0.0057, Val loss = 0.0058 Epoch 35: Train loss = 0.0056, Val loss = 0.0057 Epoch 36: Train loss = 0.0055, Val loss = 0.0062 Epoch 37: Train loss = 0.0055, Val loss = 0.0069 Epoch 38: Train loss = 0.0056, Val loss = 0.0066 Epoch 39: Train loss = 0.0055, Val loss = 0.0056 Epoch 40: Train loss = 0.0054, Val loss = 0.0064 Epoch 41: Train loss = 0.0054, Val loss = 0.0050 Epoch 42: Train loss = 0.0053, Val loss = 0.0075 Epoch 43: Train loss = 0.0054, Val loss = 0.0051 Epoch 44: Train loss = 0.0053, Val loss = 0.0076 Epoch 45: Train loss = 0.0053, Val loss = 0.0057 Epoch 46: Train loss = 0.0052, Val loss = 0.0068 Epoch 47: Train loss = 0.0053, Val loss = 0.0071 Epoch 48: Train loss = 0.0052, Val loss = 0.0043 Epoch 49: Train loss = 0.0052, Val loss = 0.0076 Epoch 50: Train loss = 0.0051, Val loss = 0.0093 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0476, Val loss = 0.0257 Epoch 2: Train loss = 0.0407, Val loss = 0.0201 Epoch 3: Train loss = 0.0341, Val loss = 0.0143 Epoch 4: Train loss = 0.0272, Val loss = 0.0098 Epoch 5: Train loss = 0.0202, Val loss = 0.0086 Epoch 6: Train loss = 0.0151, Val loss = 0.0103 Epoch 7: Train loss = 0.0123, Val loss = 0.0114 Epoch 8: Train loss = 0.0107, Val loss = 0.0102 Epoch 9: Train loss = 0.0099, Val loss = 0.0085 Epoch 10: Train loss = 0.0094, Val loss = 0.0076 Epoch 11: Train loss = 0.0092, Val loss = 0.0076 Epoch 12: Train loss = 0.0090, Val loss = 0.0081 Epoch 13: Train loss = 0.0088, Val loss = 0.0082 Epoch 14: Train loss = 0.0087, Val loss = 0.0079 Epoch 15: Train loss = 0.0085, Val loss = 0.0080 Epoch 16: Train loss = 0.0083, Val loss = 0.0081 Epoch 17: Train loss = 0.0082, Val loss = 0.0081 Epoch 18: Train loss = 0.0083, Val loss = 0.0080 Epoch 19: Train loss = 0.0082, Val loss = 0.0082 Epoch 20: Train loss = 0.0081, Val loss = 0.0084 Epoch 21: Train loss = 0.0080, Val loss = 0.0082 Epoch 22: Train loss = 0.0080, Val loss = 0.0080 Epoch 23: Train loss = 0.0079, Val loss = 0.0089 Epoch 24: Train loss = 0.0079, Val loss = 0.0087 Epoch 25: Train loss = 0.0078, Val loss = 0.0081 Epoch 26: Train loss = 0.0078, Val loss = 0.0086 Epoch 27: Train loss = 0.0078, Val loss = 0.0084 Epoch 28: Train loss = 0.0078, Val loss = 0.0084 Epoch 29: Train loss = 0.0077, Val loss = 0.0085 Epoch 30: Train loss = 0.0076, Val loss = 0.0089 Epoch 31: Train loss = 0.0075, Val loss = 0.0084 Epoch 32: Train loss = 0.0075, Val loss = 0.0083 Epoch 33: Train loss = 0.0076, Val loss = 0.0084 Epoch 34: Train loss = 0.0075, Val loss = 0.0089 Epoch 35: Train loss = 0.0075, Val loss = 0.0083 Epoch 36: Train loss = 0.0074, Val loss = 0.0087 Epoch 37: Train loss = 0.0073, Val loss = 0.0087 Epoch 38: Train loss = 0.0074, Val loss = 0.0084 Epoch 39: Train loss = 0.0073, Val loss = 0.0093 Epoch 40: Train loss = 0.0072, Val loss = 0.0083 Epoch 41: Train loss = 0.0073, Val loss = 0.0085 Epoch 42: Train loss = 0.0072, Val loss = 0.0091 Epoch 43: Train loss = 0.0072, Val loss = 0.0084 Epoch 44: Train loss = 0.0071, Val loss = 0.0089 Epoch 45: Train loss = 0.0070, Val loss = 0.0086 Epoch 46: Train loss = 0.0071, Val loss = 0.0085 Epoch 47: Train loss = 0.0071, Val loss = 0.0088 Epoch 48: Train loss = 0.0070, Val loss = 0.0088 Epoch 49: Train loss = 0.0070, Val loss = 0.0082 Epoch 50: Train loss = 0.0070, Val loss = 0.0089 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0350, Val loss = 0.0247 Epoch 2: Train loss = 0.0129, Val loss = 0.0051 Epoch 3: Train loss = 0.0106, Val loss = 0.0112 Epoch 4: Train loss = 0.0089, Val loss = 0.0058 Epoch 5: Train loss = 0.0083, Val loss = 0.0083 Epoch 6: Train loss = 0.0079, Val loss = 0.0066 Epoch 7: Train loss = 0.0077, Val loss = 0.0078 Epoch 8: Train loss = 0.0076, Val loss = 0.0093 Epoch 9: Train loss = 0.0075, Val loss = 0.0090 Epoch 10: Train loss = 0.0073, Val loss = 0.0059 Epoch 11: Train loss = 0.0072, Val loss = 0.0086 Epoch 12: Train loss = 0.0071, Val loss = 0.0102 Epoch 13: Train loss = 0.0069, Val loss = 0.0076 Epoch 14: Train loss = 0.0069, Val loss = 0.0072 Epoch 15: Train loss = 0.0068, Val loss = 0.0066 Epoch 16: Train loss = 0.0067, Val loss = 0.0065 Epoch 17: Train loss = 0.0067, Val loss = 0.0068 Epoch 18: Train loss = 0.0066, Val loss = 0.0067 Epoch 19: Train loss = 0.0065, Val loss = 0.0069 Epoch 20: Train loss = 0.0065, Val loss = 0.0057 Epoch 21: Train loss = 0.0064, Val loss = 0.0056 Epoch 22: Train loss = 0.0063, Val loss = 0.0054 Epoch 23: Train loss = 0.0064, Val loss = 0.0101 Epoch 24: Train loss = 0.0063, Val loss = 0.0082 Epoch 25: Train loss = 0.0063, Val loss = 0.0054 Epoch 26: Train loss = 0.0063, Val loss = 0.0048 Epoch 27: Train loss = 0.0063, Val loss = 0.0056 Epoch 28: Train loss = 0.0062, Val loss = 0.0077 Epoch 29: Train loss = 0.0061, Val loss = 0.0102 Epoch 30: Train loss = 0.0062, Val loss = 0.0089 Epoch 31: Train loss = 0.0062, Val loss = 0.0054 Epoch 32: Train loss = 0.0059, Val loss = 0.0055 Epoch 33: Train loss = 0.0059, Val loss = 0.0061 Epoch 34: Train loss = 0.0059, Val loss = 0.0077 Epoch 35: Train loss = 0.0058, Val loss = 0.0049 Epoch 36: Train loss = 0.0058, Val loss = 0.0044 Epoch 37: Train loss = 0.0060, Val loss = 0.0103 Epoch 38: Train loss = 0.0060, Val loss = 0.0072 Epoch 39: Train loss = 0.0058, Val loss = 0.0050 Epoch 40: Train loss = 0.0056, Val loss = 0.0039 Epoch 41: Train loss = 0.0057, Val loss = 0.0047 Epoch 42: Train loss = 0.0056, Val loss = 0.0052 Epoch 43: Train loss = 0.0055, Val loss = 0.0053 Epoch 44: Train loss = 0.0055, Val loss = 0.0060 Epoch 45: Train loss = 0.0054, Val loss = 0.0049 Epoch 46: Train loss = 0.0053, Val loss = 0.0046 Epoch 47: Train loss = 0.0053, Val loss = 0.0053 Epoch 48: Train loss = 0.0054, Val loss = 0.0041 Epoch 49: Train loss = 0.0053, Val loss = 0.0057 Epoch 50: Train loss = 0.0052, Val loss = 0.0057 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0483, Val loss = 0.0291 Epoch 2: Train loss = 0.0459, Val loss = 0.0266 Epoch 3: Train loss = 0.0426, Val loss = 0.0234 Epoch 4: Train loss = 0.0386, Val loss = 0.0194 Epoch 5: Train loss = 0.0334, Val loss = 0.0146 Epoch 6: Train loss = 0.0268, Val loss = 0.0100 Epoch 7: Train loss = 0.0200, Val loss = 0.0092 Epoch 8: Train loss = 0.0153, Val loss = 0.0121 Epoch 9: Train loss = 0.0127, Val loss = 0.0103 Epoch 10: Train loss = 0.0106, Val loss = 0.0070 Epoch 11: Train loss = 0.0095, Val loss = 0.0062 Epoch 12: Train loss = 0.0092, Val loss = 0.0070 Epoch 13: Train loss = 0.0090, Val loss = 0.0074 Epoch 14: Train loss = 0.0088, Val loss = 0.0069 Epoch 15: Train loss = 0.0086, Val loss = 0.0065 Epoch 16: Train loss = 0.0085, Val loss = 0.0068 Epoch 17: Train loss = 0.0083, Val loss = 0.0070 Epoch 18: Train loss = 0.0083, Val loss = 0.0068 Epoch 19: Train loss = 0.0081, Val loss = 0.0067 Epoch 20: Train loss = 0.0081, Val loss = 0.0072 Epoch 21: Train loss = 0.0081, Val loss = 0.0070 Epoch 22: Train loss = 0.0080, Val loss = 0.0070 Epoch 23: Train loss = 0.0080, Val loss = 0.0069 Epoch 24: Train loss = 0.0080, Val loss = 0.0069 Epoch 25: Train loss = 0.0080, Val loss = 0.0077 Epoch 26: Train loss = 0.0078, Val loss = 0.0073 Epoch 27: Train loss = 0.0078, Val loss = 0.0075 Epoch 28: Train loss = 0.0077, Val loss = 0.0075 Epoch 29: Train loss = 0.0077, Val loss = 0.0078 Epoch 30: Train loss = 0.0076, Val loss = 0.0074 Epoch 31: Train loss = 0.0077, Val loss = 0.0073 Epoch 32: Train loss = 0.0076, Val loss = 0.0080 Epoch 33: Train loss = 0.0075, Val loss = 0.0074 Epoch 34: Train loss = 0.0075, Val loss = 0.0075 Epoch 35: Train loss = 0.0075, Val loss = 0.0076 Epoch 36: Train loss = 0.0075, Val loss = 0.0074 Epoch 37: Train loss = 0.0075, Val loss = 0.0077 Epoch 38: Train loss = 0.0074, Val loss = 0.0076 Epoch 39: Train loss = 0.0073, Val loss = 0.0076 Epoch 40: Train loss = 0.0074, Val loss = 0.0074 Epoch 41: Train loss = 0.0073, Val loss = 0.0078 Epoch 42: Train loss = 0.0072, Val loss = 0.0070 Epoch 43: Train loss = 0.0073, Val loss = 0.0080 Epoch 44: Train loss = 0.0072, Val loss = 0.0072 Epoch 45: Train loss = 0.0072, Val loss = 0.0078 Epoch 46: Train loss = 0.0072, Val loss = 0.0075 Epoch 47: Train loss = 0.0071, Val loss = 0.0077 Epoch 48: Train loss = 0.0072, Val loss = 0.0076 Epoch 49: Train loss = 0.0071, Val loss = 0.0076 Epoch 50: Train loss = 0.0071, Val loss = 0.0074 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0440, Val loss = 0.0086 Epoch 2: Train loss = 0.0149, Val loss = 0.0043 Epoch 3: Train loss = 0.0111, Val loss = 0.0087 Epoch 4: Train loss = 0.0091, Val loss = 0.0048 Epoch 5: Train loss = 0.0083, Val loss = 0.0082 Epoch 6: Train loss = 0.0080, Val loss = 0.0057 Epoch 7: Train loss = 0.0076, Val loss = 0.0070 Epoch 8: Train loss = 0.0076, Val loss = 0.0078 Epoch 9: Train loss = 0.0075, Val loss = 0.0065 Epoch 10: Train loss = 0.0074, Val loss = 0.0079 Epoch 11: Train loss = 0.0073, Val loss = 0.0073 Epoch 12: Train loss = 0.0071, Val loss = 0.0081 Epoch 13: Train loss = 0.0071, Val loss = 0.0074 Epoch 14: Train loss = 0.0071, Val loss = 0.0075 Epoch 15: Train loss = 0.0070, Val loss = 0.0064 Epoch 16: Train loss = 0.0070, Val loss = 0.0063 Epoch 17: Train loss = 0.0068, Val loss = 0.0060 Epoch 18: Train loss = 0.0068, Val loss = 0.0061 Epoch 19: Train loss = 0.0068, Val loss = 0.0070 Epoch 20: Train loss = 0.0067, Val loss = 0.0092 Epoch 21: Train loss = 0.0067, Val loss = 0.0090 Epoch 22: Train loss = 0.0066, Val loss = 0.0094 Epoch 23: Train loss = 0.0066, Val loss = 0.0062 Epoch 24: Train loss = 0.0066, Val loss = 0.0073 Epoch 25: Train loss = 0.0065, Val loss = 0.0066 Epoch 26: Train loss = 0.0063, Val loss = 0.0088 Epoch 27: Train loss = 0.0064, Val loss = 0.0062 Epoch 28: Train loss = 0.0062, Val loss = 0.0069 Epoch 29: Train loss = 0.0062, Val loss = 0.0083 Epoch 30: Train loss = 0.0063, Val loss = 0.0071 Epoch 31: Train loss = 0.0062, Val loss = 0.0052 Epoch 32: Train loss = 0.0061, Val loss = 0.0055 Epoch 33: Train loss = 0.0061, Val loss = 0.0064 Epoch 34: Train loss = 0.0060, Val loss = 0.0041 Epoch 35: Train loss = 0.0060, Val loss = 0.0075 Epoch 36: Train loss = 0.0060, Val loss = 0.0067 Epoch 37: Train loss = 0.0061, Val loss = 0.0048 Epoch 38: Train loss = 0.0059, Val loss = 0.0052 Epoch 39: Train loss = 0.0058, Val loss = 0.0068 Epoch 40: Train loss = 0.0059, Val loss = 0.0053 Epoch 41: Train loss = 0.0055, Val loss = 0.0055 Epoch 42: Train loss = 0.0055, Val loss = 0.0047 Epoch 43: Train loss = 0.0055, Val loss = 0.0053 Epoch 44: Train loss = 0.0055, Val loss = 0.0065 Epoch 45: Train loss = 0.0055, Val loss = 0.0039 Epoch 46: Train loss = 0.0056, Val loss = 0.0062 Epoch 47: Train loss = 0.0054, Val loss = 0.0076 Epoch 48: Train loss = 0.0053, Val loss = 0.0062 Epoch 49: Train loss = 0.0053, Val loss = 0.0078 Epoch 50: Train loss = 0.0051, Val loss = 0.0056 Epoch 51: Train loss = 0.0051, Val loss = 0.0063 Epoch 52: Train loss = 0.0051, Val loss = 0.0077 Epoch 53: Train loss = 0.0050, Val loss = 0.0060 Epoch 54: Train loss = 0.0049, Val loss = 0.0068 Epoch 55: Train loss = 0.0050, Val loss = 0.0087 Early stopping triggered at epoch 55. Using device: mps Epoch 1: Train loss = 0.0425, Val loss = 0.0245 Epoch 2: Train loss = 0.0402, Val loss = 0.0225 Epoch 3: Train loss = 0.0375, Val loss = 0.0198 Epoch 4: Train loss = 0.0338, Val loss = 0.0161 Epoch 5: Train loss = 0.0286, Val loss = 0.0116 Epoch 6: Train loss = 0.0219, Val loss = 0.0087 Epoch 7: Train loss = 0.0160, Val loss = 0.0108 Epoch 8: Train loss = 0.0132, Val loss = 0.0127 Epoch 9: Train loss = 0.0114, Val loss = 0.0088 Epoch 10: Train loss = 0.0097, Val loss = 0.0062 Epoch 11: Train loss = 0.0091, Val loss = 0.0060 Epoch 12: Train loss = 0.0088, Val loss = 0.0069 Epoch 13: Train loss = 0.0085, Val loss = 0.0074 Epoch 14: Train loss = 0.0084, Val loss = 0.0067 Epoch 15: Train loss = 0.0083, Val loss = 0.0062 Epoch 16: Train loss = 0.0082, Val loss = 0.0066 Epoch 17: Train loss = 0.0081, Val loss = 0.0070 Epoch 18: Train loss = 0.0079, Val loss = 0.0067 Epoch 19: Train loss = 0.0079, Val loss = 0.0066 Epoch 20: Train loss = 0.0080, Val loss = 0.0068 Epoch 21: Train loss = 0.0078, Val loss = 0.0072 Epoch 22: Train loss = 0.0078, Val loss = 0.0072 Epoch 23: Train loss = 0.0077, Val loss = 0.0067 Epoch 24: Train loss = 0.0076, Val loss = 0.0071 Epoch 25: Train loss = 0.0077, Val loss = 0.0075 Epoch 26: Train loss = 0.0075, Val loss = 0.0075 Epoch 27: Train loss = 0.0076, Val loss = 0.0070 Epoch 28: Train loss = 0.0075, Val loss = 0.0076 Epoch 29: Train loss = 0.0075, Val loss = 0.0076 Epoch 30: Train loss = 0.0075, Val loss = 0.0071 Epoch 31: Train loss = 0.0075, Val loss = 0.0078 Epoch 32: Train loss = 0.0073, Val loss = 0.0079 Epoch 33: Train loss = 0.0073, Val loss = 0.0075 Epoch 34: Train loss = 0.0073, Val loss = 0.0079 Epoch 35: Train loss = 0.0073, Val loss = 0.0077 Epoch 36: Train loss = 0.0072, Val loss = 0.0077 Epoch 37: Train loss = 0.0073, Val loss = 0.0080 Epoch 38: Train loss = 0.0071, Val loss = 0.0076 Epoch 39: Train loss = 0.0071, Val loss = 0.0079 Epoch 40: Train loss = 0.0071, Val loss = 0.0076 Epoch 41: Train loss = 0.0072, Val loss = 0.0078 Epoch 42: Train loss = 0.0071, Val loss = 0.0079 Epoch 43: Train loss = 0.0070, Val loss = 0.0076 Epoch 44: Train loss = 0.0071, Val loss = 0.0079 Epoch 45: Train loss = 0.0071, Val loss = 0.0076 Epoch 46: Train loss = 0.0070, Val loss = 0.0076 Epoch 47: Train loss = 0.0069, Val loss = 0.0078 Epoch 48: Train loss = 0.0070, Val loss = 0.0074 Epoch 49: Train loss = 0.0068, Val loss = 0.0079 Epoch 50: Train loss = 0.0069, Val loss = 0.0070 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0402, Val loss = 0.0096 Epoch 2: Train loss = 0.0143, Val loss = 0.0039 Epoch 3: Train loss = 0.0099, Val loss = 0.0087 Epoch 4: Train loss = 0.0088, Val loss = 0.0050 Epoch 5: Train loss = 0.0078, Val loss = 0.0077 Epoch 6: Train loss = 0.0076, Val loss = 0.0059 Epoch 7: Train loss = 0.0075, Val loss = 0.0076 Epoch 8: Train loss = 0.0074, Val loss = 0.0072 Epoch 9: Train loss = 0.0073, Val loss = 0.0067 Epoch 10: Train loss = 0.0071, Val loss = 0.0071 Epoch 11: Train loss = 0.0071, Val loss = 0.0087 Epoch 12: Train loss = 0.0071, Val loss = 0.0062 Epoch 13: Train loss = 0.0071, Val loss = 0.0057 Epoch 14: Train loss = 0.0068, Val loss = 0.0070 Epoch 15: Train loss = 0.0069, Val loss = 0.0095 Epoch 16: Train loss = 0.0067, Val loss = 0.0093 Epoch 17: Train loss = 0.0068, Val loss = 0.0076 Epoch 18: Train loss = 0.0068, Val loss = 0.0057 Epoch 19: Train loss = 0.0066, Val loss = 0.0051 Epoch 20: Train loss = 0.0067, Val loss = 0.0069 Epoch 21: Train loss = 0.0067, Val loss = 0.0091 Epoch 22: Train loss = 0.0065, Val loss = 0.0078 Epoch 23: Train loss = 0.0063, Val loss = 0.0060 Epoch 24: Train loss = 0.0064, Val loss = 0.0062 Epoch 25: Train loss = 0.0063, Val loss = 0.0069 Epoch 26: Train loss = 0.0062, Val loss = 0.0075 Epoch 27: Train loss = 0.0063, Val loss = 0.0063 Epoch 28: Train loss = 0.0062, Val loss = 0.0058 Epoch 29: Train loss = 0.0059, Val loss = 0.0074 Epoch 30: Train loss = 0.0059, Val loss = 0.0059 Epoch 31: Train loss = 0.0058, Val loss = 0.0044 Epoch 32: Train loss = 0.0059, Val loss = 0.0074 Epoch 33: Train loss = 0.0058, Val loss = 0.0048 Epoch 34: Train loss = 0.0057, Val loss = 0.0064 Epoch 35: Train loss = 0.0059, Val loss = 0.0046 Epoch 36: Train loss = 0.0058, Val loss = 0.0061 Epoch 37: Train loss = 0.0056, Val loss = 0.0071 Epoch 38: Train loss = 0.0055, Val loss = 0.0045 Epoch 39: Train loss = 0.0054, Val loss = 0.0065 Epoch 40: Train loss = 0.0054, Val loss = 0.0050 Epoch 41: Train loss = 0.0053, Val loss = 0.0060 Epoch 42: Train loss = 0.0053, Val loss = 0.0045 Epoch 43: Train loss = 0.0054, Val loss = 0.0049 Epoch 44: Train loss = 0.0051, Val loss = 0.0066 Epoch 45: Train loss = 0.0049, Val loss = 0.0064 Epoch 46: Train loss = 0.0050, Val loss = 0.0051 Epoch 47: Train loss = 0.0049, Val loss = 0.0061 Epoch 48: Train loss = 0.0048, Val loss = 0.0075 Epoch 49: Train loss = 0.0048, Val loss = 0.0064 Epoch 50: Train loss = 0.0047, Val loss = 0.0067 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0484, Val loss = 0.0301 Epoch 2: Train loss = 0.0469, Val loss = 0.0286 Epoch 3: Train loss = 0.0451, Val loss = 0.0274 Epoch 4: Train loss = 0.0435, Val loss = 0.0264 Epoch 5: Train loss = 0.0422, Val loss = 0.0256 Epoch 6: Train loss = 0.0409, Val loss = 0.0249 Epoch 7: Train loss = 0.0399, Val loss = 0.0244 Epoch 8: Train loss = 0.0388, Val loss = 0.0239 Epoch 9: Train loss = 0.0380, Val loss = 0.0234 Epoch 10: Train loss = 0.0370, Val loss = 0.0230 Epoch 11: Train loss = 0.0362, Val loss = 0.0226 Epoch 12: Train loss = 0.0353, Val loss = 0.0222 Epoch 13: Train loss = 0.0345, Val loss = 0.0218 Epoch 14: Train loss = 0.0336, Val loss = 0.0214 Epoch 15: Train loss = 0.0328, Val loss = 0.0210 Epoch 16: Train loss = 0.0319, Val loss = 0.0206 Epoch 17: Train loss = 0.0312, Val loss = 0.0201 Epoch 18: Train loss = 0.0305, Val loss = 0.0197 Epoch 19: Train loss = 0.0297, Val loss = 0.0193 Epoch 20: Train loss = 0.0289, Val loss = 0.0188 Epoch 21: Train loss = 0.0282, Val loss = 0.0183 Epoch 22: Train loss = 0.0274, Val loss = 0.0178 Epoch 23: Train loss = 0.0265, Val loss = 0.0172 Epoch 24: Train loss = 0.0255, Val loss = 0.0167 Epoch 25: Train loss = 0.0245, Val loss = 0.0161 Epoch 26: Train loss = 0.0236, Val loss = 0.0157 Epoch 27: Train loss = 0.0226, Val loss = 0.0154 Epoch 28: Train loss = 0.0217, Val loss = 0.0152 Epoch 29: Train loss = 0.0207, Val loss = 0.0150 Epoch 30: Train loss = 0.0199, Val loss = 0.0149 Epoch 31: Train loss = 0.0192, Val loss = 0.0149 Epoch 32: Train loss = 0.0184, Val loss = 0.0150 Epoch 33: Train loss = 0.0178, Val loss = 0.0150 Epoch 34: Train loss = 0.0172, Val loss = 0.0149 Epoch 35: Train loss = 0.0166, Val loss = 0.0147 Epoch 36: Train loss = 0.0161, Val loss = 0.0145 Epoch 37: Train loss = 0.0156, Val loss = 0.0142 Epoch 38: Train loss = 0.0151, Val loss = 0.0139 Epoch 39: Train loss = 0.0146, Val loss = 0.0135 Epoch 40: Train loss = 0.0141, Val loss = 0.0132 Epoch 41: Train loss = 0.0137, Val loss = 0.0129 Epoch 42: Train loss = 0.0132, Val loss = 0.0127 Epoch 43: Train loss = 0.0128, Val loss = 0.0124 Epoch 44: Train loss = 0.0125, Val loss = 0.0123 Epoch 45: Train loss = 0.0121, Val loss = 0.0121 Epoch 46: Train loss = 0.0117, Val loss = 0.0120 Epoch 47: Train loss = 0.0114, Val loss = 0.0118 Epoch 48: Train loss = 0.0111, Val loss = 0.0117 Epoch 49: Train loss = 0.0108, Val loss = 0.0116 Epoch 50: Train loss = 0.0106, Val loss = 0.0115 Epoch 51: Train loss = 0.0103, Val loss = 0.0114 Epoch 52: Train loss = 0.0100, Val loss = 0.0113 Epoch 53: Train loss = 0.0098, Val loss = 0.0112 Epoch 54: Train loss = 0.0096, Val loss = 0.0111 Epoch 55: Train loss = 0.0094, Val loss = 0.0110 Epoch 56: Train loss = 0.0092, Val loss = 0.0109 Epoch 57: Train loss = 0.0091, Val loss = 0.0108 Epoch 58: Train loss = 0.0089, Val loss = 0.0107 Epoch 59: Train loss = 0.0087, Val loss = 0.0106 Epoch 60: Train loss = 0.0086, Val loss = 0.0106 Epoch 61: Train loss = 0.0085, Val loss = 0.0105 Epoch 62: Train loss = 0.0083, Val loss = 0.0105 Epoch 63: Train loss = 0.0082, Val loss = 0.0104 Epoch 64: Train loss = 0.0081, Val loss = 0.0104 Epoch 65: Train loss = 0.0080, Val loss = 0.0104 Epoch 66: Train loss = 0.0079, Val loss = 0.0103 Epoch 67: Train loss = 0.0078, Val loss = 0.0103 Epoch 68: Train loss = 0.0077, Val loss = 0.0102 Epoch 69: Train loss = 0.0076, Val loss = 0.0102 Epoch 70: Train loss = 0.0075, Val loss = 0.0101 Epoch 71: Train loss = 0.0075, Val loss = 0.0101 Epoch 72: Train loss = 0.0074, Val loss = 0.0101 Epoch 73: Train loss = 0.0073, Val loss = 0.0101 Epoch 74: Train loss = 0.0073, Val loss = 0.0101 Epoch 75: Train loss = 0.0072, Val loss = 0.0101 Epoch 76: Train loss = 0.0072, Val loss = 0.0101 Epoch 77: Train loss = 0.0071, Val loss = 0.0101 Epoch 78: Train loss = 0.0071, Val loss = 0.0101 Epoch 79: Train loss = 0.0070, Val loss = 0.0101 Epoch 80: Train loss = 0.0070, Val loss = 0.0101 Epoch 81: Train loss = 0.0069, Val loss = 0.0101 Epoch 82: Train loss = 0.0069, Val loss = 0.0101 Epoch 83: Train loss = 0.0069, Val loss = 0.0101 Epoch 84: Train loss = 0.0068, Val loss = 0.0101 Epoch 85: Train loss = 0.0068, Val loss = 0.0101 Epoch 86: Train loss = 0.0068, Val loss = 0.0101 Epoch 87: Train loss = 0.0068, Val loss = 0.0101 Early stopping triggered at epoch 87. Using device: mps Epoch 1: Train loss = 0.0544, Val loss = 0.0311 Epoch 2: Train loss = 0.0452, Val loss = 0.0249 Epoch 3: Train loss = 0.0370, Val loss = 0.0209 Epoch 4: Train loss = 0.0295, Val loss = 0.0185 Epoch 5: Train loss = 0.0230, Val loss = 0.0167 Epoch 6: Train loss = 0.0176, Val loss = 0.0152 Epoch 7: Train loss = 0.0135, Val loss = 0.0134 Epoch 8: Train loss = 0.0108, Val loss = 0.0118 Epoch 9: Train loss = 0.0093, Val loss = 0.0107 Epoch 10: Train loss = 0.0083, Val loss = 0.0096 Epoch 11: Train loss = 0.0076, Val loss = 0.0089 Epoch 12: Train loss = 0.0071, Val loss = 0.0091 Epoch 13: Train loss = 0.0069, Val loss = 0.0094 Epoch 14: Train loss = 0.0069, Val loss = 0.0091 Epoch 15: Train loss = 0.0068, Val loss = 0.0087 Epoch 16: Train loss = 0.0068, Val loss = 0.0087 Epoch 17: Train loss = 0.0067, Val loss = 0.0092 Epoch 18: Train loss = 0.0067, Val loss = 0.0094 Epoch 19: Train loss = 0.0067, Val loss = 0.0094 Epoch 20: Train loss = 0.0066, Val loss = 0.0093 Epoch 21: Train loss = 0.0066, Val loss = 0.0093 Epoch 22: Train loss = 0.0065, Val loss = 0.0092 Epoch 23: Train loss = 0.0065, Val loss = 0.0092 Epoch 24: Train loss = 0.0065, Val loss = 0.0094 Epoch 25: Train loss = 0.0065, Val loss = 0.0095 Epoch 26: Train loss = 0.0065, Val loss = 0.0095 Epoch 27: Train loss = 0.0065, Val loss = 0.0091 Epoch 28: Train loss = 0.0065, Val loss = 0.0094 Epoch 29: Train loss = 0.0064, Val loss = 0.0098 Epoch 30: Train loss = 0.0064, Val loss = 0.0096 Epoch 31: Train loss = 0.0064, Val loss = 0.0093 Epoch 32: Train loss = 0.0064, Val loss = 0.0094 Epoch 33: Train loss = 0.0064, Val loss = 0.0095 Epoch 34: Train loss = 0.0064, Val loss = 0.0096 Epoch 35: Train loss = 0.0064, Val loss = 0.0097 Epoch 36: Train loss = 0.0064, Val loss = 0.0097 Epoch 37: Train loss = 0.0064, Val loss = 0.0094 Epoch 38: Train loss = 0.0064, Val loss = 0.0095 Epoch 39: Train loss = 0.0063, Val loss = 0.0097 Epoch 40: Train loss = 0.0064, Val loss = 0.0096 Epoch 41: Train loss = 0.0063, Val loss = 0.0096 Epoch 42: Train loss = 0.0063, Val loss = 0.0095 Epoch 43: Train loss = 0.0063, Val loss = 0.0094 Epoch 44: Train loss = 0.0063, Val loss = 0.0098 Epoch 45: Train loss = 0.0063, Val loss = 0.0098 Epoch 46: Train loss = 0.0062, Val loss = 0.0096 Epoch 47: Train loss = 0.0062, Val loss = 0.0095 Epoch 48: Train loss = 0.0063, Val loss = 0.0095 Epoch 49: Train loss = 0.0062, Val loss = 0.0096 Epoch 50: Train loss = 0.0062, Val loss = 0.0098 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0640, Val loss = 0.0436 Epoch 2: Train loss = 0.0619, Val loss = 0.0419 Epoch 3: Train loss = 0.0604, Val loss = 0.0406 Epoch 4: Train loss = 0.0592, Val loss = 0.0395 Epoch 5: Train loss = 0.0580, Val loss = 0.0384 Epoch 6: Train loss = 0.0567, Val loss = 0.0373 Epoch 7: Train loss = 0.0552, Val loss = 0.0361 Epoch 8: Train loss = 0.0537, Val loss = 0.0348 Epoch 9: Train loss = 0.0522, Val loss = 0.0336 Epoch 10: Train loss = 0.0505, Val loss = 0.0324 Epoch 11: Train loss = 0.0490, Val loss = 0.0312 Epoch 12: Train loss = 0.0474, Val loss = 0.0300 Epoch 13: Train loss = 0.0458, Val loss = 0.0290 Epoch 14: Train loss = 0.0442, Val loss = 0.0282 Epoch 15: Train loss = 0.0426, Val loss = 0.0275 Epoch 16: Train loss = 0.0412, Val loss = 0.0269 Epoch 17: Train loss = 0.0399, Val loss = 0.0263 Epoch 18: Train loss = 0.0386, Val loss = 0.0259 Epoch 19: Train loss = 0.0375, Val loss = 0.0254 Epoch 20: Train loss = 0.0364, Val loss = 0.0250 Epoch 21: Train loss = 0.0353, Val loss = 0.0245 Epoch 22: Train loss = 0.0342, Val loss = 0.0239 Epoch 23: Train loss = 0.0333, Val loss = 0.0234 Epoch 24: Train loss = 0.0322, Val loss = 0.0228 Epoch 25: Train loss = 0.0312, Val loss = 0.0222 Epoch 26: Train loss = 0.0302, Val loss = 0.0216 Epoch 27: Train loss = 0.0292, Val loss = 0.0210 Epoch 28: Train loss = 0.0282, Val loss = 0.0204 Epoch 29: Train loss = 0.0272, Val loss = 0.0197 Epoch 30: Train loss = 0.0263, Val loss = 0.0190 Epoch 31: Train loss = 0.0253, Val loss = 0.0184 Epoch 32: Train loss = 0.0243, Val loss = 0.0177 Epoch 33: Train loss = 0.0233, Val loss = 0.0171 Epoch 34: Train loss = 0.0224, Val loss = 0.0165 Epoch 35: Train loss = 0.0215, Val loss = 0.0159 Epoch 36: Train loss = 0.0206, Val loss = 0.0153 Epoch 37: Train loss = 0.0198, Val loss = 0.0149 Epoch 38: Train loss = 0.0190, Val loss = 0.0144 Epoch 39: Train loss = 0.0182, Val loss = 0.0140 Epoch 40: Train loss = 0.0174, Val loss = 0.0135 Epoch 41: Train loss = 0.0166, Val loss = 0.0131 Epoch 42: Train loss = 0.0158, Val loss = 0.0127 Epoch 43: Train loss = 0.0152, Val loss = 0.0123 Epoch 44: Train loss = 0.0145, Val loss = 0.0120 Epoch 45: Train loss = 0.0139, Val loss = 0.0117 Epoch 46: Train loss = 0.0133, Val loss = 0.0114 Epoch 47: Train loss = 0.0127, Val loss = 0.0111 Epoch 48: Train loss = 0.0122, Val loss = 0.0108 Epoch 49: Train loss = 0.0117, Val loss = 0.0106 Epoch 50: Train loss = 0.0113, Val loss = 0.0103 Epoch 51: Train loss = 0.0108, Val loss = 0.0101 Epoch 52: Train loss = 0.0104, Val loss = 0.0099 Epoch 53: Train loss = 0.0101, Val loss = 0.0097 Epoch 54: Train loss = 0.0097, Val loss = 0.0096 Epoch 55: Train loss = 0.0094, Val loss = 0.0095 Epoch 56: Train loss = 0.0091, Val loss = 0.0094 Epoch 57: Train loss = 0.0088, Val loss = 0.0094 Epoch 58: Train loss = 0.0086, Val loss = 0.0093 Epoch 59: Train loss = 0.0084, Val loss = 0.0093 Epoch 60: Train loss = 0.0081, Val loss = 0.0093 Epoch 61: Train loss = 0.0080, Val loss = 0.0093 Epoch 62: Train loss = 0.0078, Val loss = 0.0093 Epoch 63: Train loss = 0.0077, Val loss = 0.0093 Epoch 64: Train loss = 0.0076, Val loss = 0.0093 Epoch 65: Train loss = 0.0075, Val loss = 0.0093 Epoch 66: Train loss = 0.0073, Val loss = 0.0093 Epoch 67: Train loss = 0.0073, Val loss = 0.0093 Epoch 68: Train loss = 0.0072, Val loss = 0.0093 Early stopping triggered at epoch 68. Using device: mps Epoch 1: Train loss = 0.0399, Val loss = 0.0214 Epoch 2: Train loss = 0.0312, Val loss = 0.0199 Epoch 3: Train loss = 0.0252, Val loss = 0.0151 Epoch 4: Train loss = 0.0200, Val loss = 0.0139 Epoch 5: Train loss = 0.0165, Val loss = 0.0143 Epoch 6: Train loss = 0.0138, Val loss = 0.0135 Epoch 7: Train loss = 0.0114, Val loss = 0.0111 Epoch 8: Train loss = 0.0096, Val loss = 0.0100 Epoch 9: Train loss = 0.0084, Val loss = 0.0101 Epoch 10: Train loss = 0.0078, Val loss = 0.0095 Epoch 11: Train loss = 0.0074, Val loss = 0.0088 Epoch 12: Train loss = 0.0070, Val loss = 0.0090 Epoch 13: Train loss = 0.0069, Val loss = 0.0092 Epoch 14: Train loss = 0.0067, Val loss = 0.0092 Epoch 15: Train loss = 0.0067, Val loss = 0.0095 Epoch 16: Train loss = 0.0067, Val loss = 0.0092 Epoch 17: Train loss = 0.0066, Val loss = 0.0090 Epoch 18: Train loss = 0.0066, Val loss = 0.0092 Epoch 19: Train loss = 0.0066, Val loss = 0.0096 Epoch 20: Train loss = 0.0065, Val loss = 0.0092 Epoch 21: Train loss = 0.0065, Val loss = 0.0095 Epoch 22: Train loss = 0.0065, Val loss = 0.0092 Epoch 23: Train loss = 0.0064, Val loss = 0.0095 Epoch 24: Train loss = 0.0064, Val loss = 0.0097 Epoch 25: Train loss = 0.0064, Val loss = 0.0093 Epoch 26: Train loss = 0.0064, Val loss = 0.0099 Epoch 27: Train loss = 0.0063, Val loss = 0.0097 Epoch 28: Train loss = 0.0064, Val loss = 0.0094 Epoch 29: Train loss = 0.0063, Val loss = 0.0104 Epoch 30: Train loss = 0.0063, Val loss = 0.0095 Epoch 31: Train loss = 0.0063, Val loss = 0.0099 Epoch 32: Train loss = 0.0063, Val loss = 0.0101 Epoch 33: Train loss = 0.0063, Val loss = 0.0096 Epoch 34: Train loss = 0.0063, Val loss = 0.0104 Epoch 35: Train loss = 0.0062, Val loss = 0.0097 Epoch 36: Train loss = 0.0062, Val loss = 0.0101 Epoch 37: Train loss = 0.0062, Val loss = 0.0096 Epoch 38: Train loss = 0.0062, Val loss = 0.0101 Epoch 39: Train loss = 0.0062, Val loss = 0.0097 Epoch 40: Train loss = 0.0061, Val loss = 0.0103 Epoch 41: Train loss = 0.0061, Val loss = 0.0097 Epoch 42: Train loss = 0.0061, Val loss = 0.0100 Epoch 43: Train loss = 0.0061, Val loss = 0.0100 Epoch 44: Train loss = 0.0061, Val loss = 0.0099 Epoch 45: Train loss = 0.0060, Val loss = 0.0103 Epoch 46: Train loss = 0.0060, Val loss = 0.0091 Epoch 47: Train loss = 0.0060, Val loss = 0.0101 Epoch 48: Train loss = 0.0060, Val loss = 0.0091 Epoch 49: Train loss = 0.0060, Val loss = 0.0102 Epoch 50: Train loss = 0.0059, Val loss = 0.0094 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0430, Val loss = 0.0266 Epoch 2: Train loss = 0.0423, Val loss = 0.0260 Epoch 3: Train loss = 0.0416, Val loss = 0.0254 Epoch 4: Train loss = 0.0409, Val loss = 0.0247 Epoch 5: Train loss = 0.0401, Val loss = 0.0240 Epoch 6: Train loss = 0.0393, Val loss = 0.0233 Epoch 7: Train loss = 0.0385, Val loss = 0.0226 Epoch 8: Train loss = 0.0377, Val loss = 0.0220 Epoch 9: Train loss = 0.0370, Val loss = 0.0215 Epoch 10: Train loss = 0.0362, Val loss = 0.0209 Epoch 11: Train loss = 0.0355, Val loss = 0.0204 Epoch 12: Train loss = 0.0349, Val loss = 0.0199 Epoch 13: Train loss = 0.0342, Val loss = 0.0194 Epoch 14: Train loss = 0.0334, Val loss = 0.0188 Epoch 15: Train loss = 0.0328, Val loss = 0.0183 Epoch 16: Train loss = 0.0320, Val loss = 0.0178 Epoch 17: Train loss = 0.0313, Val loss = 0.0172 Epoch 18: Train loss = 0.0305, Val loss = 0.0167 Epoch 19: Train loss = 0.0297, Val loss = 0.0162 Epoch 20: Train loss = 0.0289, Val loss = 0.0157 Epoch 21: Train loss = 0.0282, Val loss = 0.0152 Epoch 22: Train loss = 0.0273, Val loss = 0.0147 Epoch 23: Train loss = 0.0265, Val loss = 0.0142 Epoch 24: Train loss = 0.0255, Val loss = 0.0137 Epoch 25: Train loss = 0.0248, Val loss = 0.0133 Epoch 26: Train loss = 0.0239, Val loss = 0.0129 Epoch 27: Train loss = 0.0231, Val loss = 0.0126 Epoch 28: Train loss = 0.0222, Val loss = 0.0122 Epoch 29: Train loss = 0.0214, Val loss = 0.0119 Epoch 30: Train loss = 0.0206, Val loss = 0.0117 Epoch 31: Train loss = 0.0199, Val loss = 0.0114 Epoch 32: Train loss = 0.0191, Val loss = 0.0112 Epoch 33: Train loss = 0.0184, Val loss = 0.0110 Epoch 34: Train loss = 0.0177, Val loss = 0.0109 Epoch 35: Train loss = 0.0170, Val loss = 0.0107 Epoch 36: Train loss = 0.0163, Val loss = 0.0106 Epoch 37: Train loss = 0.0158, Val loss = 0.0104 Epoch 38: Train loss = 0.0151, Val loss = 0.0102 Epoch 39: Train loss = 0.0146, Val loss = 0.0100 Epoch 40: Train loss = 0.0140, Val loss = 0.0098 Epoch 41: Train loss = 0.0135, Val loss = 0.0096 Epoch 42: Train loss = 0.0130, Val loss = 0.0094 Epoch 43: Train loss = 0.0125, Val loss = 0.0092 Epoch 44: Train loss = 0.0120, Val loss = 0.0090 Epoch 45: Train loss = 0.0116, Val loss = 0.0089 Epoch 46: Train loss = 0.0112, Val loss = 0.0088 Epoch 47: Train loss = 0.0108, Val loss = 0.0087 Epoch 48: Train loss = 0.0105, Val loss = 0.0086 Epoch 49: Train loss = 0.0102, Val loss = 0.0085 Epoch 50: Train loss = 0.0099, Val loss = 0.0084 Epoch 51: Train loss = 0.0096, Val loss = 0.0084 Epoch 52: Train loss = 0.0093, Val loss = 0.0083 Epoch 53: Train loss = 0.0091, Val loss = 0.0083 Epoch 54: Train loss = 0.0089, Val loss = 0.0083 Epoch 55: Train loss = 0.0087, Val loss = 0.0083 Epoch 56: Train loss = 0.0086, Val loss = 0.0082 Epoch 57: Train loss = 0.0084, Val loss = 0.0082 Epoch 58: Train loss = 0.0083, Val loss = 0.0082 Epoch 59: Train loss = 0.0081, Val loss = 0.0081 Epoch 60: Train loss = 0.0080, Val loss = 0.0081 Epoch 61: Train loss = 0.0080, Val loss = 0.0081 Epoch 62: Train loss = 0.0078, Val loss = 0.0081 Epoch 63: Train loss = 0.0077, Val loss = 0.0081 Epoch 64: Train loss = 0.0077, Val loss = 0.0081 Epoch 65: Train loss = 0.0076, Val loss = 0.0080 Epoch 66: Train loss = 0.0075, Val loss = 0.0080 Epoch 67: Train loss = 0.0074, Val loss = 0.0080 Epoch 68: Train loss = 0.0074, Val loss = 0.0080 Epoch 69: Train loss = 0.0073, Val loss = 0.0080 Epoch 70: Train loss = 0.0073, Val loss = 0.0080 Epoch 71: Train loss = 0.0072, Val loss = 0.0080 Early stopping triggered at epoch 71. Using device: mps Epoch 1: Train loss = 0.0545, Val loss = 0.0329 Epoch 2: Train loss = 0.0492, Val loss = 0.0289 Epoch 3: Train loss = 0.0443, Val loss = 0.0242 Epoch 4: Train loss = 0.0383, Val loss = 0.0191 Epoch 5: Train loss = 0.0311, Val loss = 0.0164 Epoch 6: Train loss = 0.0246, Val loss = 0.0192 Epoch 7: Train loss = 0.0208, Val loss = 0.0206 Epoch 8: Train loss = 0.0173, Val loss = 0.0156 Epoch 9: Train loss = 0.0136, Val loss = 0.0107 Epoch 10: Train loss = 0.0113, Val loss = 0.0082 Epoch 11: Train loss = 0.0098, Val loss = 0.0085 Epoch 12: Train loss = 0.0085, Val loss = 0.0103 Epoch 13: Train loss = 0.0077, Val loss = 0.0110 Epoch 14: Train loss = 0.0073, Val loss = 0.0094 Epoch 15: Train loss = 0.0070, Val loss = 0.0081 Epoch 16: Train loss = 0.0069, Val loss = 0.0083 Epoch 17: Train loss = 0.0069, Val loss = 0.0094 Epoch 18: Train loss = 0.0068, Val loss = 0.0093 Epoch 19: Train loss = 0.0068, Val loss = 0.0087 Epoch 20: Train loss = 0.0067, Val loss = 0.0087 Epoch 21: Train loss = 0.0066, Val loss = 0.0090 Epoch 22: Train loss = 0.0066, Val loss = 0.0091 Epoch 23: Train loss = 0.0066, Val loss = 0.0090 Epoch 24: Train loss = 0.0066, Val loss = 0.0090 Epoch 25: Train loss = 0.0065, Val loss = 0.0090 Epoch 26: Train loss = 0.0065, Val loss = 0.0091 Epoch 27: Train loss = 0.0065, Val loss = 0.0093 Epoch 28: Train loss = 0.0065, Val loss = 0.0094 Epoch 29: Train loss = 0.0065, Val loss = 0.0092 Epoch 30: Train loss = 0.0065, Val loss = 0.0095 Epoch 31: Train loss = 0.0065, Val loss = 0.0097 Epoch 32: Train loss = 0.0065, Val loss = 0.0094 Epoch 33: Train loss = 0.0065, Val loss = 0.0095 Epoch 34: Train loss = 0.0065, Val loss = 0.0096 Epoch 35: Train loss = 0.0065, Val loss = 0.0095 Epoch 36: Train loss = 0.0065, Val loss = 0.0095 Epoch 37: Train loss = 0.0065, Val loss = 0.0097 Epoch 38: Train loss = 0.0065, Val loss = 0.0096 Epoch 39: Train loss = 0.0064, Val loss = 0.0100 Epoch 40: Train loss = 0.0064, Val loss = 0.0096 Epoch 41: Train loss = 0.0064, Val loss = 0.0096 Epoch 42: Train loss = 0.0064, Val loss = 0.0100 Epoch 43: Train loss = 0.0064, Val loss = 0.0098 Epoch 44: Train loss = 0.0064, Val loss = 0.0095 Epoch 45: Train loss = 0.0064, Val loss = 0.0100 Epoch 46: Train loss = 0.0064, Val loss = 0.0102 Epoch 47: Train loss = 0.0064, Val loss = 0.0095 Epoch 48: Train loss = 0.0064, Val loss = 0.0096 Epoch 49: Train loss = 0.0064, Val loss = 0.0106 Epoch 50: Train loss = 0.0064, Val loss = 0.0099 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0658, Val loss = 0.0448 Epoch 2: Train loss = 0.0651, Val loss = 0.0441 Epoch 3: Train loss = 0.0643, Val loss = 0.0434 Epoch 4: Train loss = 0.0634, Val loss = 0.0427 Epoch 5: Train loss = 0.0627, Val loss = 0.0421 Epoch 6: Train loss = 0.0620, Val loss = 0.0416 Epoch 7: Train loss = 0.0614, Val loss = 0.0411 Epoch 8: Train loss = 0.0608, Val loss = 0.0406 Epoch 9: Train loss = 0.0602, Val loss = 0.0401 Epoch 10: Train loss = 0.0594, Val loss = 0.0395 Epoch 11: Train loss = 0.0589, Val loss = 0.0390 Epoch 12: Train loss = 0.0583, Val loss = 0.0384 Epoch 13: Train loss = 0.0577, Val loss = 0.0379 Epoch 14: Train loss = 0.0571, Val loss = 0.0373 Epoch 15: Train loss = 0.0564, Val loss = 0.0368 Epoch 16: Train loss = 0.0559, Val loss = 0.0362 Epoch 17: Train loss = 0.0551, Val loss = 0.0357 Epoch 18: Train loss = 0.0544, Val loss = 0.0351 Epoch 19: Train loss = 0.0539, Val loss = 0.0345 Epoch 20: Train loss = 0.0531, Val loss = 0.0339 Epoch 21: Train loss = 0.0523, Val loss = 0.0333 Epoch 22: Train loss = 0.0517, Val loss = 0.0327 Epoch 23: Train loss = 0.0508, Val loss = 0.0320 Epoch 24: Train loss = 0.0500, Val loss = 0.0313 Epoch 25: Train loss = 0.0492, Val loss = 0.0306 Epoch 26: Train loss = 0.0483, Val loss = 0.0299 Epoch 27: Train loss = 0.0475, Val loss = 0.0291 Epoch 28: Train loss = 0.0466, Val loss = 0.0283 Epoch 29: Train loss = 0.0455, Val loss = 0.0275 Epoch 30: Train loss = 0.0446, Val loss = 0.0266 Epoch 31: Train loss = 0.0435, Val loss = 0.0257 Epoch 32: Train loss = 0.0424, Val loss = 0.0248 Epoch 33: Train loss = 0.0412, Val loss = 0.0239 Epoch 34: Train loss = 0.0401, Val loss = 0.0230 Epoch 35: Train loss = 0.0388, Val loss = 0.0220 Epoch 36: Train loss = 0.0376, Val loss = 0.0211 Epoch 37: Train loss = 0.0361, Val loss = 0.0202 Epoch 38: Train loss = 0.0349, Val loss = 0.0193 Epoch 39: Train loss = 0.0334, Val loss = 0.0184 Epoch 40: Train loss = 0.0322, Val loss = 0.0176 Epoch 41: Train loss = 0.0308, Val loss = 0.0168 Epoch 42: Train loss = 0.0293, Val loss = 0.0160 Epoch 43: Train loss = 0.0280, Val loss = 0.0154 Epoch 44: Train loss = 0.0267, Val loss = 0.0147 Epoch 45: Train loss = 0.0254, Val loss = 0.0142 Epoch 46: Train loss = 0.0241, Val loss = 0.0137 Epoch 47: Train loss = 0.0229, Val loss = 0.0133 Epoch 48: Train loss = 0.0218, Val loss = 0.0130 Epoch 49: Train loss = 0.0207, Val loss = 0.0128 Epoch 50: Train loss = 0.0196, Val loss = 0.0127 Epoch 51: Train loss = 0.0187, Val loss = 0.0126 Epoch 52: Train loss = 0.0177, Val loss = 0.0125 Epoch 53: Train loss = 0.0169, Val loss = 0.0124 Epoch 54: Train loss = 0.0161, Val loss = 0.0123 Epoch 55: Train loss = 0.0154, Val loss = 0.0122 Epoch 56: Train loss = 0.0147, Val loss = 0.0120 Epoch 57: Train loss = 0.0142, Val loss = 0.0118 Epoch 58: Train loss = 0.0136, Val loss = 0.0116 Epoch 59: Train loss = 0.0131, Val loss = 0.0114 Epoch 60: Train loss = 0.0127, Val loss = 0.0112 Epoch 61: Train loss = 0.0122, Val loss = 0.0111 Epoch 62: Train loss = 0.0119, Val loss = 0.0110 Epoch 63: Train loss = 0.0114, Val loss = 0.0109 Epoch 64: Train loss = 0.0111, Val loss = 0.0107 Epoch 65: Train loss = 0.0108, Val loss = 0.0106 Epoch 66: Train loss = 0.0105, Val loss = 0.0105 Epoch 67: Train loss = 0.0102, Val loss = 0.0103 Epoch 68: Train loss = 0.0100, Val loss = 0.0102 Epoch 69: Train loss = 0.0097, Val loss = 0.0101 Epoch 70: Train loss = 0.0095, Val loss = 0.0100 Epoch 71: Train loss = 0.0093, Val loss = 0.0099 Epoch 72: Train loss = 0.0091, Val loss = 0.0099 Epoch 73: Train loss = 0.0089, Val loss = 0.0098 Epoch 74: Train loss = 0.0087, Val loss = 0.0097 Epoch 75: Train loss = 0.0086, Val loss = 0.0096 Epoch 76: Train loss = 0.0084, Val loss = 0.0096 Epoch 77: Train loss = 0.0083, Val loss = 0.0096 Epoch 78: Train loss = 0.0082, Val loss = 0.0095 Epoch 79: Train loss = 0.0080, Val loss = 0.0095 Epoch 80: Train loss = 0.0079, Val loss = 0.0095 Epoch 81: Train loss = 0.0078, Val loss = 0.0094 Epoch 82: Train loss = 0.0077, Val loss = 0.0094 Epoch 83: Train loss = 0.0076, Val loss = 0.0094 Epoch 84: Train loss = 0.0075, Val loss = 0.0094 Epoch 85: Train loss = 0.0075, Val loss = 0.0094 Epoch 86: Train loss = 0.0074, Val loss = 0.0093 Epoch 87: Train loss = 0.0073, Val loss = 0.0093 Epoch 88: Train loss = 0.0073, Val loss = 0.0093 Epoch 89: Train loss = 0.0072, Val loss = 0.0092 Epoch 90: Train loss = 0.0072, Val loss = 0.0093 Epoch 91: Train loss = 0.0071, Val loss = 0.0093 Epoch 92: Train loss = 0.0071, Val loss = 0.0093 Epoch 93: Train loss = 0.0070, Val loss = 0.0093 Epoch 94: Train loss = 0.0070, Val loss = 0.0093 Epoch 95: Train loss = 0.0069, Val loss = 0.0093 Epoch 96: Train loss = 0.0070, Val loss = 0.0092 Epoch 97: Train loss = 0.0069, Val loss = 0.0092 Epoch 98: Train loss = 0.0069, Val loss = 0.0092 Epoch 99: Train loss = 0.0069, Val loss = 0.0092 Epoch 100: Train loss = 0.0068, Val loss = 0.0093 Using device: mps Epoch 1: Train loss = 0.0537, Val loss = 0.0332 Epoch 2: Train loss = 0.0494, Val loss = 0.0297 Epoch 3: Train loss = 0.0451, Val loss = 0.0262 Epoch 4: Train loss = 0.0405, Val loss = 0.0229 Epoch 5: Train loss = 0.0356, Val loss = 0.0203 Epoch 6: Train loss = 0.0304, Val loss = 0.0194 Epoch 7: Train loss = 0.0255, Val loss = 0.0198 Epoch 8: Train loss = 0.0213, Val loss = 0.0172 Epoch 9: Train loss = 0.0165, Val loss = 0.0132 Epoch 10: Train loss = 0.0125, Val loss = 0.0107 Epoch 11: Train loss = 0.0101, Val loss = 0.0103 Epoch 12: Train loss = 0.0089, Val loss = 0.0103 Epoch 13: Train loss = 0.0081, Val loss = 0.0101 Epoch 14: Train loss = 0.0077, Val loss = 0.0093 Epoch 15: Train loss = 0.0074, Val loss = 0.0089 Epoch 16: Train loss = 0.0071, Val loss = 0.0091 Epoch 17: Train loss = 0.0069, Val loss = 0.0090 Epoch 18: Train loss = 0.0068, Val loss = 0.0085 Epoch 19: Train loss = 0.0067, Val loss = 0.0083 Epoch 20: Train loss = 0.0067, Val loss = 0.0086 Epoch 21: Train loss = 0.0067, Val loss = 0.0090 Epoch 22: Train loss = 0.0066, Val loss = 0.0089 Epoch 23: Train loss = 0.0066, Val loss = 0.0086 Epoch 24: Train loss = 0.0066, Val loss = 0.0090 Epoch 25: Train loss = 0.0066, Val loss = 0.0091 Epoch 26: Train loss = 0.0065, Val loss = 0.0091 Epoch 27: Train loss = 0.0065, Val loss = 0.0092 Epoch 28: Train loss = 0.0065, Val loss = 0.0095 Epoch 29: Train loss = 0.0065, Val loss = 0.0092 Epoch 30: Train loss = 0.0065, Val loss = 0.0093 Epoch 31: Train loss = 0.0065, Val loss = 0.0095 Epoch 32: Train loss = 0.0065, Val loss = 0.0097 Epoch 33: Train loss = 0.0065, Val loss = 0.0095 Epoch 34: Train loss = 0.0065, Val loss = 0.0094 Epoch 35: Train loss = 0.0065, Val loss = 0.0096 Epoch 36: Train loss = 0.0065, Val loss = 0.0097 Epoch 37: Train loss = 0.0065, Val loss = 0.0097 Epoch 38: Train loss = 0.0064, Val loss = 0.0094 Epoch 39: Train loss = 0.0064, Val loss = 0.0096 Epoch 40: Train loss = 0.0064, Val loss = 0.0099 Epoch 41: Train loss = 0.0064, Val loss = 0.0095 Epoch 42: Train loss = 0.0064, Val loss = 0.0094 Epoch 43: Train loss = 0.0064, Val loss = 0.0097 Epoch 44: Train loss = 0.0064, Val loss = 0.0101 Epoch 45: Train loss = 0.0064, Val loss = 0.0094 Epoch 46: Train loss = 0.0064, Val loss = 0.0099 Epoch 47: Train loss = 0.0064, Val loss = 0.0100 Epoch 48: Train loss = 0.0064, Val loss = 0.0097 Epoch 49: Train loss = 0.0064, Val loss = 0.0096 Epoch 50: Train loss = 0.0063, Val loss = 0.0102 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0532, Val loss = 0.0318 Epoch 2: Train loss = 0.0491, Val loss = 0.0279 Epoch 3: Train loss = 0.0449, Val loss = 0.0241 Epoch 4: Train loss = 0.0409, Val loss = 0.0205 Epoch 5: Train loss = 0.0367, Val loss = 0.0170 Epoch 6: Train loss = 0.0324, Val loss = 0.0137 Epoch 7: Train loss = 0.0279, Val loss = 0.0109 Epoch 8: Train loss = 0.0235, Val loss = 0.0087 Epoch 9: Train loss = 0.0192, Val loss = 0.0077 Epoch 10: Train loss = 0.0156, Val loss = 0.0083 Epoch 11: Train loss = 0.0128, Val loss = 0.0101 Epoch 12: Train loss = 0.0110, Val loss = 0.0123 Epoch 13: Train loss = 0.0099, Val loss = 0.0139 Epoch 14: Train loss = 0.0091, Val loss = 0.0141 Epoch 15: Train loss = 0.0085, Val loss = 0.0134 Epoch 16: Train loss = 0.0079, Val loss = 0.0121 Epoch 17: Train loss = 0.0075, Val loss = 0.0108 Epoch 18: Train loss = 0.0072, Val loss = 0.0099 Epoch 19: Train loss = 0.0071, Val loss = 0.0093 Epoch 20: Train loss = 0.0071, Val loss = 0.0090 Epoch 21: Train loss = 0.0070, Val loss = 0.0091 Epoch 22: Train loss = 0.0070, Val loss = 0.0093 Epoch 23: Train loss = 0.0069, Val loss = 0.0096 Epoch 24: Train loss = 0.0068, Val loss = 0.0098 Epoch 25: Train loss = 0.0068, Val loss = 0.0099 Epoch 26: Train loss = 0.0067, Val loss = 0.0097 Epoch 27: Train loss = 0.0067, Val loss = 0.0096 Epoch 28: Train loss = 0.0067, Val loss = 0.0094 Epoch 29: Train loss = 0.0067, Val loss = 0.0094 Epoch 30: Train loss = 0.0066, Val loss = 0.0093 Epoch 31: Train loss = 0.0066, Val loss = 0.0094 Epoch 32: Train loss = 0.0065, Val loss = 0.0094 Epoch 33: Train loss = 0.0065, Val loss = 0.0094 Epoch 34: Train loss = 0.0065, Val loss = 0.0095 Epoch 35: Train loss = 0.0065, Val loss = 0.0095 Epoch 36: Train loss = 0.0065, Val loss = 0.0096 Epoch 37: Train loss = 0.0065, Val loss = 0.0096 Epoch 38: Train loss = 0.0065, Val loss = 0.0096 Epoch 39: Train loss = 0.0064, Val loss = 0.0096 Epoch 40: Train loss = 0.0064, Val loss = 0.0096 Epoch 41: Train loss = 0.0064, Val loss = 0.0097 Epoch 42: Train loss = 0.0064, Val loss = 0.0097 Epoch 43: Train loss = 0.0064, Val loss = 0.0098 Epoch 44: Train loss = 0.0064, Val loss = 0.0099 Epoch 45: Train loss = 0.0064, Val loss = 0.0099 Epoch 46: Train loss = 0.0064, Val loss = 0.0099 Epoch 47: Train loss = 0.0063, Val loss = 0.0099 Epoch 48: Train loss = 0.0063, Val loss = 0.0099 Epoch 49: Train loss = 0.0064, Val loss = 0.0100 Epoch 50: Train loss = 0.0063, Val loss = 0.0101 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0414, Val loss = 0.0089 Epoch 2: Train loss = 0.0152, Val loss = 0.0320 Epoch 3: Train loss = 0.0114, Val loss = 0.0106 Epoch 4: Train loss = 0.0085, Val loss = 0.0063 Epoch 5: Train loss = 0.0082, Val loss = 0.0117 Epoch 6: Train loss = 0.0075, Val loss = 0.0118 Epoch 7: Train loss = 0.0069, Val loss = 0.0092 Epoch 8: Train loss = 0.0069, Val loss = 0.0098 Epoch 9: Train loss = 0.0067, Val loss = 0.0105 Epoch 10: Train loss = 0.0066, Val loss = 0.0096 Epoch 11: Train loss = 0.0066, Val loss = 0.0091 Epoch 12: Train loss = 0.0065, Val loss = 0.0108 Epoch 13: Train loss = 0.0065, Val loss = 0.0102 Epoch 14: Train loss = 0.0064, Val loss = 0.0097 Epoch 15: Train loss = 0.0064, Val loss = 0.0104 Epoch 16: Train loss = 0.0064, Val loss = 0.0105 Epoch 17: Train loss = 0.0063, Val loss = 0.0099 Epoch 18: Train loss = 0.0063, Val loss = 0.0105 Epoch 19: Train loss = 0.0063, Val loss = 0.0100 Epoch 20: Train loss = 0.0062, Val loss = 0.0103 Epoch 21: Train loss = 0.0062, Val loss = 0.0100 Epoch 22: Train loss = 0.0062, Val loss = 0.0102 Epoch 23: Train loss = 0.0062, Val loss = 0.0104 Epoch 24: Train loss = 0.0062, Val loss = 0.0100 Epoch 25: Train loss = 0.0061, Val loss = 0.0106 Epoch 26: Train loss = 0.0061, Val loss = 0.0100 Epoch 27: Train loss = 0.0061, Val loss = 0.0100 Epoch 28: Train loss = 0.0060, Val loss = 0.0104 Epoch 29: Train loss = 0.0060, Val loss = 0.0099 Epoch 30: Train loss = 0.0060, Val loss = 0.0104 Epoch 31: Train loss = 0.0060, Val loss = 0.0099 Epoch 32: Train loss = 0.0059, Val loss = 0.0107 Epoch 33: Train loss = 0.0059, Val loss = 0.0101 Epoch 34: Train loss = 0.0059, Val loss = 0.0100 Epoch 35: Train loss = 0.0058, Val loss = 0.0094 Epoch 36: Train loss = 0.0058, Val loss = 0.0096 Epoch 37: Train loss = 0.0058, Val loss = 0.0103 Epoch 38: Train loss = 0.0058, Val loss = 0.0095 Epoch 39: Train loss = 0.0057, Val loss = 0.0093 Epoch 40: Train loss = 0.0057, Val loss = 0.0090 Epoch 41: Train loss = 0.0057, Val loss = 0.0091 Epoch 42: Train loss = 0.0056, Val loss = 0.0089 Epoch 43: Train loss = 0.0056, Val loss = 0.0088 Epoch 44: Train loss = 0.0056, Val loss = 0.0082 Epoch 45: Train loss = 0.0055, Val loss = 0.0077 Epoch 46: Train loss = 0.0055, Val loss = 0.0069 Epoch 47: Train loss = 0.0055, Val loss = 0.0068 Epoch 48: Train loss = 0.0055, Val loss = 0.0070 Epoch 49: Train loss = 0.0055, Val loss = 0.0072 Epoch 50: Train loss = 0.0054, Val loss = 0.0077 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0465, Val loss = 0.0263 Epoch 2: Train loss = 0.0422, Val loss = 0.0223 Epoch 3: Train loss = 0.0379, Val loss = 0.0186 Epoch 4: Train loss = 0.0335, Val loss = 0.0150 Epoch 5: Train loss = 0.0290, Val loss = 0.0117 Epoch 6: Train loss = 0.0246, Val loss = 0.0090 Epoch 7: Train loss = 0.0203, Val loss = 0.0073 Epoch 8: Train loss = 0.0164, Val loss = 0.0070 Epoch 9: Train loss = 0.0133, Val loss = 0.0082 Epoch 10: Train loss = 0.0111, Val loss = 0.0105 Epoch 11: Train loss = 0.0099, Val loss = 0.0128 Epoch 12: Train loss = 0.0092, Val loss = 0.0141 Epoch 13: Train loss = 0.0087, Val loss = 0.0139 Epoch 14: Train loss = 0.0081, Val loss = 0.0128 Epoch 15: Train loss = 0.0077, Val loss = 0.0113 Epoch 16: Train loss = 0.0074, Val loss = 0.0100 Epoch 17: Train loss = 0.0073, Val loss = 0.0091 Epoch 18: Train loss = 0.0072, Val loss = 0.0087 Epoch 19: Train loss = 0.0071, Val loss = 0.0086 Epoch 20: Train loss = 0.0071, Val loss = 0.0089 Epoch 21: Train loss = 0.0070, Val loss = 0.0093 Epoch 22: Train loss = 0.0069, Val loss = 0.0097 Epoch 23: Train loss = 0.0068, Val loss = 0.0099 Epoch 24: Train loss = 0.0068, Val loss = 0.0098 Epoch 25: Train loss = 0.0067, Val loss = 0.0097 Epoch 26: Train loss = 0.0067, Val loss = 0.0095 Epoch 27: Train loss = 0.0067, Val loss = 0.0094 Epoch 28: Train loss = 0.0066, Val loss = 0.0094 Epoch 29: Train loss = 0.0066, Val loss = 0.0094 Epoch 30: Train loss = 0.0066, Val loss = 0.0094 Epoch 31: Train loss = 0.0066, Val loss = 0.0095 Epoch 32: Train loss = 0.0066, Val loss = 0.0096 Epoch 33: Train loss = 0.0065, Val loss = 0.0097 Epoch 34: Train loss = 0.0065, Val loss = 0.0097 Epoch 35: Train loss = 0.0065, Val loss = 0.0096 Epoch 36: Train loss = 0.0065, Val loss = 0.0096 Epoch 37: Train loss = 0.0065, Val loss = 0.0097 Epoch 38: Train loss = 0.0065, Val loss = 0.0097 Epoch 39: Train loss = 0.0065, Val loss = 0.0097 Epoch 40: Train loss = 0.0064, Val loss = 0.0099 Epoch 41: Train loss = 0.0064, Val loss = 0.0099 Epoch 42: Train loss = 0.0064, Val loss = 0.0100 Epoch 43: Train loss = 0.0064, Val loss = 0.0100 Epoch 44: Train loss = 0.0064, Val loss = 0.0100 Epoch 45: Train loss = 0.0064, Val loss = 0.0100 Epoch 46: Train loss = 0.0064, Val loss = 0.0100 Epoch 47: Train loss = 0.0064, Val loss = 0.0100 Epoch 48: Train loss = 0.0064, Val loss = 0.0101 Epoch 49: Train loss = 0.0064, Val loss = 0.0101 Epoch 50: Train loss = 0.0064, Val loss = 0.0101 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0378, Val loss = 0.0121 Epoch 2: Train loss = 0.0155, Val loss = 0.0216 Epoch 3: Train loss = 0.0091, Val loss = 0.0085 Epoch 4: Train loss = 0.0091, Val loss = 0.0081 Epoch 5: Train loss = 0.0077, Val loss = 0.0121 Epoch 6: Train loss = 0.0072, Val loss = 0.0088 Epoch 7: Train loss = 0.0069, Val loss = 0.0076 Epoch 8: Train loss = 0.0068, Val loss = 0.0098 Epoch 9: Train loss = 0.0067, Val loss = 0.0096 Epoch 10: Train loss = 0.0065, Val loss = 0.0088 Epoch 11: Train loss = 0.0065, Val loss = 0.0101 Epoch 12: Train loss = 0.0065, Val loss = 0.0104 Epoch 13: Train loss = 0.0065, Val loss = 0.0101 Epoch 14: Train loss = 0.0064, Val loss = 0.0104 Epoch 15: Train loss = 0.0064, Val loss = 0.0104 Epoch 16: Train loss = 0.0063, Val loss = 0.0107 Epoch 17: Train loss = 0.0063, Val loss = 0.0102 Epoch 18: Train loss = 0.0063, Val loss = 0.0105 Epoch 19: Train loss = 0.0063, Val loss = 0.0109 Epoch 20: Train loss = 0.0062, Val loss = 0.0101 Epoch 21: Train loss = 0.0062, Val loss = 0.0105 Epoch 22: Train loss = 0.0062, Val loss = 0.0106 Epoch 23: Train loss = 0.0062, Val loss = 0.0101 Epoch 24: Train loss = 0.0062, Val loss = 0.0107 Epoch 25: Train loss = 0.0061, Val loss = 0.0102 Epoch 26: Train loss = 0.0061, Val loss = 0.0101 Epoch 27: Train loss = 0.0061, Val loss = 0.0106 Epoch 28: Train loss = 0.0060, Val loss = 0.0097 Epoch 29: Train loss = 0.0061, Val loss = 0.0110 Epoch 30: Train loss = 0.0060, Val loss = 0.0101 Epoch 31: Train loss = 0.0060, Val loss = 0.0102 Epoch 32: Train loss = 0.0060, Val loss = 0.0104 Epoch 33: Train loss = 0.0059, Val loss = 0.0105 Epoch 34: Train loss = 0.0059, Val loss = 0.0098 Epoch 35: Train loss = 0.0059, Val loss = 0.0110 Epoch 36: Train loss = 0.0059, Val loss = 0.0097 Epoch 37: Train loss = 0.0059, Val loss = 0.0106 Epoch 38: Train loss = 0.0059, Val loss = 0.0100 Epoch 39: Train loss = 0.0058, Val loss = 0.0092 Epoch 40: Train loss = 0.0058, Val loss = 0.0104 Epoch 41: Train loss = 0.0058, Val loss = 0.0097 Epoch 42: Train loss = 0.0058, Val loss = 0.0095 Epoch 43: Train loss = 0.0057, Val loss = 0.0093 Epoch 44: Train loss = 0.0057, Val loss = 0.0095 Epoch 45: Train loss = 0.0057, Val loss = 0.0084 Epoch 46: Train loss = 0.0057, Val loss = 0.0079 Epoch 47: Train loss = 0.0056, Val loss = 0.0101 Epoch 48: Train loss = 0.0057, Val loss = 0.0098 Epoch 49: Train loss = 0.0056, Val loss = 0.0079 Epoch 50: Train loss = 0.0056, Val loss = 0.0075 Epoch 51: Train loss = 0.0056, Val loss = 0.0077 Epoch 52: Train loss = 0.0055, Val loss = 0.0094 Epoch 53: Train loss = 0.0055, Val loss = 0.0077 Epoch 54: Train loss = 0.0054, Val loss = 0.0074 Epoch 55: Train loss = 0.0054, Val loss = 0.0087 Epoch 56: Train loss = 0.0054, Val loss = 0.0086 Epoch 57: Train loss = 0.0054, Val loss = 0.0083 Epoch 58: Train loss = 0.0053, Val loss = 0.0072 Epoch 59: Train loss = 0.0053, Val loss = 0.0060 Epoch 60: Train loss = 0.0053, Val loss = 0.0056 Epoch 61: Train loss = 0.0054, Val loss = 0.0055 Epoch 62: Train loss = 0.0056, Val loss = 0.0076 Epoch 63: Train loss = 0.0054, Val loss = 0.0086 Epoch 64: Train loss = 0.0053, Val loss = 0.0067 Epoch 65: Train loss = 0.0053, Val loss = 0.0052 Epoch 66: Train loss = 0.0053, Val loss = 0.0065 Epoch 67: Train loss = 0.0052, Val loss = 0.0083 Epoch 68: Train loss = 0.0052, Val loss = 0.0068 Epoch 69: Train loss = 0.0051, Val loss = 0.0057 Epoch 70: Train loss = 0.0051, Val loss = 0.0061 Epoch 71: Train loss = 0.0050, Val loss = 0.0063 Epoch 72: Train loss = 0.0050, Val loss = 0.0061 Epoch 73: Train loss = 0.0050, Val loss = 0.0064 Epoch 74: Train loss = 0.0049, Val loss = 0.0063 Epoch 75: Train loss = 0.0049, Val loss = 0.0064 Early stopping triggered at epoch 75. Using device: mps Epoch 1: Train loss = 0.0569, Val loss = 0.0366 Epoch 2: Train loss = 0.0554, Val loss = 0.0353 Epoch 3: Train loss = 0.0540, Val loss = 0.0341 Epoch 4: Train loss = 0.0525, Val loss = 0.0326 Epoch 5: Train loss = 0.0509, Val loss = 0.0310 Epoch 6: Train loss = 0.0489, Val loss = 0.0292 Epoch 7: Train loss = 0.0467, Val loss = 0.0270 Epoch 8: Train loss = 0.0442, Val loss = 0.0245 Epoch 9: Train loss = 0.0412, Val loss = 0.0217 Epoch 10: Train loss = 0.0378, Val loss = 0.0187 Epoch 11: Train loss = 0.0341, Val loss = 0.0154 Epoch 12: Train loss = 0.0299, Val loss = 0.0121 Epoch 13: Train loss = 0.0252, Val loss = 0.0093 Epoch 14: Train loss = 0.0205, Val loss = 0.0076 Epoch 15: Train loss = 0.0162, Val loss = 0.0079 Epoch 16: Train loss = 0.0129, Val loss = 0.0105 Epoch 17: Train loss = 0.0110, Val loss = 0.0142 Epoch 18: Train loss = 0.0103, Val loss = 0.0162 Epoch 19: Train loss = 0.0096, Val loss = 0.0154 Epoch 20: Train loss = 0.0086, Val loss = 0.0129 Epoch 21: Train loss = 0.0078, Val loss = 0.0104 Epoch 22: Train loss = 0.0074, Val loss = 0.0087 Epoch 23: Train loss = 0.0073, Val loss = 0.0079 Epoch 24: Train loss = 0.0072, Val loss = 0.0079 Epoch 25: Train loss = 0.0072, Val loss = 0.0085 Epoch 26: Train loss = 0.0071, Val loss = 0.0092 Epoch 27: Train loss = 0.0070, Val loss = 0.0099 Epoch 28: Train loss = 0.0069, Val loss = 0.0102 Epoch 29: Train loss = 0.0069, Val loss = 0.0100 Epoch 30: Train loss = 0.0069, Val loss = 0.0096 Epoch 31: Train loss = 0.0068, Val loss = 0.0092 Epoch 32: Train loss = 0.0068, Val loss = 0.0089 Epoch 33: Train loss = 0.0068, Val loss = 0.0089 Epoch 34: Train loss = 0.0067, Val loss = 0.0091 Epoch 35: Train loss = 0.0067, Val loss = 0.0093 Epoch 36: Train loss = 0.0066, Val loss = 0.0096 Epoch 37: Train loss = 0.0066, Val loss = 0.0096 Epoch 38: Train loss = 0.0066, Val loss = 0.0095 Epoch 39: Train loss = 0.0066, Val loss = 0.0093 Epoch 40: Train loss = 0.0066, Val loss = 0.0092 Epoch 41: Train loss = 0.0066, Val loss = 0.0091 Epoch 42: Train loss = 0.0066, Val loss = 0.0092 Epoch 43: Train loss = 0.0065, Val loss = 0.0093 Epoch 44: Train loss = 0.0065, Val loss = 0.0095 Epoch 45: Train loss = 0.0065, Val loss = 0.0095 Epoch 46: Train loss = 0.0065, Val loss = 0.0095 Epoch 47: Train loss = 0.0065, Val loss = 0.0093 Epoch 48: Train loss = 0.0065, Val loss = 0.0093 Epoch 49: Train loss = 0.0065, Val loss = 0.0094 Epoch 50: Train loss = 0.0065, Val loss = 0.0094 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0441, Val loss = 0.0200 Epoch 2: Train loss = 0.0302, Val loss = 0.0086 Epoch 3: Train loss = 0.0154, Val loss = 0.0256 Epoch 4: Train loss = 0.0100, Val loss = 0.0065 Epoch 5: Train loss = 0.0082, Val loss = 0.0057 Epoch 6: Train loss = 0.0083, Val loss = 0.0104 Epoch 7: Train loss = 0.0077, Val loss = 0.0118 Epoch 8: Train loss = 0.0071, Val loss = 0.0072 Epoch 9: Train loss = 0.0069, Val loss = 0.0070 Epoch 10: Train loss = 0.0067, Val loss = 0.0102 Epoch 11: Train loss = 0.0067, Val loss = 0.0096 Epoch 12: Train loss = 0.0067, Val loss = 0.0074 Epoch 13: Train loss = 0.0066, Val loss = 0.0092 Epoch 14: Train loss = 0.0065, Val loss = 0.0105 Epoch 15: Train loss = 0.0065, Val loss = 0.0090 Epoch 16: Train loss = 0.0065, Val loss = 0.0089 Epoch 17: Train loss = 0.0065, Val loss = 0.0103 Epoch 18: Train loss = 0.0064, Val loss = 0.0101 Epoch 19: Train loss = 0.0064, Val loss = 0.0093 Epoch 20: Train loss = 0.0064, Val loss = 0.0108 Epoch 21: Train loss = 0.0064, Val loss = 0.0102 Epoch 22: Train loss = 0.0064, Val loss = 0.0098 Epoch 23: Train loss = 0.0064, Val loss = 0.0106 Epoch 24: Train loss = 0.0064, Val loss = 0.0105 Epoch 25: Train loss = 0.0063, Val loss = 0.0100 Epoch 26: Train loss = 0.0063, Val loss = 0.0103 Epoch 27: Train loss = 0.0063, Val loss = 0.0105 Epoch 28: Train loss = 0.0063, Val loss = 0.0099 Epoch 29: Train loss = 0.0063, Val loss = 0.0105 Epoch 30: Train loss = 0.0063, Val loss = 0.0100 Epoch 31: Train loss = 0.0063, Val loss = 0.0100 Epoch 32: Train loss = 0.0063, Val loss = 0.0101 Epoch 33: Train loss = 0.0062, Val loss = 0.0097 Epoch 34: Train loss = 0.0062, Val loss = 0.0102 Epoch 35: Train loss = 0.0062, Val loss = 0.0104 Epoch 36: Train loss = 0.0062, Val loss = 0.0094 Epoch 37: Train loss = 0.0062, Val loss = 0.0102 Epoch 38: Train loss = 0.0061, Val loss = 0.0094 Epoch 39: Train loss = 0.0061, Val loss = 0.0104 Epoch 40: Train loss = 0.0061, Val loss = 0.0092 Epoch 41: Train loss = 0.0061, Val loss = 0.0104 Epoch 42: Train loss = 0.0061, Val loss = 0.0093 Epoch 43: Train loss = 0.0060, Val loss = 0.0098 Epoch 44: Train loss = 0.0060, Val loss = 0.0100 Epoch 45: Train loss = 0.0060, Val loss = 0.0083 Epoch 46: Train loss = 0.0060, Val loss = 0.0122 Epoch 47: Train loss = 0.0060, Val loss = 0.0075 Epoch 48: Train loss = 0.0060, Val loss = 0.0124 Epoch 49: Train loss = 0.0059, Val loss = 0.0082 Epoch 50: Train loss = 0.0059, Val loss = 0.0100 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0498, Val loss = 0.0308 Epoch 2: Train loss = 0.0487, Val loss = 0.0296 Epoch 3: Train loss = 0.0473, Val loss = 0.0284 Epoch 4: Train loss = 0.0459, Val loss = 0.0271 Epoch 5: Train loss = 0.0442, Val loss = 0.0256 Epoch 6: Train loss = 0.0425, Val loss = 0.0240 Epoch 7: Train loss = 0.0405, Val loss = 0.0220 Epoch 8: Train loss = 0.0383, Val loss = 0.0198 Epoch 9: Train loss = 0.0355, Val loss = 0.0174 Epoch 10: Train loss = 0.0324, Val loss = 0.0147 Epoch 11: Train loss = 0.0288, Val loss = 0.0119 Epoch 12: Train loss = 0.0249, Val loss = 0.0093 Epoch 13: Train loss = 0.0209, Val loss = 0.0076 Epoch 14: Train loss = 0.0169, Val loss = 0.0071 Epoch 15: Train loss = 0.0137, Val loss = 0.0087 Epoch 16: Train loss = 0.0114, Val loss = 0.0118 Epoch 17: Train loss = 0.0104, Val loss = 0.0147 Epoch 18: Train loss = 0.0097, Val loss = 0.0154 Epoch 19: Train loss = 0.0090, Val loss = 0.0138 Epoch 20: Train loss = 0.0081, Val loss = 0.0113 Epoch 21: Train loss = 0.0075, Val loss = 0.0091 Epoch 22: Train loss = 0.0073, Val loss = 0.0079 Epoch 23: Train loss = 0.0073, Val loss = 0.0076 Epoch 24: Train loss = 0.0072, Val loss = 0.0079 Epoch 25: Train loss = 0.0071, Val loss = 0.0086 Epoch 26: Train loss = 0.0071, Val loss = 0.0093 Epoch 27: Train loss = 0.0070, Val loss = 0.0098 Epoch 28: Train loss = 0.0070, Val loss = 0.0099 Epoch 29: Train loss = 0.0069, Val loss = 0.0096 Epoch 30: Train loss = 0.0068, Val loss = 0.0091 Epoch 31: Train loss = 0.0068, Val loss = 0.0087 Epoch 32: Train loss = 0.0068, Val loss = 0.0085 Epoch 33: Train loss = 0.0067, Val loss = 0.0086 Epoch 34: Train loss = 0.0067, Val loss = 0.0089 Epoch 35: Train loss = 0.0067, Val loss = 0.0092 Epoch 36: Train loss = 0.0066, Val loss = 0.0093 Epoch 37: Train loss = 0.0066, Val loss = 0.0091 Epoch 38: Train loss = 0.0066, Val loss = 0.0089 Epoch 39: Train loss = 0.0066, Val loss = 0.0087 Epoch 40: Train loss = 0.0066, Val loss = 0.0088 Epoch 41: Train loss = 0.0065, Val loss = 0.0090 Epoch 42: Train loss = 0.0065, Val loss = 0.0091 Epoch 43: Train loss = 0.0065, Val loss = 0.0091 Epoch 44: Train loss = 0.0065, Val loss = 0.0090 Epoch 45: Train loss = 0.0065, Val loss = 0.0090 Epoch 46: Train loss = 0.0065, Val loss = 0.0090 Epoch 47: Train loss = 0.0065, Val loss = 0.0090 Epoch 48: Train loss = 0.0065, Val loss = 0.0091 Epoch 49: Train loss = 0.0065, Val loss = 0.0092 Epoch 50: Train loss = 0.0064, Val loss = 0.0092 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0440, Val loss = 0.0204 Epoch 2: Train loss = 0.0305, Val loss = 0.0120 Epoch 3: Train loss = 0.0164, Val loss = 0.0203 Epoch 4: Train loss = 0.0095, Val loss = 0.0068 Epoch 5: Train loss = 0.0085, Val loss = 0.0071 Epoch 6: Train loss = 0.0080, Val loss = 0.0115 Epoch 7: Train loss = 0.0075, Val loss = 0.0090 Epoch 8: Train loss = 0.0069, Val loss = 0.0067 Epoch 9: Train loss = 0.0068, Val loss = 0.0094 Epoch 10: Train loss = 0.0067, Val loss = 0.0097 Epoch 11: Train loss = 0.0066, Val loss = 0.0076 Epoch 12: Train loss = 0.0066, Val loss = 0.0101 Epoch 13: Train loss = 0.0066, Val loss = 0.0096 Epoch 14: Train loss = 0.0065, Val loss = 0.0095 Epoch 15: Train loss = 0.0064, Val loss = 0.0098 Epoch 16: Train loss = 0.0064, Val loss = 0.0103 Epoch 17: Train loss = 0.0064, Val loss = 0.0100 Epoch 18: Train loss = 0.0064, Val loss = 0.0107 Epoch 19: Train loss = 0.0064, Val loss = 0.0104 Epoch 20: Train loss = 0.0064, Val loss = 0.0099 Epoch 21: Train loss = 0.0063, Val loss = 0.0112 Epoch 22: Train loss = 0.0063, Val loss = 0.0101 Epoch 23: Train loss = 0.0063, Val loss = 0.0108 Epoch 24: Train loss = 0.0063, Val loss = 0.0111 Epoch 25: Train loss = 0.0063, Val loss = 0.0106 Epoch 26: Train loss = 0.0063, Val loss = 0.0103 Epoch 27: Train loss = 0.0062, Val loss = 0.0111 Epoch 28: Train loss = 0.0062, Val loss = 0.0103 Epoch 29: Train loss = 0.0062, Val loss = 0.0108 Epoch 30: Train loss = 0.0062, Val loss = 0.0106 Epoch 31: Train loss = 0.0061, Val loss = 0.0113 Epoch 32: Train loss = 0.0061, Val loss = 0.0095 Epoch 33: Train loss = 0.0061, Val loss = 0.0113 Epoch 34: Train loss = 0.0061, Val loss = 0.0097 Epoch 35: Train loss = 0.0061, Val loss = 0.0108 Epoch 36: Train loss = 0.0060, Val loss = 0.0089 Epoch 37: Train loss = 0.0061, Val loss = 0.0110 Epoch 38: Train loss = 0.0060, Val loss = 0.0082 Epoch 39: Train loss = 0.0060, Val loss = 0.0105 Epoch 40: Train loss = 0.0060, Val loss = 0.0095 Epoch 41: Train loss = 0.0059, Val loss = 0.0091 Epoch 42: Train loss = 0.0059, Val loss = 0.0109 Epoch 43: Train loss = 0.0059, Val loss = 0.0088 Epoch 44: Train loss = 0.0059, Val loss = 0.0089 Epoch 45: Train loss = 0.0058, Val loss = 0.0104 Epoch 46: Train loss = 0.0058, Val loss = 0.0085 Epoch 47: Train loss = 0.0058, Val loss = 0.0079 Epoch 48: Train loss = 0.0058, Val loss = 0.0097 Epoch 49: Train loss = 0.0058, Val loss = 0.0089 Epoch 50: Train loss = 0.0057, Val loss = 0.0063 Epoch 51: Train loss = 0.0058, Val loss = 0.0084 Epoch 52: Train loss = 0.0057, Val loss = 0.0096 Epoch 53: Train loss = 0.0057, Val loss = 0.0067 Epoch 54: Train loss = 0.0056, Val loss = 0.0058 Epoch 55: Train loss = 0.0058, Val loss = 0.0071 Epoch 56: Train loss = 0.0057, Val loss = 0.0098 Epoch 57: Train loss = 0.0057, Val loss = 0.0065 Epoch 58: Train loss = 0.0056, Val loss = 0.0057 Epoch 59: Train loss = 0.0056, Val loss = 0.0096 Epoch 60: Train loss = 0.0057, Val loss = 0.0072 Epoch 61: Train loss = 0.0056, Val loss = 0.0056 Epoch 62: Train loss = 0.0056, Val loss = 0.0075 Epoch 63: Train loss = 0.0055, Val loss = 0.0070 Epoch 64: Train loss = 0.0054, Val loss = 0.0061 Epoch 65: Train loss = 0.0054, Val loss = 0.0063 Epoch 66: Train loss = 0.0054, Val loss = 0.0074 Epoch 67: Train loss = 0.0053, Val loss = 0.0060 Epoch 68: Train loss = 0.0053, Val loss = 0.0060 Epoch 69: Train loss = 0.0053, Val loss = 0.0063 Epoch 70: Train loss = 0.0053, Val loss = 0.0067 Epoch 71: Train loss = 0.0053, Val loss = 0.0073 Early stopping triggered at epoch 71. Using device: mps Epoch 1: Train loss = 0.0567, Val loss = 0.0377 Epoch 2: Train loss = 0.0551, Val loss = 0.0360 Epoch 3: Train loss = 0.0537, Val loss = 0.0345 Epoch 4: Train loss = 0.0523, Val loss = 0.0332 Epoch 5: Train loss = 0.0509, Val loss = 0.0319 Epoch 6: Train loss = 0.0496, Val loss = 0.0307 Epoch 7: Train loss = 0.0484, Val loss = 0.0294 Epoch 8: Train loss = 0.0468, Val loss = 0.0283 Epoch 9: Train loss = 0.0453, Val loss = 0.0272 Epoch 10: Train loss = 0.0442, Val loss = 0.0263 Epoch 11: Train loss = 0.0429, Val loss = 0.0254 Epoch 12: Train loss = 0.0417, Val loss = 0.0247 Epoch 13: Train loss = 0.0407, Val loss = 0.0240 Epoch 14: Train loss = 0.0396, Val loss = 0.0233 Epoch 15: Train loss = 0.0386, Val loss = 0.0226 Epoch 16: Train loss = 0.0377, Val loss = 0.0219 Epoch 17: Train loss = 0.0367, Val loss = 0.0212 Epoch 18: Train loss = 0.0356, Val loss = 0.0205 Epoch 19: Train loss = 0.0347, Val loss = 0.0197 Epoch 20: Train loss = 0.0339, Val loss = 0.0189 Epoch 21: Train loss = 0.0331, Val loss = 0.0182 Epoch 22: Train loss = 0.0320, Val loss = 0.0174 Epoch 23: Train loss = 0.0310, Val loss = 0.0167 Epoch 24: Train loss = 0.0302, Val loss = 0.0160 Epoch 25: Train loss = 0.0296, Val loss = 0.0153 Epoch 26: Train loss = 0.0286, Val loss = 0.0148 Epoch 27: Train loss = 0.0277, Val loss = 0.0142 Epoch 28: Train loss = 0.0271, Val loss = 0.0136 Epoch 29: Train loss = 0.0263, Val loss = 0.0131 Epoch 30: Train loss = 0.0256, Val loss = 0.0126 Epoch 31: Train loss = 0.0249, Val loss = 0.0121 Epoch 32: Train loss = 0.0242, Val loss = 0.0116 Epoch 33: Train loss = 0.0236, Val loss = 0.0111 Epoch 34: Train loss = 0.0229, Val loss = 0.0107 Epoch 35: Train loss = 0.0226, Val loss = 0.0102 Epoch 36: Train loss = 0.0219, Val loss = 0.0098 Epoch 37: Train loss = 0.0211, Val loss = 0.0095 Epoch 38: Train loss = 0.0208, Val loss = 0.0092 Epoch 39: Train loss = 0.0204, Val loss = 0.0089 Epoch 40: Train loss = 0.0202, Val loss = 0.0087 Epoch 41: Train loss = 0.0198, Val loss = 0.0084 Epoch 42: Train loss = 0.0192, Val loss = 0.0082 Epoch 43: Train loss = 0.0190, Val loss = 0.0080 Epoch 44: Train loss = 0.0185, Val loss = 0.0079 Epoch 45: Train loss = 0.0185, Val loss = 0.0077 Epoch 46: Train loss = 0.0178, Val loss = 0.0075 Epoch 47: Train loss = 0.0174, Val loss = 0.0073 Epoch 48: Train loss = 0.0172, Val loss = 0.0072 Epoch 49: Train loss = 0.0172, Val loss = 0.0070 Epoch 50: Train loss = 0.0168, Val loss = 0.0069 Epoch 51: Train loss = 0.0165, Val loss = 0.0068 Epoch 52: Train loss = 0.0163, Val loss = 0.0067 Epoch 53: Train loss = 0.0162, Val loss = 0.0066 Epoch 54: Train loss = 0.0159, Val loss = 0.0065 Epoch 55: Train loss = 0.0158, Val loss = 0.0064 Epoch 56: Train loss = 0.0157, Val loss = 0.0062 Epoch 57: Train loss = 0.0152, Val loss = 0.0061 Epoch 58: Train loss = 0.0154, Val loss = 0.0061 Epoch 59: Train loss = 0.0152, Val loss = 0.0060 Epoch 60: Train loss = 0.0151, Val loss = 0.0060 Epoch 61: Train loss = 0.0145, Val loss = 0.0060 Epoch 62: Train loss = 0.0145, Val loss = 0.0060 Epoch 63: Train loss = 0.0144, Val loss = 0.0060 Epoch 64: Train loss = 0.0144, Val loss = 0.0060 Epoch 65: Train loss = 0.0143, Val loss = 0.0059 Epoch 66: Train loss = 0.0137, Val loss = 0.0059 Epoch 67: Train loss = 0.0138, Val loss = 0.0058 Epoch 68: Train loss = 0.0137, Val loss = 0.0058 Epoch 69: Train loss = 0.0137, Val loss = 0.0059 Epoch 70: Train loss = 0.0136, Val loss = 0.0059 Epoch 71: Train loss = 0.0136, Val loss = 0.0059 Epoch 72: Train loss = 0.0136, Val loss = 0.0059 Epoch 73: Train loss = 0.0134, Val loss = 0.0059 Epoch 74: Train loss = 0.0129, Val loss = 0.0058 Epoch 75: Train loss = 0.0130, Val loss = 0.0057 Epoch 76: Train loss = 0.0132, Val loss = 0.0057 Epoch 77: Train loss = 0.0130, Val loss = 0.0058 Epoch 78: Train loss = 0.0128, Val loss = 0.0058 Epoch 79: Train loss = 0.0125, Val loss = 0.0059 Epoch 80: Train loss = 0.0125, Val loss = 0.0059 Epoch 81: Train loss = 0.0127, Val loss = 0.0059 Epoch 82: Train loss = 0.0123, Val loss = 0.0059 Epoch 83: Train loss = 0.0123, Val loss = 0.0058 Epoch 84: Train loss = 0.0122, Val loss = 0.0058 Epoch 85: Train loss = 0.0123, Val loss = 0.0058 Early stopping triggered at epoch 85. Using device: mps Epoch 1: Train loss = 0.0679, Val loss = 0.0392 Epoch 2: Train loss = 0.0570, Val loss = 0.0305 Epoch 3: Train loss = 0.0466, Val loss = 0.0222 Epoch 4: Train loss = 0.0375, Val loss = 0.0180 Epoch 5: Train loss = 0.0315, Val loss = 0.0154 Epoch 6: Train loss = 0.0265, Val loss = 0.0112 Epoch 7: Train loss = 0.0221, Val loss = 0.0078 Epoch 8: Train loss = 0.0195, Val loss = 0.0059 Epoch 9: Train loss = 0.0172, Val loss = 0.0052 Epoch 10: Train loss = 0.0156, Val loss = 0.0053 Epoch 11: Train loss = 0.0144, Val loss = 0.0060 Epoch 12: Train loss = 0.0132, Val loss = 0.0060 Epoch 13: Train loss = 0.0127, Val loss = 0.0054 Epoch 14: Train loss = 0.0123, Val loss = 0.0052 Epoch 15: Train loss = 0.0121, Val loss = 0.0057 Epoch 16: Train loss = 0.0115, Val loss = 0.0063 Epoch 17: Train loss = 0.0111, Val loss = 0.0060 Epoch 18: Train loss = 0.0107, Val loss = 0.0059 Epoch 19: Train loss = 0.0106, Val loss = 0.0065 Epoch 20: Train loss = 0.0104, Val loss = 0.0063 Epoch 21: Train loss = 0.0100, Val loss = 0.0061 Epoch 22: Train loss = 0.0100, Val loss = 0.0065 Epoch 23: Train loss = 0.0098, Val loss = 0.0064 Epoch 24: Train loss = 0.0096, Val loss = 0.0061 Epoch 25: Train loss = 0.0094, Val loss = 0.0064 Epoch 26: Train loss = 0.0096, Val loss = 0.0063 Epoch 27: Train loss = 0.0092, Val loss = 0.0066 Epoch 28: Train loss = 0.0092, Val loss = 0.0065 Epoch 29: Train loss = 0.0091, Val loss = 0.0063 Epoch 30: Train loss = 0.0090, Val loss = 0.0072 Epoch 31: Train loss = 0.0089, Val loss = 0.0060 Epoch 32: Train loss = 0.0087, Val loss = 0.0069 Epoch 33: Train loss = 0.0089, Val loss = 0.0060 Epoch 34: Train loss = 0.0085, Val loss = 0.0064 Epoch 35: Train loss = 0.0084, Val loss = 0.0062 Epoch 36: Train loss = 0.0085, Val loss = 0.0067 Epoch 37: Train loss = 0.0083, Val loss = 0.0062 Epoch 38: Train loss = 0.0082, Val loss = 0.0070 Epoch 39: Train loss = 0.0081, Val loss = 0.0065 Epoch 40: Train loss = 0.0082, Val loss = 0.0063 Epoch 41: Train loss = 0.0080, Val loss = 0.0069 Epoch 42: Train loss = 0.0079, Val loss = 0.0064 Epoch 43: Train loss = 0.0078, Val loss = 0.0070 Epoch 44: Train loss = 0.0079, Val loss = 0.0061 Epoch 45: Train loss = 0.0077, Val loss = 0.0057 Epoch 46: Train loss = 0.0078, Val loss = 0.0065 Epoch 47: Train loss = 0.0076, Val loss = 0.0050 Epoch 48: Train loss = 0.0076, Val loss = 0.0067 Epoch 49: Train loss = 0.0077, Val loss = 0.0075 Epoch 50: Train loss = 0.0075, Val loss = 0.0051 Epoch 51: Train loss = 0.0074, Val loss = 0.0059 Epoch 52: Train loss = 0.0074, Val loss = 0.0065 Epoch 53: Train loss = 0.0074, Val loss = 0.0061 Epoch 54: Train loss = 0.0072, Val loss = 0.0047 Epoch 55: Train loss = 0.0073, Val loss = 0.0058 Epoch 56: Train loss = 0.0073, Val loss = 0.0073 Epoch 57: Train loss = 0.0073, Val loss = 0.0049 Epoch 58: Train loss = 0.0073, Val loss = 0.0053 Epoch 59: Train loss = 0.0071, Val loss = 0.0055 Epoch 60: Train loss = 0.0071, Val loss = 0.0050 Epoch 61: Train loss = 0.0070, Val loss = 0.0054 Epoch 62: Train loss = 0.0071, Val loss = 0.0067 Epoch 63: Train loss = 0.0070, Val loss = 0.0045 Epoch 64: Train loss = 0.0070, Val loss = 0.0041 Epoch 65: Train loss = 0.0070, Val loss = 0.0059 Epoch 66: Train loss = 0.0070, Val loss = 0.0062 Epoch 67: Train loss = 0.0070, Val loss = 0.0040 Epoch 68: Train loss = 0.0069, Val loss = 0.0044 Epoch 69: Train loss = 0.0069, Val loss = 0.0075 Epoch 70: Train loss = 0.0067, Val loss = 0.0045 Epoch 71: Train loss = 0.0068, Val loss = 0.0040 Epoch 72: Train loss = 0.0069, Val loss = 0.0065 Epoch 73: Train loss = 0.0068, Val loss = 0.0047 Epoch 74: Train loss = 0.0067, Val loss = 0.0050 Epoch 75: Train loss = 0.0067, Val loss = 0.0049 Epoch 76: Train loss = 0.0067, Val loss = 0.0055 Epoch 77: Train loss = 0.0067, Val loss = 0.0054 Early stopping triggered at epoch 77. Using device: mps Epoch 1: Train loss = 0.0470, Val loss = 0.0291 Epoch 2: Train loss = 0.0452, Val loss = 0.0272 Epoch 3: Train loss = 0.0433, Val loss = 0.0253 Epoch 4: Train loss = 0.0413, Val loss = 0.0235 Epoch 5: Train loss = 0.0393, Val loss = 0.0219 Epoch 6: Train loss = 0.0377, Val loss = 0.0205 Epoch 7: Train loss = 0.0360, Val loss = 0.0193 Epoch 8: Train loss = 0.0344, Val loss = 0.0182 Epoch 9: Train loss = 0.0330, Val loss = 0.0172 Epoch 10: Train loss = 0.0318, Val loss = 0.0164 Epoch 11: Train loss = 0.0305, Val loss = 0.0157 Epoch 12: Train loss = 0.0295, Val loss = 0.0151 Epoch 13: Train loss = 0.0285, Val loss = 0.0146 Epoch 14: Train loss = 0.0275, Val loss = 0.0142 Epoch 15: Train loss = 0.0267, Val loss = 0.0138 Epoch 16: Train loss = 0.0262, Val loss = 0.0135 Epoch 17: Train loss = 0.0253, Val loss = 0.0131 Epoch 18: Train loss = 0.0246, Val loss = 0.0128 Epoch 19: Train loss = 0.0238, Val loss = 0.0124 Epoch 20: Train loss = 0.0233, Val loss = 0.0120 Epoch 21: Train loss = 0.0229, Val loss = 0.0116 Epoch 22: Train loss = 0.0222, Val loss = 0.0113 Epoch 23: Train loss = 0.0218, Val loss = 0.0110 Epoch 24: Train loss = 0.0213, Val loss = 0.0108 Epoch 25: Train loss = 0.0207, Val loss = 0.0107 Epoch 26: Train loss = 0.0202, Val loss = 0.0105 Epoch 27: Train loss = 0.0198, Val loss = 0.0103 Epoch 28: Train loss = 0.0192, Val loss = 0.0102 Epoch 29: Train loss = 0.0189, Val loss = 0.0100 Epoch 30: Train loss = 0.0188, Val loss = 0.0098 Epoch 31: Train loss = 0.0182, Val loss = 0.0096 Epoch 32: Train loss = 0.0182, Val loss = 0.0094 Epoch 33: Train loss = 0.0178, Val loss = 0.0092 Epoch 34: Train loss = 0.0175, Val loss = 0.0090 Epoch 35: Train loss = 0.0172, Val loss = 0.0089 Epoch 36: Train loss = 0.0169, Val loss = 0.0087 Epoch 37: Train loss = 0.0166, Val loss = 0.0085 Epoch 38: Train loss = 0.0162, Val loss = 0.0084 Epoch 39: Train loss = 0.0160, Val loss = 0.0082 Epoch 40: Train loss = 0.0157, Val loss = 0.0081 Epoch 41: Train loss = 0.0155, Val loss = 0.0080 Epoch 42: Train loss = 0.0153, Val loss = 0.0078 Epoch 43: Train loss = 0.0151, Val loss = 0.0077 Epoch 44: Train loss = 0.0148, Val loss = 0.0076 Epoch 45: Train loss = 0.0148, Val loss = 0.0075 Epoch 46: Train loss = 0.0145, Val loss = 0.0074 Epoch 47: Train loss = 0.0143, Val loss = 0.0073 Epoch 48: Train loss = 0.0143, Val loss = 0.0072 Epoch 49: Train loss = 0.0140, Val loss = 0.0071 Epoch 50: Train loss = 0.0139, Val loss = 0.0071 Epoch 51: Train loss = 0.0138, Val loss = 0.0070 Epoch 52: Train loss = 0.0136, Val loss = 0.0069 Epoch 53: Train loss = 0.0136, Val loss = 0.0068 Epoch 54: Train loss = 0.0131, Val loss = 0.0067 Epoch 55: Train loss = 0.0131, Val loss = 0.0066 Epoch 56: Train loss = 0.0133, Val loss = 0.0066 Epoch 57: Train loss = 0.0130, Val loss = 0.0065 Epoch 58: Train loss = 0.0128, Val loss = 0.0065 Epoch 59: Train loss = 0.0127, Val loss = 0.0065 Epoch 60: Train loss = 0.0125, Val loss = 0.0064 Epoch 61: Train loss = 0.0124, Val loss = 0.0064 Epoch 62: Train loss = 0.0126, Val loss = 0.0064 Epoch 63: Train loss = 0.0123, Val loss = 0.0064 Epoch 64: Train loss = 0.0121, Val loss = 0.0063 Epoch 65: Train loss = 0.0120, Val loss = 0.0063 Epoch 66: Train loss = 0.0120, Val loss = 0.0063 Epoch 67: Train loss = 0.0119, Val loss = 0.0062 Epoch 68: Train loss = 0.0120, Val loss = 0.0062 Epoch 69: Train loss = 0.0119, Val loss = 0.0062 Epoch 70: Train loss = 0.0117, Val loss = 0.0062 Epoch 71: Train loss = 0.0116, Val loss = 0.0063 Epoch 72: Train loss = 0.0117, Val loss = 0.0062 Epoch 73: Train loss = 0.0115, Val loss = 0.0062 Epoch 74: Train loss = 0.0113, Val loss = 0.0062 Epoch 75: Train loss = 0.0113, Val loss = 0.0062 Epoch 76: Train loss = 0.0112, Val loss = 0.0062 Epoch 77: Train loss = 0.0114, Val loss = 0.0062 Early stopping triggered at epoch 77. Using device: mps Epoch 1: Train loss = 0.0575, Val loss = 0.0313 Epoch 2: Train loss = 0.0456, Val loss = 0.0236 Epoch 3: Train loss = 0.0372, Val loss = 0.0183 Epoch 4: Train loss = 0.0302, Val loss = 0.0140 Epoch 5: Train loss = 0.0248, Val loss = 0.0112 Epoch 6: Train loss = 0.0212, Val loss = 0.0090 Epoch 7: Train loss = 0.0181, Val loss = 0.0070 Epoch 8: Train loss = 0.0158, Val loss = 0.0059 Epoch 9: Train loss = 0.0143, Val loss = 0.0055 Epoch 10: Train loss = 0.0133, Val loss = 0.0052 Epoch 11: Train loss = 0.0126, Val loss = 0.0050 Epoch 12: Train loss = 0.0118, Val loss = 0.0052 Epoch 13: Train loss = 0.0116, Val loss = 0.0054 Epoch 14: Train loss = 0.0110, Val loss = 0.0054 Epoch 15: Train loss = 0.0108, Val loss = 0.0056 Epoch 16: Train loss = 0.0104, Val loss = 0.0058 Epoch 17: Train loss = 0.0101, Val loss = 0.0056 Epoch 18: Train loss = 0.0100, Val loss = 0.0058 Epoch 19: Train loss = 0.0098, Val loss = 0.0061 Epoch 20: Train loss = 0.0097, Val loss = 0.0059 Epoch 21: Train loss = 0.0094, Val loss = 0.0061 Epoch 22: Train loss = 0.0093, Val loss = 0.0060 Epoch 23: Train loss = 0.0091, Val loss = 0.0060 Epoch 24: Train loss = 0.0091, Val loss = 0.0061 Epoch 25: Train loss = 0.0089, Val loss = 0.0058 Epoch 26: Train loss = 0.0087, Val loss = 0.0065 Epoch 27: Train loss = 0.0085, Val loss = 0.0057 Epoch 28: Train loss = 0.0086, Val loss = 0.0071 Epoch 29: Train loss = 0.0084, Val loss = 0.0059 Epoch 30: Train loss = 0.0083, Val loss = 0.0060 Epoch 31: Train loss = 0.0082, Val loss = 0.0069 Epoch 32: Train loss = 0.0082, Val loss = 0.0058 Epoch 33: Train loss = 0.0081, Val loss = 0.0068 Epoch 34: Train loss = 0.0081, Val loss = 0.0061 Epoch 35: Train loss = 0.0079, Val loss = 0.0056 Epoch 36: Train loss = 0.0080, Val loss = 0.0066 Epoch 37: Train loss = 0.0078, Val loss = 0.0061 Epoch 38: Train loss = 0.0078, Val loss = 0.0061 Epoch 39: Train loss = 0.0077, Val loss = 0.0063 Epoch 40: Train loss = 0.0075, Val loss = 0.0065 Epoch 41: Train loss = 0.0076, Val loss = 0.0058 Epoch 42: Train loss = 0.0075, Val loss = 0.0062 Epoch 43: Train loss = 0.0074, Val loss = 0.0061 Epoch 44: Train loss = 0.0075, Val loss = 0.0063 Epoch 45: Train loss = 0.0074, Val loss = 0.0056 Epoch 46: Train loss = 0.0072, Val loss = 0.0065 Epoch 47: Train loss = 0.0073, Val loss = 0.0055 Epoch 48: Train loss = 0.0073, Val loss = 0.0061 Epoch 49: Train loss = 0.0073, Val loss = 0.0053 Epoch 50: Train loss = 0.0072, Val loss = 0.0064 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0595, Val loss = 0.0399 Epoch 2: Train loss = 0.0591, Val loss = 0.0394 Epoch 3: Train loss = 0.0585, Val loss = 0.0389 Epoch 4: Train loss = 0.0578, Val loss = 0.0384 Epoch 5: Train loss = 0.0574, Val loss = 0.0380 Epoch 6: Train loss = 0.0569, Val loss = 0.0375 Epoch 7: Train loss = 0.0562, Val loss = 0.0370 Epoch 8: Train loss = 0.0559, Val loss = 0.0366 Epoch 9: Train loss = 0.0554, Val loss = 0.0361 Epoch 10: Train loss = 0.0547, Val loss = 0.0357 Epoch 11: Train loss = 0.0542, Val loss = 0.0352 Epoch 12: Train loss = 0.0537, Val loss = 0.0347 Epoch 13: Train loss = 0.0531, Val loss = 0.0342 Epoch 14: Train loss = 0.0526, Val loss = 0.0337 Epoch 15: Train loss = 0.0520, Val loss = 0.0331 Epoch 16: Train loss = 0.0514, Val loss = 0.0325 Epoch 17: Train loss = 0.0507, Val loss = 0.0319 Epoch 18: Train loss = 0.0498, Val loss = 0.0312 Epoch 19: Train loss = 0.0493, Val loss = 0.0305 Epoch 20: Train loss = 0.0484, Val loss = 0.0297 Epoch 21: Train loss = 0.0478, Val loss = 0.0289 Epoch 22: Train loss = 0.0469, Val loss = 0.0281 Epoch 23: Train loss = 0.0459, Val loss = 0.0273 Epoch 24: Train loss = 0.0450, Val loss = 0.0265 Epoch 25: Train loss = 0.0440, Val loss = 0.0256 Epoch 26: Train loss = 0.0429, Val loss = 0.0247 Epoch 27: Train loss = 0.0421, Val loss = 0.0238 Epoch 28: Train loss = 0.0410, Val loss = 0.0229 Epoch 29: Train loss = 0.0400, Val loss = 0.0221 Epoch 30: Train loss = 0.0389, Val loss = 0.0212 Epoch 31: Train loss = 0.0374, Val loss = 0.0204 Epoch 32: Train loss = 0.0364, Val loss = 0.0196 Epoch 33: Train loss = 0.0352, Val loss = 0.0189 Epoch 34: Train loss = 0.0341, Val loss = 0.0182 Epoch 35: Train loss = 0.0330, Val loss = 0.0175 Epoch 36: Train loss = 0.0318, Val loss = 0.0168 Epoch 37: Train loss = 0.0309, Val loss = 0.0161 Epoch 38: Train loss = 0.0297, Val loss = 0.0154 Epoch 39: Train loss = 0.0285, Val loss = 0.0148 Epoch 40: Train loss = 0.0276, Val loss = 0.0141 Epoch 41: Train loss = 0.0265, Val loss = 0.0135 Epoch 42: Train loss = 0.0255, Val loss = 0.0128 Epoch 43: Train loss = 0.0243, Val loss = 0.0122 Epoch 44: Train loss = 0.0236, Val loss = 0.0116 Epoch 45: Train loss = 0.0226, Val loss = 0.0110 Epoch 46: Train loss = 0.0217, Val loss = 0.0104 Epoch 47: Train loss = 0.0214, Val loss = 0.0099 Epoch 48: Train loss = 0.0204, Val loss = 0.0094 Epoch 49: Train loss = 0.0197, Val loss = 0.0090 Epoch 50: Train loss = 0.0192, Val loss = 0.0086 Epoch 51: Train loss = 0.0187, Val loss = 0.0082 Epoch 52: Train loss = 0.0179, Val loss = 0.0078 Epoch 53: Train loss = 0.0177, Val loss = 0.0075 Epoch 54: Train loss = 0.0171, Val loss = 0.0072 Epoch 55: Train loss = 0.0168, Val loss = 0.0069 Epoch 56: Train loss = 0.0165, Val loss = 0.0066 Epoch 57: Train loss = 0.0162, Val loss = 0.0064 Epoch 58: Train loss = 0.0159, Val loss = 0.0062 Epoch 59: Train loss = 0.0155, Val loss = 0.0060 Epoch 60: Train loss = 0.0154, Val loss = 0.0058 Epoch 61: Train loss = 0.0148, Val loss = 0.0057 Epoch 62: Train loss = 0.0148, Val loss = 0.0056 Epoch 63: Train loss = 0.0146, Val loss = 0.0055 Epoch 64: Train loss = 0.0147, Val loss = 0.0055 Epoch 65: Train loss = 0.0141, Val loss = 0.0055 Epoch 66: Train loss = 0.0141, Val loss = 0.0055 Epoch 67: Train loss = 0.0138, Val loss = 0.0054 Epoch 68: Train loss = 0.0140, Val loss = 0.0054 Epoch 69: Train loss = 0.0137, Val loss = 0.0053 Epoch 70: Train loss = 0.0136, Val loss = 0.0053 Epoch 71: Train loss = 0.0136, Val loss = 0.0052 Epoch 72: Train loss = 0.0134, Val loss = 0.0052 Epoch 73: Train loss = 0.0135, Val loss = 0.0051 Epoch 74: Train loss = 0.0131, Val loss = 0.0051 Epoch 75: Train loss = 0.0130, Val loss = 0.0052 Epoch 76: Train loss = 0.0131, Val loss = 0.0052 Epoch 77: Train loss = 0.0130, Val loss = 0.0052 Epoch 78: Train loss = 0.0125, Val loss = 0.0051 Epoch 79: Train loss = 0.0127, Val loss = 0.0051 Epoch 80: Train loss = 0.0128, Val loss = 0.0050 Epoch 81: Train loss = 0.0126, Val loss = 0.0049 Epoch 82: Train loss = 0.0124, Val loss = 0.0050 Epoch 83: Train loss = 0.0123, Val loss = 0.0049 Epoch 84: Train loss = 0.0125, Val loss = 0.0049 Epoch 85: Train loss = 0.0123, Val loss = 0.0050 Epoch 86: Train loss = 0.0125, Val loss = 0.0049 Epoch 87: Train loss = 0.0123, Val loss = 0.0049 Epoch 88: Train loss = 0.0120, Val loss = 0.0050 Epoch 89: Train loss = 0.0120, Val loss = 0.0050 Epoch 90: Train loss = 0.0121, Val loss = 0.0050 Early stopping triggered at epoch 90. Using device: mps Epoch 1: Train loss = 0.0543, Val loss = 0.0325 Epoch 2: Train loss = 0.0492, Val loss = 0.0282 Epoch 3: Train loss = 0.0438, Val loss = 0.0237 Epoch 4: Train loss = 0.0379, Val loss = 0.0197 Epoch 5: Train loss = 0.0320, Val loss = 0.0164 Epoch 6: Train loss = 0.0268, Val loss = 0.0129 Epoch 7: Train loss = 0.0222, Val loss = 0.0093 Epoch 8: Train loss = 0.0186, Val loss = 0.0072 Epoch 9: Train loss = 0.0160, Val loss = 0.0067 Epoch 10: Train loss = 0.0143, Val loss = 0.0062 Epoch 11: Train loss = 0.0129, Val loss = 0.0049 Epoch 12: Train loss = 0.0124, Val loss = 0.0048 Epoch 13: Train loss = 0.0114, Val loss = 0.0055 Epoch 14: Train loss = 0.0110, Val loss = 0.0050 Epoch 15: Train loss = 0.0108, Val loss = 0.0046 Epoch 16: Train loss = 0.0104, Val loss = 0.0053 Epoch 17: Train loss = 0.0103, Val loss = 0.0053 Epoch 18: Train loss = 0.0097, Val loss = 0.0051 Epoch 19: Train loss = 0.0096, Val loss = 0.0059 Epoch 20: Train loss = 0.0096, Val loss = 0.0052 Epoch 21: Train loss = 0.0094, Val loss = 0.0049 Epoch 22: Train loss = 0.0093, Val loss = 0.0053 Epoch 23: Train loss = 0.0093, Val loss = 0.0050 Epoch 24: Train loss = 0.0088, Val loss = 0.0052 Epoch 25: Train loss = 0.0087, Val loss = 0.0047 Epoch 26: Train loss = 0.0088, Val loss = 0.0049 Epoch 27: Train loss = 0.0087, Val loss = 0.0045 Epoch 28: Train loss = 0.0088, Val loss = 0.0045 Epoch 29: Train loss = 0.0087, Val loss = 0.0057 Epoch 30: Train loss = 0.0085, Val loss = 0.0041 Epoch 31: Train loss = 0.0085, Val loss = 0.0053 Epoch 32: Train loss = 0.0082, Val loss = 0.0047 Epoch 33: Train loss = 0.0082, Val loss = 0.0053 Epoch 34: Train loss = 0.0081, Val loss = 0.0050 Epoch 35: Train loss = 0.0081, Val loss = 0.0046 Epoch 36: Train loss = 0.0078, Val loss = 0.0055 Epoch 37: Train loss = 0.0078, Val loss = 0.0047 Epoch 38: Train loss = 0.0079, Val loss = 0.0051 Epoch 39: Train loss = 0.0079, Val loss = 0.0048 Epoch 40: Train loss = 0.0077, Val loss = 0.0047 Epoch 41: Train loss = 0.0077, Val loss = 0.0048 Epoch 42: Train loss = 0.0076, Val loss = 0.0046 Epoch 43: Train loss = 0.0076, Val loss = 0.0055 Epoch 44: Train loss = 0.0075, Val loss = 0.0046 Epoch 45: Train loss = 0.0074, Val loss = 0.0057 Epoch 46: Train loss = 0.0073, Val loss = 0.0047 Epoch 47: Train loss = 0.0073, Val loss = 0.0056 Epoch 48: Train loss = 0.0073, Val loss = 0.0048 Epoch 49: Train loss = 0.0072, Val loss = 0.0056 Epoch 50: Train loss = 0.0072, Val loss = 0.0048 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0579, Val loss = 0.0383 Epoch 2: Train loss = 0.0573, Val loss = 0.0378 Epoch 3: Train loss = 0.0567, Val loss = 0.0373 Epoch 4: Train loss = 0.0562, Val loss = 0.0367 Epoch 5: Train loss = 0.0555, Val loss = 0.0362 Epoch 6: Train loss = 0.0550, Val loss = 0.0356 Epoch 7: Train loss = 0.0543, Val loss = 0.0349 Epoch 8: Train loss = 0.0537, Val loss = 0.0343 Epoch 9: Train loss = 0.0532, Val loss = 0.0337 Epoch 10: Train loss = 0.0524, Val loss = 0.0332 Epoch 11: Train loss = 0.0519, Val loss = 0.0326 Epoch 12: Train loss = 0.0513, Val loss = 0.0320 Epoch 13: Train loss = 0.0505, Val loss = 0.0314 Epoch 14: Train loss = 0.0497, Val loss = 0.0307 Epoch 15: Train loss = 0.0490, Val loss = 0.0299 Epoch 16: Train loss = 0.0482, Val loss = 0.0291 Epoch 17: Train loss = 0.0472, Val loss = 0.0282 Epoch 18: Train loss = 0.0463, Val loss = 0.0273 Epoch 19: Train loss = 0.0453, Val loss = 0.0263 Epoch 20: Train loss = 0.0441, Val loss = 0.0253 Epoch 21: Train loss = 0.0431, Val loss = 0.0241 Epoch 22: Train loss = 0.0416, Val loss = 0.0229 Epoch 23: Train loss = 0.0403, Val loss = 0.0217 Epoch 24: Train loss = 0.0387, Val loss = 0.0204 Epoch 25: Train loss = 0.0374, Val loss = 0.0192 Epoch 26: Train loss = 0.0358, Val loss = 0.0180 Epoch 27: Train loss = 0.0345, Val loss = 0.0168 Epoch 28: Train loss = 0.0328, Val loss = 0.0158 Epoch 29: Train loss = 0.0313, Val loss = 0.0149 Epoch 30: Train loss = 0.0299, Val loss = 0.0140 Epoch 31: Train loss = 0.0284, Val loss = 0.0133 Epoch 32: Train loss = 0.0274, Val loss = 0.0128 Epoch 33: Train loss = 0.0258, Val loss = 0.0123 Epoch 34: Train loss = 0.0251, Val loss = 0.0119 Epoch 35: Train loss = 0.0240, Val loss = 0.0116 Epoch 36: Train loss = 0.0230, Val loss = 0.0112 Epoch 37: Train loss = 0.0222, Val loss = 0.0109 Epoch 38: Train loss = 0.0215, Val loss = 0.0105 Epoch 39: Train loss = 0.0208, Val loss = 0.0102 Epoch 40: Train loss = 0.0204, Val loss = 0.0098 Epoch 41: Train loss = 0.0197, Val loss = 0.0095 Epoch 42: Train loss = 0.0191, Val loss = 0.0092 Epoch 43: Train loss = 0.0186, Val loss = 0.0090 Epoch 44: Train loss = 0.0182, Val loss = 0.0087 Epoch 45: Train loss = 0.0178, Val loss = 0.0084 Epoch 46: Train loss = 0.0175, Val loss = 0.0082 Epoch 47: Train loss = 0.0171, Val loss = 0.0081 Epoch 48: Train loss = 0.0167, Val loss = 0.0079 Epoch 49: Train loss = 0.0165, Val loss = 0.0078 Epoch 50: Train loss = 0.0162, Val loss = 0.0076 Epoch 51: Train loss = 0.0158, Val loss = 0.0075 Epoch 52: Train loss = 0.0158, Val loss = 0.0074 Epoch 53: Train loss = 0.0155, Val loss = 0.0072 Epoch 54: Train loss = 0.0151, Val loss = 0.0071 Epoch 55: Train loss = 0.0147, Val loss = 0.0069 Epoch 56: Train loss = 0.0147, Val loss = 0.0068 Epoch 57: Train loss = 0.0145, Val loss = 0.0066 Epoch 58: Train loss = 0.0143, Val loss = 0.0065 Epoch 59: Train loss = 0.0141, Val loss = 0.0065 Epoch 60: Train loss = 0.0141, Val loss = 0.0064 Epoch 61: Train loss = 0.0137, Val loss = 0.0063 Epoch 62: Train loss = 0.0136, Val loss = 0.0063 Epoch 63: Train loss = 0.0135, Val loss = 0.0063 Epoch 64: Train loss = 0.0136, Val loss = 0.0063 Epoch 65: Train loss = 0.0132, Val loss = 0.0063 Epoch 66: Train loss = 0.0133, Val loss = 0.0063 Epoch 67: Train loss = 0.0129, Val loss = 0.0063 Epoch 68: Train loss = 0.0127, Val loss = 0.0062 Epoch 69: Train loss = 0.0127, Val loss = 0.0061 Epoch 70: Train loss = 0.0126, Val loss = 0.0061 Epoch 71: Train loss = 0.0125, Val loss = 0.0061 Epoch 72: Train loss = 0.0125, Val loss = 0.0060 Epoch 73: Train loss = 0.0122, Val loss = 0.0060 Epoch 74: Train loss = 0.0121, Val loss = 0.0060 Epoch 75: Train loss = 0.0121, Val loss = 0.0060 Epoch 76: Train loss = 0.0122, Val loss = 0.0059 Epoch 77: Train loss = 0.0119, Val loss = 0.0059 Epoch 78: Train loss = 0.0120, Val loss = 0.0060 Epoch 79: Train loss = 0.0120, Val loss = 0.0060 Epoch 80: Train loss = 0.0119, Val loss = 0.0060 Epoch 81: Train loss = 0.0120, Val loss = 0.0060 Epoch 82: Train loss = 0.0116, Val loss = 0.0059 Epoch 83: Train loss = 0.0116, Val loss = 0.0059 Epoch 84: Train loss = 0.0114, Val loss = 0.0059 Epoch 85: Train loss = 0.0112, Val loss = 0.0058 Epoch 86: Train loss = 0.0115, Val loss = 0.0058 Epoch 87: Train loss = 0.0112, Val loss = 0.0058 Epoch 88: Train loss = 0.0114, Val loss = 0.0058 Epoch 89: Train loss = 0.0112, Val loss = 0.0058 Epoch 90: Train loss = 0.0111, Val loss = 0.0059 Epoch 91: Train loss = 0.0111, Val loss = 0.0059 Epoch 92: Train loss = 0.0108, Val loss = 0.0059 Epoch 93: Train loss = 0.0112, Val loss = 0.0058 Epoch 94: Train loss = 0.0111, Val loss = 0.0057 Epoch 95: Train loss = 0.0109, Val loss = 0.0057 Early stopping triggered at epoch 95. Using device: mps Epoch 1: Train loss = 0.0582, Val loss = 0.0356 Epoch 2: Train loss = 0.0527, Val loss = 0.0310 Epoch 3: Train loss = 0.0470, Val loss = 0.0251 Epoch 4: Train loss = 0.0396, Val loss = 0.0191 Epoch 5: Train loss = 0.0317, Val loss = 0.0158 Epoch 6: Train loss = 0.0260, Val loss = 0.0167 Epoch 7: Train loss = 0.0220, Val loss = 0.0130 Epoch 8: Train loss = 0.0177, Val loss = 0.0079 Epoch 9: Train loss = 0.0149, Val loss = 0.0060 Epoch 10: Train loss = 0.0139, Val loss = 0.0058 Epoch 11: Train loss = 0.0130, Val loss = 0.0062 Epoch 12: Train loss = 0.0121, Val loss = 0.0063 Epoch 13: Train loss = 0.0118, Val loss = 0.0056 Epoch 14: Train loss = 0.0109, Val loss = 0.0051 Epoch 15: Train loss = 0.0108, Val loss = 0.0050 Epoch 16: Train loss = 0.0105, Val loss = 0.0053 Epoch 17: Train loss = 0.0101, Val loss = 0.0056 Epoch 18: Train loss = 0.0100, Val loss = 0.0052 Epoch 19: Train loss = 0.0098, Val loss = 0.0046 Epoch 20: Train loss = 0.0095, Val loss = 0.0050 Epoch 21: Train loss = 0.0094, Val loss = 0.0054 Epoch 22: Train loss = 0.0095, Val loss = 0.0050 Epoch 23: Train loss = 0.0091, Val loss = 0.0050 Epoch 24: Train loss = 0.0091, Val loss = 0.0051 Epoch 25: Train loss = 0.0091, Val loss = 0.0051 Epoch 26: Train loss = 0.0088, Val loss = 0.0051 Epoch 27: Train loss = 0.0088, Val loss = 0.0051 Epoch 28: Train loss = 0.0089, Val loss = 0.0049 Epoch 29: Train loss = 0.0087, Val loss = 0.0048 Epoch 30: Train loss = 0.0086, Val loss = 0.0052 Epoch 31: Train loss = 0.0085, Val loss = 0.0048 Epoch 32: Train loss = 0.0084, Val loss = 0.0046 Epoch 33: Train loss = 0.0083, Val loss = 0.0057 Epoch 34: Train loss = 0.0082, Val loss = 0.0050 Epoch 35: Train loss = 0.0083, Val loss = 0.0048 Epoch 36: Train loss = 0.0083, Val loss = 0.0058 Epoch 37: Train loss = 0.0080, Val loss = 0.0049 Epoch 38: Train loss = 0.0080, Val loss = 0.0053 Epoch 39: Train loss = 0.0079, Val loss = 0.0057 Epoch 40: Train loss = 0.0080, Val loss = 0.0050 Epoch 41: Train loss = 0.0080, Val loss = 0.0057 Epoch 42: Train loss = 0.0079, Val loss = 0.0054 Epoch 43: Train loss = 0.0079, Val loss = 0.0051 Epoch 44: Train loss = 0.0077, Val loss = 0.0059 Epoch 45: Train loss = 0.0076, Val loss = 0.0052 Epoch 46: Train loss = 0.0077, Val loss = 0.0063 Epoch 47: Train loss = 0.0076, Val loss = 0.0053 Epoch 48: Train loss = 0.0077, Val loss = 0.0054 Epoch 49: Train loss = 0.0075, Val loss = 0.0058 Epoch 50: Train loss = 0.0074, Val loss = 0.0054 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0541, Val loss = 0.0316 Epoch 2: Train loss = 0.0498, Val loss = 0.0275 Epoch 3: Train loss = 0.0456, Val loss = 0.0235 Epoch 4: Train loss = 0.0411, Val loss = 0.0197 Epoch 5: Train loss = 0.0367, Val loss = 0.0160 Epoch 6: Train loss = 0.0320, Val loss = 0.0128 Epoch 7: Train loss = 0.0275, Val loss = 0.0101 Epoch 8: Train loss = 0.0232, Val loss = 0.0085 Epoch 9: Train loss = 0.0194, Val loss = 0.0081 Epoch 10: Train loss = 0.0165, Val loss = 0.0090 Epoch 11: Train loss = 0.0145, Val loss = 0.0106 Epoch 12: Train loss = 0.0132, Val loss = 0.0122 Epoch 13: Train loss = 0.0124, Val loss = 0.0129 Epoch 14: Train loss = 0.0117, Val loss = 0.0124 Epoch 15: Train loss = 0.0110, Val loss = 0.0112 Epoch 16: Train loss = 0.0103, Val loss = 0.0098 Epoch 17: Train loss = 0.0101, Val loss = 0.0085 Epoch 18: Train loss = 0.0098, Val loss = 0.0077 Epoch 19: Train loss = 0.0097, Val loss = 0.0073 Epoch 20: Train loss = 0.0096, Val loss = 0.0073 Epoch 21: Train loss = 0.0094, Val loss = 0.0075 Epoch 22: Train loss = 0.0093, Val loss = 0.0078 Epoch 23: Train loss = 0.0092, Val loss = 0.0080 Epoch 24: Train loss = 0.0090, Val loss = 0.0082 Epoch 25: Train loss = 0.0090, Val loss = 0.0083 Epoch 26: Train loss = 0.0089, Val loss = 0.0082 Epoch 27: Train loss = 0.0088, Val loss = 0.0081 Epoch 28: Train loss = 0.0088, Val loss = 0.0080 Epoch 29: Train loss = 0.0087, Val loss = 0.0080 Epoch 30: Train loss = 0.0087, Val loss = 0.0079 Epoch 31: Train loss = 0.0085, Val loss = 0.0079 Epoch 32: Train loss = 0.0085, Val loss = 0.0080 Epoch 33: Train loss = 0.0085, Val loss = 0.0081 Epoch 34: Train loss = 0.0083, Val loss = 0.0082 Epoch 35: Train loss = 0.0084, Val loss = 0.0082 Epoch 36: Train loss = 0.0084, Val loss = 0.0081 Epoch 37: Train loss = 0.0082, Val loss = 0.0080 Epoch 38: Train loss = 0.0083, Val loss = 0.0079 Epoch 39: Train loss = 0.0082, Val loss = 0.0081 Epoch 40: Train loss = 0.0082, Val loss = 0.0082 Epoch 41: Train loss = 0.0082, Val loss = 0.0083 Epoch 42: Train loss = 0.0082, Val loss = 0.0084 Epoch 43: Train loss = 0.0081, Val loss = 0.0082 Epoch 44: Train loss = 0.0082, Val loss = 0.0083 Epoch 45: Train loss = 0.0081, Val loss = 0.0085 Epoch 46: Train loss = 0.0080, Val loss = 0.0084 Epoch 47: Train loss = 0.0080, Val loss = 0.0082 Epoch 48: Train loss = 0.0080, Val loss = 0.0082 Epoch 49: Train loss = 0.0079, Val loss = 0.0082 Epoch 50: Train loss = 0.0078, Val loss = 0.0084 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0405, Val loss = 0.0092 Epoch 2: Train loss = 0.0177, Val loss = 0.0223 Epoch 3: Train loss = 0.0119, Val loss = 0.0064 Epoch 4: Train loss = 0.0108, Val loss = 0.0047 Epoch 5: Train loss = 0.0106, Val loss = 0.0068 Epoch 6: Train loss = 0.0091, Val loss = 0.0102 Epoch 7: Train loss = 0.0086, Val loss = 0.0064 Epoch 8: Train loss = 0.0082, Val loss = 0.0053 Epoch 9: Train loss = 0.0083, Val loss = 0.0078 Epoch 10: Train loss = 0.0081, Val loss = 0.0089 Epoch 11: Train loss = 0.0078, Val loss = 0.0069 Epoch 12: Train loss = 0.0076, Val loss = 0.0077 Epoch 13: Train loss = 0.0077, Val loss = 0.0091 Epoch 14: Train loss = 0.0075, Val loss = 0.0084 Epoch 15: Train loss = 0.0074, Val loss = 0.0082 Epoch 16: Train loss = 0.0073, Val loss = 0.0091 Epoch 17: Train loss = 0.0072, Val loss = 0.0085 Epoch 18: Train loss = 0.0072, Val loss = 0.0083 Epoch 19: Train loss = 0.0071, Val loss = 0.0086 Epoch 20: Train loss = 0.0070, Val loss = 0.0082 Epoch 21: Train loss = 0.0071, Val loss = 0.0084 Epoch 22: Train loss = 0.0070, Val loss = 0.0088 Epoch 23: Train loss = 0.0069, Val loss = 0.0072 Epoch 24: Train loss = 0.0069, Val loss = 0.0094 Epoch 25: Train loss = 0.0068, Val loss = 0.0079 Epoch 26: Train loss = 0.0068, Val loss = 0.0077 Epoch 27: Train loss = 0.0067, Val loss = 0.0090 Epoch 28: Train loss = 0.0066, Val loss = 0.0075 Epoch 29: Train loss = 0.0067, Val loss = 0.0086 Epoch 30: Train loss = 0.0066, Val loss = 0.0072 Epoch 31: Train loss = 0.0065, Val loss = 0.0080 Epoch 32: Train loss = 0.0064, Val loss = 0.0083 Epoch 33: Train loss = 0.0064, Val loss = 0.0082 Epoch 34: Train loss = 0.0064, Val loss = 0.0070 Epoch 35: Train loss = 0.0063, Val loss = 0.0086 Epoch 36: Train loss = 0.0064, Val loss = 0.0090 Epoch 37: Train loss = 0.0063, Val loss = 0.0073 Epoch 38: Train loss = 0.0062, Val loss = 0.0060 Epoch 39: Train loss = 0.0062, Val loss = 0.0055 Epoch 40: Train loss = 0.0062, Val loss = 0.0063 Epoch 41: Train loss = 0.0062, Val loss = 0.0072 Epoch 42: Train loss = 0.0060, Val loss = 0.0071 Epoch 43: Train loss = 0.0060, Val loss = 0.0063 Epoch 44: Train loss = 0.0060, Val loss = 0.0057 Epoch 45: Train loss = 0.0059, Val loss = 0.0051 Epoch 46: Train loss = 0.0061, Val loss = 0.0038 Epoch 47: Train loss = 0.0062, Val loss = 0.0071 Epoch 48: Train loss = 0.0060, Val loss = 0.0079 Epoch 49: Train loss = 0.0059, Val loss = 0.0055 Epoch 50: Train loss = 0.0058, Val loss = 0.0052 Epoch 51: Train loss = 0.0058, Val loss = 0.0062 Epoch 52: Train loss = 0.0057, Val loss = 0.0071 Epoch 53: Train loss = 0.0058, Val loss = 0.0061 Epoch 54: Train loss = 0.0055, Val loss = 0.0053 Epoch 55: Train loss = 0.0057, Val loss = 0.0058 Epoch 56: Train loss = 0.0057, Val loss = 0.0060 Early stopping triggered at epoch 56. Using device: mps Epoch 1: Train loss = 0.0472, Val loss = 0.0262 Epoch 2: Train loss = 0.0428, Val loss = 0.0222 Epoch 3: Train loss = 0.0385, Val loss = 0.0184 Epoch 4: Train loss = 0.0341, Val loss = 0.0147 Epoch 5: Train loss = 0.0295, Val loss = 0.0114 Epoch 6: Train loss = 0.0250, Val loss = 0.0087 Epoch 7: Train loss = 0.0209, Val loss = 0.0072 Epoch 8: Train loss = 0.0173, Val loss = 0.0074 Epoch 9: Train loss = 0.0147, Val loss = 0.0090 Epoch 10: Train loss = 0.0133, Val loss = 0.0111 Epoch 11: Train loss = 0.0126, Val loss = 0.0122 Epoch 12: Train loss = 0.0118, Val loss = 0.0120 Epoch 13: Train loss = 0.0110, Val loss = 0.0107 Epoch 14: Train loss = 0.0103, Val loss = 0.0091 Epoch 15: Train loss = 0.0099, Val loss = 0.0078 Epoch 16: Train loss = 0.0097, Val loss = 0.0069 Epoch 17: Train loss = 0.0094, Val loss = 0.0065 Epoch 18: Train loss = 0.0093, Val loss = 0.0066 Epoch 19: Train loss = 0.0093, Val loss = 0.0070 Epoch 20: Train loss = 0.0091, Val loss = 0.0075 Epoch 21: Train loss = 0.0090, Val loss = 0.0079 Epoch 22: Train loss = 0.0089, Val loss = 0.0080 Epoch 23: Train loss = 0.0088, Val loss = 0.0080 Epoch 24: Train loss = 0.0087, Val loss = 0.0077 Epoch 25: Train loss = 0.0086, Val loss = 0.0075 Epoch 26: Train loss = 0.0086, Val loss = 0.0073 Epoch 27: Train loss = 0.0085, Val loss = 0.0073 Epoch 28: Train loss = 0.0086, Val loss = 0.0075 Epoch 29: Train loss = 0.0084, Val loss = 0.0077 Epoch 30: Train loss = 0.0083, Val loss = 0.0079 Epoch 31: Train loss = 0.0082, Val loss = 0.0080 Epoch 32: Train loss = 0.0083, Val loss = 0.0080 Epoch 33: Train loss = 0.0081, Val loss = 0.0079 Epoch 34: Train loss = 0.0082, Val loss = 0.0079 Epoch 35: Train loss = 0.0082, Val loss = 0.0079 Epoch 36: Train loss = 0.0081, Val loss = 0.0080 Epoch 37: Train loss = 0.0080, Val loss = 0.0083 Epoch 38: Train loss = 0.0080, Val loss = 0.0084 Epoch 39: Train loss = 0.0079, Val loss = 0.0085 Epoch 40: Train loss = 0.0079, Val loss = 0.0085 Epoch 41: Train loss = 0.0080, Val loss = 0.0086 Epoch 42: Train loss = 0.0080, Val loss = 0.0086 Epoch 43: Train loss = 0.0079, Val loss = 0.0086 Epoch 44: Train loss = 0.0078, Val loss = 0.0085 Epoch 45: Train loss = 0.0077, Val loss = 0.0086 Epoch 46: Train loss = 0.0077, Val loss = 0.0087 Epoch 47: Train loss = 0.0078, Val loss = 0.0089 Epoch 48: Train loss = 0.0077, Val loss = 0.0090 Epoch 49: Train loss = 0.0077, Val loss = 0.0090 Epoch 50: Train loss = 0.0075, Val loss = 0.0090 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0385, Val loss = 0.0088 Epoch 2: Train loss = 0.0169, Val loss = 0.0212 Epoch 3: Train loss = 0.0113, Val loss = 0.0052 Epoch 4: Train loss = 0.0109, Val loss = 0.0052 Epoch 5: Train loss = 0.0100, Val loss = 0.0103 Epoch 6: Train loss = 0.0093, Val loss = 0.0106 Epoch 7: Train loss = 0.0085, Val loss = 0.0061 Epoch 8: Train loss = 0.0083, Val loss = 0.0060 Epoch 9: Train loss = 0.0080, Val loss = 0.0096 Epoch 10: Train loss = 0.0079, Val loss = 0.0086 Epoch 11: Train loss = 0.0078, Val loss = 0.0065 Epoch 12: Train loss = 0.0078, Val loss = 0.0083 Epoch 13: Train loss = 0.0076, Val loss = 0.0097 Epoch 14: Train loss = 0.0075, Val loss = 0.0079 Epoch 15: Train loss = 0.0073, Val loss = 0.0078 Epoch 16: Train loss = 0.0073, Val loss = 0.0096 Epoch 17: Train loss = 0.0073, Val loss = 0.0087 Epoch 18: Train loss = 0.0072, Val loss = 0.0081 Epoch 19: Train loss = 0.0072, Val loss = 0.0092 Epoch 20: Train loss = 0.0072, Val loss = 0.0089 Epoch 21: Train loss = 0.0072, Val loss = 0.0081 Epoch 22: Train loss = 0.0070, Val loss = 0.0088 Epoch 23: Train loss = 0.0069, Val loss = 0.0086 Epoch 24: Train loss = 0.0069, Val loss = 0.0082 Epoch 25: Train loss = 0.0068, Val loss = 0.0092 Epoch 26: Train loss = 0.0068, Val loss = 0.0080 Epoch 27: Train loss = 0.0067, Val loss = 0.0085 Epoch 28: Train loss = 0.0067, Val loss = 0.0081 Epoch 29: Train loss = 0.0065, Val loss = 0.0081 Epoch 30: Train loss = 0.0066, Val loss = 0.0086 Epoch 31: Train loss = 0.0066, Val loss = 0.0086 Epoch 32: Train loss = 0.0066, Val loss = 0.0068 Epoch 33: Train loss = 0.0065, Val loss = 0.0092 Epoch 34: Train loss = 0.0065, Val loss = 0.0093 Epoch 35: Train loss = 0.0064, Val loss = 0.0069 Epoch 36: Train loss = 0.0063, Val loss = 0.0082 Epoch 37: Train loss = 0.0063, Val loss = 0.0088 Epoch 38: Train loss = 0.0062, Val loss = 0.0077 Epoch 39: Train loss = 0.0062, Val loss = 0.0079 Epoch 40: Train loss = 0.0062, Val loss = 0.0085 Epoch 41: Train loss = 0.0061, Val loss = 0.0056 Epoch 42: Train loss = 0.0061, Val loss = 0.0058 Epoch 43: Train loss = 0.0062, Val loss = 0.0065 Epoch 44: Train loss = 0.0061, Val loss = 0.0090 Epoch 45: Train loss = 0.0062, Val loss = 0.0085 Epoch 46: Train loss = 0.0061, Val loss = 0.0062 Epoch 47: Train loss = 0.0060, Val loss = 0.0051 Epoch 48: Train loss = 0.0060, Val loss = 0.0045 Epoch 49: Train loss = 0.0060, Val loss = 0.0056 Epoch 50: Train loss = 0.0058, Val loss = 0.0060 Epoch 51: Train loss = 0.0058, Val loss = 0.0058 Epoch 52: Train loss = 0.0058, Val loss = 0.0054 Epoch 53: Train loss = 0.0058, Val loss = 0.0066 Epoch 54: Train loss = 0.0058, Val loss = 0.0051 Epoch 55: Train loss = 0.0058, Val loss = 0.0054 Epoch 56: Train loss = 0.0057, Val loss = 0.0054 Epoch 57: Train loss = 0.0056, Val loss = 0.0051 Epoch 58: Train loss = 0.0057, Val loss = 0.0046 Early stopping triggered at epoch 58. Using device: mps Epoch 1: Train loss = 0.0496, Val loss = 0.0306 Epoch 2: Train loss = 0.0484, Val loss = 0.0295 Epoch 3: Train loss = 0.0470, Val loss = 0.0282 Epoch 4: Train loss = 0.0456, Val loss = 0.0268 Epoch 5: Train loss = 0.0441, Val loss = 0.0253 Epoch 6: Train loss = 0.0423, Val loss = 0.0237 Epoch 7: Train loss = 0.0404, Val loss = 0.0218 Epoch 8: Train loss = 0.0383, Val loss = 0.0197 Epoch 9: Train loss = 0.0357, Val loss = 0.0175 Epoch 10: Train loss = 0.0327, Val loss = 0.0151 Epoch 11: Train loss = 0.0295, Val loss = 0.0125 Epoch 12: Train loss = 0.0259, Val loss = 0.0102 Epoch 13: Train loss = 0.0220, Val loss = 0.0086 Epoch 14: Train loss = 0.0186, Val loss = 0.0083 Epoch 15: Train loss = 0.0160, Val loss = 0.0098 Epoch 16: Train loss = 0.0144, Val loss = 0.0118 Epoch 17: Train loss = 0.0134, Val loss = 0.0127 Epoch 18: Train loss = 0.0125, Val loss = 0.0114 Epoch 19: Train loss = 0.0113, Val loss = 0.0093 Epoch 20: Train loss = 0.0104, Val loss = 0.0074 Epoch 21: Train loss = 0.0098, Val loss = 0.0062 Epoch 22: Train loss = 0.0095, Val loss = 0.0058 Epoch 23: Train loss = 0.0093, Val loss = 0.0058 Epoch 24: Train loss = 0.0093, Val loss = 0.0063 Epoch 25: Train loss = 0.0092, Val loss = 0.0068 Epoch 26: Train loss = 0.0090, Val loss = 0.0073 Epoch 27: Train loss = 0.0091, Val loss = 0.0074 Epoch 28: Train loss = 0.0090, Val loss = 0.0073 Epoch 29: Train loss = 0.0089, Val loss = 0.0070 Epoch 30: Train loss = 0.0087, Val loss = 0.0067 Epoch 31: Train loss = 0.0086, Val loss = 0.0066 Epoch 32: Train loss = 0.0086, Val loss = 0.0066 Epoch 33: Train loss = 0.0085, Val loss = 0.0067 Epoch 34: Train loss = 0.0084, Val loss = 0.0069 Epoch 35: Train loss = 0.0084, Val loss = 0.0070 Epoch 36: Train loss = 0.0084, Val loss = 0.0069 Epoch 37: Train loss = 0.0082, Val loss = 0.0068 Epoch 38: Train loss = 0.0084, Val loss = 0.0067 Epoch 39: Train loss = 0.0082, Val loss = 0.0068 Epoch 40: Train loss = 0.0082, Val loss = 0.0068 Epoch 41: Train loss = 0.0082, Val loss = 0.0067 Epoch 42: Train loss = 0.0082, Val loss = 0.0068 Epoch 43: Train loss = 0.0081, Val loss = 0.0069 Epoch 44: Train loss = 0.0081, Val loss = 0.0071 Epoch 45: Train loss = 0.0082, Val loss = 0.0071 Epoch 46: Train loss = 0.0081, Val loss = 0.0070 Epoch 47: Train loss = 0.0080, Val loss = 0.0070 Epoch 48: Train loss = 0.0080, Val loss = 0.0070 Epoch 49: Train loss = 0.0079, Val loss = 0.0072 Epoch 50: Train loss = 0.0080, Val loss = 0.0072 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0493, Val loss = 0.0224 Epoch 2: Train loss = 0.0308, Val loss = 0.0161 Epoch 3: Train loss = 0.0170, Val loss = 0.0130 Epoch 4: Train loss = 0.0101, Val loss = 0.0045 Epoch 5: Train loss = 0.0108, Val loss = 0.0063 Epoch 6: Train loss = 0.0100, Val loss = 0.0095 Epoch 7: Train loss = 0.0088, Val loss = 0.0061 Epoch 8: Train loss = 0.0082, Val loss = 0.0053 Epoch 9: Train loss = 0.0080, Val loss = 0.0080 Epoch 10: Train loss = 0.0078, Val loss = 0.0069 Epoch 11: Train loss = 0.0077, Val loss = 0.0058 Epoch 12: Train loss = 0.0076, Val loss = 0.0077 Epoch 13: Train loss = 0.0075, Val loss = 0.0068 Epoch 14: Train loss = 0.0074, Val loss = 0.0063 Epoch 15: Train loss = 0.0073, Val loss = 0.0078 Epoch 16: Train loss = 0.0073, Val loss = 0.0068 Epoch 17: Train loss = 0.0072, Val loss = 0.0076 Epoch 18: Train loss = 0.0071, Val loss = 0.0072 Epoch 19: Train loss = 0.0072, Val loss = 0.0072 Epoch 20: Train loss = 0.0071, Val loss = 0.0070 Epoch 21: Train loss = 0.0071, Val loss = 0.0067 Epoch 22: Train loss = 0.0070, Val loss = 0.0072 Epoch 23: Train loss = 0.0070, Val loss = 0.0066 Epoch 24: Train loss = 0.0070, Val loss = 0.0071 Epoch 25: Train loss = 0.0069, Val loss = 0.0066 Epoch 26: Train loss = 0.0068, Val loss = 0.0078 Epoch 27: Train loss = 0.0069, Val loss = 0.0061 Epoch 28: Train loss = 0.0068, Val loss = 0.0084 Epoch 29: Train loss = 0.0068, Val loss = 0.0058 Epoch 30: Train loss = 0.0068, Val loss = 0.0082 Epoch 31: Train loss = 0.0067, Val loss = 0.0062 Epoch 32: Train loss = 0.0067, Val loss = 0.0066 Epoch 33: Train loss = 0.0066, Val loss = 0.0077 Epoch 34: Train loss = 0.0065, Val loss = 0.0065 Epoch 35: Train loss = 0.0066, Val loss = 0.0056 Epoch 36: Train loss = 0.0065, Val loss = 0.0077 Epoch 37: Train loss = 0.0066, Val loss = 0.0082 Epoch 38: Train loss = 0.0065, Val loss = 0.0068 Epoch 39: Train loss = 0.0064, Val loss = 0.0059 Epoch 40: Train loss = 0.0063, Val loss = 0.0097 Epoch 41: Train loss = 0.0064, Val loss = 0.0072 Epoch 42: Train loss = 0.0065, Val loss = 0.0045 Epoch 43: Train loss = 0.0064, Val loss = 0.0070 Epoch 44: Train loss = 0.0062, Val loss = 0.0078 Epoch 45: Train loss = 0.0062, Val loss = 0.0049 Epoch 46: Train loss = 0.0062, Val loss = 0.0057 Epoch 47: Train loss = 0.0061, Val loss = 0.0075 Epoch 48: Train loss = 0.0061, Val loss = 0.0071 Epoch 49: Train loss = 0.0062, Val loss = 0.0062 Epoch 50: Train loss = 0.0060, Val loss = 0.0044 Epoch 51: Train loss = 0.0061, Val loss = 0.0048 Epoch 52: Train loss = 0.0059, Val loss = 0.0053 Epoch 53: Train loss = 0.0059, Val loss = 0.0062 Epoch 54: Train loss = 0.0060, Val loss = 0.0082 Epoch 55: Train loss = 0.0060, Val loss = 0.0056 Epoch 56: Train loss = 0.0058, Val loss = 0.0055 Epoch 57: Train loss = 0.0058, Val loss = 0.0046 Epoch 58: Train loss = 0.0057, Val loss = 0.0054 Epoch 59: Train loss = 0.0057, Val loss = 0.0056 Epoch 60: Train loss = 0.0056, Val loss = 0.0059 Early stopping triggered at epoch 60. Using device: mps Epoch 1: Train loss = 0.0522, Val loss = 0.0327 Epoch 2: Train loss = 0.0510, Val loss = 0.0316 Epoch 3: Train loss = 0.0497, Val loss = 0.0305 Epoch 4: Train loss = 0.0483, Val loss = 0.0291 Epoch 5: Train loss = 0.0468, Val loss = 0.0276 Epoch 6: Train loss = 0.0449, Val loss = 0.0258 Epoch 7: Train loss = 0.0428, Val loss = 0.0237 Epoch 8: Train loss = 0.0403, Val loss = 0.0212 Epoch 9: Train loss = 0.0374, Val loss = 0.0185 Epoch 10: Train loss = 0.0342, Val loss = 0.0156 Epoch 11: Train loss = 0.0304, Val loss = 0.0127 Epoch 12: Train loss = 0.0262, Val loss = 0.0103 Epoch 13: Train loss = 0.0221, Val loss = 0.0089 Epoch 14: Train loss = 0.0186, Val loss = 0.0091 Epoch 15: Train loss = 0.0159, Val loss = 0.0109 Epoch 16: Train loss = 0.0144, Val loss = 0.0124 Epoch 17: Train loss = 0.0132, Val loss = 0.0121 Epoch 18: Train loss = 0.0119, Val loss = 0.0104 Epoch 19: Train loss = 0.0108, Val loss = 0.0083 Epoch 20: Train loss = 0.0100, Val loss = 0.0068 Epoch 21: Train loss = 0.0097, Val loss = 0.0060 Epoch 22: Train loss = 0.0096, Val loss = 0.0059 Epoch 23: Train loss = 0.0095, Val loss = 0.0061 Epoch 24: Train loss = 0.0093, Val loss = 0.0066 Epoch 25: Train loss = 0.0092, Val loss = 0.0072 Epoch 26: Train loss = 0.0090, Val loss = 0.0076 Epoch 27: Train loss = 0.0089, Val loss = 0.0075 Epoch 28: Train loss = 0.0088, Val loss = 0.0073 Epoch 29: Train loss = 0.0088, Val loss = 0.0070 Epoch 30: Train loss = 0.0087, Val loss = 0.0067 Epoch 31: Train loss = 0.0085, Val loss = 0.0066 Epoch 32: Train loss = 0.0086, Val loss = 0.0067 Epoch 33: Train loss = 0.0085, Val loss = 0.0068 Epoch 34: Train loss = 0.0086, Val loss = 0.0069 Epoch 35: Train loss = 0.0084, Val loss = 0.0068 Epoch 36: Train loss = 0.0084, Val loss = 0.0067 Epoch 37: Train loss = 0.0082, Val loss = 0.0068 Epoch 38: Train loss = 0.0083, Val loss = 0.0068 Epoch 39: Train loss = 0.0082, Val loss = 0.0067 Epoch 40: Train loss = 0.0083, Val loss = 0.0067 Epoch 41: Train loss = 0.0082, Val loss = 0.0068 Epoch 42: Train loss = 0.0081, Val loss = 0.0069 Epoch 43: Train loss = 0.0081, Val loss = 0.0069 Epoch 44: Train loss = 0.0081, Val loss = 0.0070 Epoch 45: Train loss = 0.0081, Val loss = 0.0070 Epoch 46: Train loss = 0.0080, Val loss = 0.0070 Epoch 47: Train loss = 0.0081, Val loss = 0.0070 Epoch 48: Train loss = 0.0080, Val loss = 0.0070 Epoch 49: Train loss = 0.0080, Val loss = 0.0071 Epoch 50: Train loss = 0.0079, Val loss = 0.0072 Early stopping triggered at epoch 50. Using device: mps Epoch 1: Train loss = 0.0472, Val loss = 0.0209 Epoch 2: Train loss = 0.0293, Val loss = 0.0109 Epoch 3: Train loss = 0.0176, Val loss = 0.0152 Epoch 4: Train loss = 0.0103, Val loss = 0.0036 Epoch 5: Train loss = 0.0111, Val loss = 0.0043 Epoch 6: Train loss = 0.0102, Val loss = 0.0091 Epoch 7: Train loss = 0.0096, Val loss = 0.0077 Epoch 8: Train loss = 0.0083, Val loss = 0.0047 Epoch 9: Train loss = 0.0082, Val loss = 0.0054 Epoch 10: Train loss = 0.0079, Val loss = 0.0081 Epoch 11: Train loss = 0.0078, Val loss = 0.0069 Epoch 12: Train loss = 0.0078, Val loss = 0.0057 Epoch 13: Train loss = 0.0077, Val loss = 0.0065 Epoch 14: Train loss = 0.0075, Val loss = 0.0078 Epoch 15: Train loss = 0.0075, Val loss = 0.0067 Epoch 16: Train loss = 0.0073, Val loss = 0.0066 Epoch 17: Train loss = 0.0073, Val loss = 0.0082 Epoch 18: Train loss = 0.0073, Val loss = 0.0071 Epoch 19: Train loss = 0.0072, Val loss = 0.0071 Epoch 20: Train loss = 0.0072, Val loss = 0.0076 Epoch 21: Train loss = 0.0071, Val loss = 0.0072 Epoch 22: Train loss = 0.0071, Val loss = 0.0072 Epoch 23: Train loss = 0.0071, Val loss = 0.0075 Epoch 24: Train loss = 0.0071, Val loss = 0.0075 Epoch 25: Train loss = 0.0071, Val loss = 0.0070 Epoch 26: Train loss = 0.0070, Val loss = 0.0074 Epoch 27: Train loss = 0.0069, Val loss = 0.0068 Epoch 28: Train loss = 0.0069, Val loss = 0.0068 Epoch 29: Train loss = 0.0068, Val loss = 0.0075 Epoch 30: Train loss = 0.0069, Val loss = 0.0069 Epoch 31: Train loss = 0.0067, Val loss = 0.0071 Epoch 32: Train loss = 0.0067, Val loss = 0.0065 Epoch 33: Train loss = 0.0066, Val loss = 0.0077 Epoch 34: Train loss = 0.0067, Val loss = 0.0065 Epoch 35: Train loss = 0.0066, Val loss = 0.0061 Epoch 36: Train loss = 0.0065, Val loss = 0.0090 Epoch 37: Train loss = 0.0065, Val loss = 0.0060 Epoch 38: Train loss = 0.0065, Val loss = 0.0062 Epoch 39: Train loss = 0.0064, Val loss = 0.0075 Epoch 40: Train loss = 0.0064, Val loss = 0.0072 Epoch 41: Train loss = 0.0063, Val loss = 0.0065 Epoch 42: Train loss = 0.0062, Val loss = 0.0063 Epoch 43: Train loss = 0.0063, Val loss = 0.0086 Epoch 44: Train loss = 0.0063, Val loss = 0.0054 Epoch 45: Train loss = 0.0062, Val loss = 0.0054 Epoch 46: Train loss = 0.0062, Val loss = 0.0066 Epoch 47: Train loss = 0.0061, Val loss = 0.0075 Epoch 48: Train loss = 0.0061, Val loss = 0.0047 Epoch 49: Train loss = 0.0061, Val loss = 0.0047 Epoch 50: Train loss = 0.0060, Val loss = 0.0042 Early stopping triggered at epoch 50. Best parameters found in single holdout: batch_size: 512 dropout: 0.1 hidden_dim: 256 hidden_layers: 4 l2_weight: 0.0005 lr: 0.0005 Results for the best model from sigle hold out evaluation: --- Task 1 --- 1 day(s) MAE (log-var) : 0.64061763 1 day(s) RMSE (log-var) : 0.83228683 1 day(s) R2 (log-var) : -0.03610080 1 day(s) MAE (var) : 2.49631354 1 day(s) RMSE (var) : 8.65006150 1 day(s) R2 (var) : -0.00173153 1 day(s) QLIKE (var) : 0.49636718 5 day(s) MAE (log-var) : 0.65466516 5 day(s) RMSE (log-var) : 0.85062716 5 day(s) R2 (log-var) : -0.04632316 5 day(s) MAE (var) : 2.63176205 5 day(s) RMSE (var) : 8.95934072 5 day(s) R2 (var) : -0.00450938 5 day(s) QLIKE (var) : 0.52235639 10 day(s) MAE (log-var) : 0.66138330 10 day(s) RMSE (log-var) : 0.85546546 10 day(s) R2 (log-var) : -0.04766021 10 day(s) MAE (var) : 2.66861418 10 day(s) RMSE (var) : 9.00214806 10 day(s) R2 (var) : -0.00578935 10 day(s) QLIKE (var) : 0.52905687 20 day(s) MAE (log-var) : 0.66353974 20 day(s) RMSE (log-var) : 0.85419605 20 day(s) R2 (log-var) : -0.03253761 20 day(s) MAE (var) : 2.68673372 20 day(s) RMSE (var) : 9.00735573 20 day(s) R2 (var) : -0.00267009 20 day(s) QLIKE (var) : 0.52306397 full horizon MAE (log-var) : 0.66376878 full horizon RMSE (log-var) : 0.85178716 full horizon R2 (log-var) : -0.01547248 full horizon MAE (var) : 2.70255373 full horizon RMSE (var) : 9.00769311 full horizon R2 (var) : 0.00272291 full horizon QLIKE (var) : 0.51362617 Best single-holdout model saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_OHO_SMLP_model.pkl
All tasks EURUSD with Vanilla MLP without CV¶
In [ ]:
import os
save_test_SMLP_model_9_EURUSD_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_SMLP_model_9_EURUSD.pkl"
)
t9_EURUSD_MLP_results, t9_EURUSD_MLP_nested_results, t9_EURUSD_MLP_best_model, t9_EURUSD_MLP_best_params, _ = train_and_evaluate_model(
model_type="Simple_MLP",
X_price=eur2_X_price,
X_time=eur2_X_time,
y=eur2_y,
no_tasks=eur2_y.shape[2],
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
save_model_path=save_test_SMLP_model_9_EURUSD_file_path,
lr=1e-3,
epochs=50,
batch_size=32,
verbose=True,
time_horizon=28,
dropout=0,
l2_weight=1e-5,
patience=10,
min_epochs=90,
hidden_dim=256,
hidden_layers=3
)
All tasks EURUSD 3 with Vanilla MLP without CV¶
In [ ]:
import os
save_test_SMLP_model_9_EURUSD_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_SMLP_model_9_EURUSD.pkl"
)
t9_EURUSD_MLP_results, t9_EURUSD_MLP_nested_results, t9_EURUSD_MLP_best_model, t9_EURUSD_MLP_best_params, _ = train_and_evaluate_model(
model_type="Simple_MLP",
X_price=eur3_X_price,
X_time=eur3_X_time,
y=eur3_y,
no_tasks=eur3_y.shape[2],
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
save_model_path=save_test_SMLP_model_9_EURUSD_file_path,
lr=5e-4,
epochs=50,
batch_size=128,
verbose=True,
time_horizon=28,
dropout=0,
l2_weight=1e-5,
patience=10,
min_epochs=90,
hidden_dim=64,
hidden_layers=2
)
Using device: mps Switching model.loss_type → 'mse' for log_var_ratio training (NLL expects absolute variance). Batch size for y: 3782 Time steps for y: 28 Features for y: 8 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking y_train_core (log_var_ratio scaled): Shape: (2723, 28, 8) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.305190246584762 Min value: -4.314240572680862 Checking X_price_val: Shape: (302, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.864238163413908 Min value: 0.00022460106458896233 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.1249999999999998 Min value: 0.0 Checking y_val (log_var_ratio scaled): Shape: (302, 28, 8) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3172909297315214 Min value: -3.6501912507837058 Checking X_price_test: Shape: (757, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.9835594561318155 Min value: -0.00012220230225973657 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.4999999999999996 Min value: 0.0 Checking y_test (log_var_ratio scaled): Shape: (757, 28, 8) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.2742605055227685 Min value: -4.387187839607449 Size of the X data for training (core): (2723, 120) Size of the Y data for training (core): (2723, 224) Epoch 1: Train mse = 1.0712 | Val mse = 0.6699 Epoch 2: Train mse = 1.0192 | Val mse = 0.6591 Epoch 3: Train mse = 1.0088 | Val mse = 0.6530 Epoch 4: Train mse = 1.0028 | Val mse = 0.6516 Epoch 5: Train mse = 1.0005 | Val mse = 0.6493 Epoch 6: Train mse = 0.9976 | Val mse = 0.6463 Epoch 7: Train mse = 0.9930 | Val mse = 0.6396 Epoch 8: Train mse = 0.9857 | Val mse = 0.6292 Epoch 9: Train mse = 0.9763 | Val mse = 0.6106 Epoch 10: Train mse = 0.9603 | Val mse = 0.5837 Epoch 11: Train mse = 0.9201 | Val mse = 0.5625 Epoch 12: Train mse = 0.8633 | Val mse = 0.5451 Epoch 13: Train mse = 0.8196 | Val mse = 0.5403 Epoch 14: Train mse = 0.7944 | Val mse = 0.5307 Epoch 15: Train mse = 0.7813 | Val mse = 0.5341 Epoch 16: Train mse = 0.7725 | Val mse = 0.5412 Epoch 17: Train mse = 0.7675 | Val mse = 0.5470 Epoch 18: Train mse = 0.7629 | Val mse = 0.5582 Epoch 19: Train mse = 0.7587 | Val mse = 0.5686 Epoch 20: Train mse = 0.7549 | Val mse = 0.5796 Epoch 21: Train mse = 0.7514 | Val mse = 0.5903 Epoch 22: Train mse = 0.7483 | Val mse = 0.5990 Epoch 23: Train mse = 0.7455 | Val mse = 0.6058 Epoch 24: Train mse = 0.7428 | Val mse = 0.6109 Epoch 25: Train mse = 0.7403 | Val mse = 0.6142 Epoch 26: Train mse = 0.7378 | Val mse = 0.6162 Epoch 27: Train mse = 0.7356 | Val mse = 0.6172 Epoch 28: Train mse = 0.7336 | Val mse = 0.6175 Epoch 29: Train mse = 0.7319 | Val mse = 0.6172 Epoch 30: Train mse = 0.7304 | Val mse = 0.6164 Epoch 31: Train mse = 0.7292 | Val mse = 0.6153 Epoch 32: Train mse = 0.7282 | Val mse = 0.6143 Epoch 33: Train mse = 0.7274 | Val mse = 0.6135 Epoch 34: Train mse = 0.7268 | Val mse = 0.6132 Epoch 35: Train mse = 0.7262 | Val mse = 0.6137 Epoch 36: Train mse = 0.7256 | Val mse = 0.6148 Epoch 37: Train mse = 0.7251 | Val mse = 0.6163 Epoch 38: Train mse = 0.7245 | Val mse = 0.6181 Epoch 39: Train mse = 0.7240 | Val mse = 0.6200 Epoch 40: Train mse = 0.7234 | Val mse = 0.6218 Epoch 41: Train mse = 0.7228 | Val mse = 0.6233 Epoch 42: Train mse = 0.7222 | Val mse = 0.6247 Epoch 43: Train mse = 0.7216 | Val mse = 0.6258 Epoch 44: Train mse = 0.7209 | Val mse = 0.6267 Epoch 45: Train mse = 0.7201 | Val mse = 0.6274 Epoch 46: Train mse = 0.7193 | Val mse = 0.6279 Epoch 47: Train mse = 0.7185 | Val mse = 0.6281 Epoch 48: Train mse = 0.7176 | Val mse = 0.6282 Epoch 49: Train mse = 0.7167 | Val mse = 0.6281 Epoch 50: Train mse = 0.7157 | Val mse = 0.6279 Parameters used in the single-fit model: input_dim: 120 output_dim: 224 dropout: 0.00000000 lr: 0.00050000 epochs: 50 batch_size: 128 device: mps verbose: True hidden_layers: 2 no_tasks: 8 l2_weight: 0.00001000 patience: 10 min_epochs: 90 min_delta: 0.00010000 hidden_dim: 64 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.11103872 1 day(s) RMSE : 0.20864335 1 day(s) R2 : 0.04625683 1 day(s) Pearson r : 0.34357118 1 day(s) QLIKE : 0.51659101 3 day(s) MAE : 0.11042431 3 day(s) RMSE : 0.20581183 3 day(s) R2 : 0.06480831 3 day(s) Pearson r : 0.33792193 3 day(s) QLIKE : 0.51526911 5 day(s) MAE : 0.11063432 5 day(s) RMSE : 0.20497981 5 day(s) R2 : 0.06181290 5 day(s) Pearson r : 0.32561392 5 day(s) QLIKE : 0.51706068 10 day(s) MAE : 0.11042366 10 day(s) RMSE : 0.20422895 10 day(s) R2 : 0.05978366 10 day(s) Pearson r : 0.31826269 10 day(s) QLIKE : 0.51751716 20 day(s) MAE : 0.11071115 20 day(s) RMSE : 0.20385819 20 day(s) R2 : 0.05214553 20 day(s) Pearson r : 0.30587741 20 day(s) QLIKE : 0.51985509 full horizon MAE : 0.11133031 full horizon RMSE : 0.20428137 full horizon R2 : 0.04304657 full horizon Pearson r : 0.28814428 full horizon QLIKE : 0.52567919 --- Task 2 --- 1 day(s) MAE : 0.15539526 1 day(s) RMSE : 0.31164278 1 day(s) R2 : -0.33090319 1 day(s) Pearson r : 0.01163244 1 day(s) QLIKE : 12.98528923 3 day(s) MAE : 0.15421316 3 day(s) RMSE : 0.30966925 3 day(s) R2 : -0.32977651 3 day(s) Pearson r : 0.02741066 3 day(s) QLIKE : 13.01252850 5 day(s) MAE : 0.15378779 5 day(s) RMSE : 0.30888711 5 day(s) R2 : -0.32957251 5 day(s) Pearson r : 0.01242597 5 day(s) QLIKE : 13.14369696 10 day(s) MAE : 0.15330539 10 day(s) RMSE : 0.30815422 10 day(s) R2 : -0.32890168 10 day(s) Pearson r : 0.03218002 10 day(s) QLIKE : 13.08815369 20 day(s) MAE : 0.15307454 20 day(s) RMSE : 0.30788331 20 day(s) R2 : -0.32835322 20 day(s) Pearson r : 0.03486894 20 day(s) QLIKE : 13.12220077 full horizon MAE : 0.15358063 full horizon RMSE : 0.30866619 full horizon R2 : -0.32901815 full horizon Pearson r : 0.02788881 full horizon QLIKE : 13.11606246 --- Task 3 --- 1 day(s) MAE : 0.06343678 1 day(s) RMSE : 0.08439328 1 day(s) R2 : 0.00216887 1 day(s) Pearson r : 0.71092168 1 day(s) QLIKE : 0.06096452 3 day(s) MAE : 0.06091535 3 day(s) RMSE : 0.08097772 3 day(s) R2 : 0.07894655 3 day(s) Pearson r : 0.71231073 3 day(s) QLIKE : 0.06334039 5 day(s) MAE : 0.06434223 5 day(s) RMSE : 0.08967931 5 day(s) R2 : -0.12704834 5 day(s) Pearson r : 0.70459164 5 day(s) QLIKE : 0.06194658 10 day(s) MAE : 0.06365845 10 day(s) RMSE : 0.08605711 10 day(s) R2 : -0.03351570 10 day(s) Pearson r : 0.66658640 10 day(s) QLIKE : 0.06121039 20 day(s) MAE : 0.06133690 20 day(s) RMSE : 0.08162220 20 day(s) R2 : 0.05503045 20 day(s) Pearson r : 0.67642336 20 day(s) QLIKE : 0.05986425 full horizon MAE : 0.06164279 full horizon RMSE : 0.08202875 full horizon R2 : 0.03792841 full horizon Pearson r : 0.66917059 full horizon QLIKE : 0.05879267 --- Task 4 --- 1 day(s) MAE : 0.03453525 1 day(s) RMSE : 0.05623033 1 day(s) R2 : -0.60508741 1 day(s) Pearson r : -0.10135682 1 day(s) QLIKE : 11.11405374 3 day(s) MAE : 0.03452383 3 day(s) RMSE : 0.05621810 3 day(s) R2 : -0.60398405 3 day(s) Pearson r : -0.08810974 3 day(s) QLIKE : 11.40786087 5 day(s) MAE : 0.03449000 5 day(s) RMSE : 0.05621482 5 day(s) R2 : -0.60271880 5 day(s) Pearson r : -0.07085644 5 day(s) QLIKE : 11.29629285 10 day(s) MAE : 0.03441563 10 day(s) RMSE : 0.05619619 10 day(s) R2 : -0.59947874 10 day(s) Pearson r : -0.06286938 10 day(s) QLIKE : 11.35737413 20 day(s) MAE : 0.03437502 20 day(s) RMSE : 0.05618612 20 day(s) R2 : -0.59781729 20 day(s) Pearson r : -0.04927722 20 day(s) QLIKE : 11.14983119 full horizon MAE : 0.03432559 full horizon RMSE : 0.05617418 full horizon R2 : -0.59557058 full horizon Pearson r : -0.04121754 full horizon QLIKE : 11.19202595 --- Task 5 --- 1 day(s) MAE : 0.69546400 1 day(s) RMSE : 0.76492033 1 day(s) R2 : -3.33174581 1 day(s) Pearson r : 0.08997483 1 day(s) QLIKE : 3.42706487 3 day(s) MAE : 0.69218298 3 day(s) RMSE : 0.76131513 3 day(s) R2 : -3.29055973 3 day(s) Pearson r : 0.07644863 3 day(s) QLIKE : 3.45252049 5 day(s) MAE : 0.68458022 5 day(s) RMSE : 0.75450730 5 day(s) R2 : -3.21320529 5 day(s) Pearson r : 0.08190575 5 day(s) QLIKE : 3.45892221 10 day(s) MAE : 0.69400294 10 day(s) RMSE : 0.76486142 10 day(s) R2 : -3.32740476 10 day(s) Pearson r : 0.07096972 10 day(s) QLIKE : 3.51356565 20 day(s) MAE : 0.69670907 20 day(s) RMSE : 0.76766275 20 day(s) R2 : -3.35741105 20 day(s) Pearson r : 0.05621275 20 day(s) QLIKE : 3.50296607 full horizon MAE : 0.69462232 full horizon RMSE : 0.76490433 full horizon R2 : -3.32401319 full horizon Pearson r : 0.03779874 full horizon QLIKE : 3.52336656 --- Task 6 --- 1 day(s) MAE : 0.03310339 1 day(s) RMSE : 0.06223379 1 day(s) R2 : -0.38629635 1 day(s) Pearson r : -0.17633517 1 day(s) QLIKE : 4.16885653 3 day(s) MAE : 0.03312630 3 day(s) RMSE : 0.06222116 3 day(s) R2 : -0.38552510 3 day(s) Pearson r : -0.15250340 3 day(s) QLIKE : 4.25629915 5 day(s) MAE : 0.03305612 5 day(s) RMSE : 0.06220861 5 day(s) R2 : -0.38459070 5 day(s) Pearson r : -0.16008457 5 day(s) QLIKE : 4.36238227 10 day(s) MAE : 0.03319670 10 day(s) RMSE : 0.06231751 10 day(s) R2 : -0.38857092 10 day(s) Pearson r : -0.12495025 10 day(s) QLIKE : 4.30631520 20 day(s) MAE : 0.03302093 20 day(s) RMSE : 0.06226365 20 day(s) R2 : -0.38531604 20 day(s) Pearson r : -0.12767741 20 day(s) QLIKE : 4.27389322 full horizon MAE : 0.03288570 full horizon RMSE : 0.06222517 full horizon R2 : -0.38260683 full horizon Pearson r : -0.12637777 full horizon QLIKE : 4.24863318 --- Task 7 --- 1 day(s) MAE : 0.00512763 1 day(s) RMSE : 0.00780973 1 day(s) R2 : -0.75770887 1 day(s) Pearson r : 0.17016662 1 day(s) QLIKE : 9.07038862 3 day(s) MAE : 0.00512744 3 day(s) RMSE : 0.00780943 3 day(s) R2 : -0.75757006 3 day(s) Pearson r : 0.10004566 3 day(s) QLIKE : 10.31558587 5 day(s) MAE : 0.00512751 5 day(s) RMSE : 0.00780954 5 day(s) R2 : -0.75762192 5 day(s) Pearson r : 0.08203973 5 day(s) QLIKE : 10.04213929 10 day(s) MAE : 0.00512755 10 day(s) RMSE : 0.00780961 10 day(s) R2 : -0.75765181 10 day(s) Pearson r : 0.07528033 10 day(s) QLIKE : 9.72162298 20 day(s) MAE : 0.00513558 20 day(s) RMSE : 0.00781228 20 day(s) R2 : -0.76093509 20 day(s) Pearson r : 0.07207377 20 day(s) QLIKE : 9.64707149 full horizon MAE : 0.00515839 full horizon RMSE : 0.00782259 full horizon R2 : -0.76935270 full horizon Pearson r : 0.06373753 full horizon QLIKE : 9.65553754 --- Task 8 --- 1 day(s) MAE : 2.49077294 1 day(s) RMSE : 3.36139940 1 day(s) R2 : -6.39456952 1 day(s) Pearson r : 0.25720817 1 day(s) QLIKE : 0.05153811 3 day(s) MAE : 2.42889177 3 day(s) RMSE : 3.41054160 3 day(s) R2 : -6.60967509 3 day(s) Pearson r : 0.22954922 3 day(s) QLIKE : 0.05917651 5 day(s) MAE : 2.57341971 5 day(s) RMSE : 3.52759259 5 day(s) R2 : -7.13666040 5 day(s) Pearson r : 0.23911820 5 day(s) QLIKE : 0.06120540 10 day(s) MAE : 2.65922680 10 day(s) RMSE : 3.60099697 10 day(s) R2 : -7.46081003 10 day(s) Pearson r : 0.22169162 10 day(s) QLIKE : 0.06317712 20 day(s) MAE : 2.79918060 20 day(s) RMSE : 3.74973350 20 day(s) R2 : -8.17319960 20 day(s) Pearson r : 0.19941223 20 day(s) QLIKE : 0.06600200 full horizon MAE : 2.87952960 full horizon RMSE : 3.81439699 full horizon R2 : -8.50153808 full horizon Pearson r : 0.19067444 full horizon QLIKE : 0.06677623
Test 3 tasks Vanila MLP with CV¶
In [ ]:
import os
save_checkpoint_SMLP_model_3_CV_file_path = os.path.join(
root_folder,
objects_relative_path,
"checkpoint_SMLP_model_3_CV.pkl"
)
save_test_SMLP_model_3_CV_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_SMLP_model_3_CV.pkl"
)
param_grid_test_SMLP_3_CV_aapl_cv = {
"dropout": [0.2],
"lr": [3e-5],
"batch_size": [128, 256],
"epochs": [3],
"resume": [True],
"verbose": [True]
}
t3CV_SMLP_results, t3CV_SMLP_nested_results, t3CV_SMLP_best_model, t3CV_SMLP_best_params = train_and_evaluate_model(
model_type="Simple_MLP",
X_price=aapl_X_price,
X_time=aapl_X_time,
y=aapl_y,
no_tasks=3,
use_nested_cv=True,
flatten=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
save_model_path=save_test_SMLP_model_3_CV_file_path,
lr=3e-6,
epochs=150,
batch_size=64,
verbose=True,
hidden_layers=21,
outer_folds=2,
inner_folds=2,
param_grid=param_grid_test_SMLP_3_CV_aapl_cv,
checkpoint_path= save_checkpoint_SMLP_model_3_CV_file_path
)
LSTM models¶
Vanilla LSTM¶
Vanilla LSTM architecture¶
In [121]:
import torch
import torch.nn as nn
class LSTMSeq2Seq(nn.Module):
def __init__(self, input_dim, output_dim, hidden_layers = 2, dropout=0.0, pred_len=28, hidden_dim=64, no_tasks=1):
super().__init__()
self.encoder_layers = hidden_layers
self.decoder_layers = hidden_layers
self.pred_len = pred_len
self.output_dim = output_dim
self.input_dim = input_dim
self.hidden_dim = hidden_dim
enc_dec_dropout = dropout if hidden_layers > 1 else 0.0
self.encoder = nn.LSTM(
input_size=self.input_dim,
hidden_size=self.hidden_dim,
num_layers=self.encoder_layers,
bias=True,
batch_first=True,
dropout=enc_dec_dropout,
bidirectional=False,
proj_size=0,
)
self.decoder = nn.LSTM(
input_size=self.output_dim,
hidden_size=self.hidden_dim,
num_layers=self.decoder_layers,
bias=True,
batch_first=True,
dropout=enc_dec_dropout,
bidirectional=False,
proj_size=0,
)
self.start_proj = nn.Linear(self.hidden_dim, self.output_dim)
self.fc = nn.Linear(self.hidden_dim, self.output_dim)
self._sched_mode = None
self._sched_start = 1.0
self._sched_end = 0.0
self._sched_steps = 1
self._sched_step = 0
self.teacher_forcing_ratio = 1.0
self._tf_floor = 1e-6
def set_tf_schedule(self, start: float = 1.0, end: float = 0.0, steps: int = 1000, mode: str = "linear"):
self._sched_mode = mode
self._sched_start = float(start)
self._sched_end = float(end)
self._sched_steps = max(int(steps), 1)
self._sched_step = 0
self.teacher_forcing_ratio = self._sched_start
def disable_tf_schedule(self, ratio: float = 1.0):
self._sched_mode = None
self.teacher_forcing_ratio = float(ratio)
def _advance_tf_ratio(self):
if self._sched_mode is None:
return
t = min(self._sched_step / max(self._sched_steps - 1, 1), 1.0)
if self._sched_mode == "linear":
r = self._sched_start + t * (self._sched_end - self._sched_start)
elif self._sched_mode == "exp":
start = max(self._sched_start, self._tf_floor)
end = max(self._sched_end, self._tf_floor)
r = start * ((end / start) ** t)
else:
r = self.teacher_forcing_ratio
self.teacher_forcing_ratio = float(min(max(r, 0.0), 1.0))
self._sched_step += 1
def forward(self, x, y=None, teacher_forcing=True):
B = x.size(0)
_, (h, c) = self.encoder(x)
h_dec, c_dec = h, c
z = h[-1]
dec_in = self.start_proj(z).unsqueeze(1)
outs = []
if self.training and teacher_forcing:
self._advance_tf_ratio()
tf_ratio = self.teacher_forcing_ratio
else:
tf_ratio = 0.0
for t in range(self.pred_len):
out, (h_dec, c_dec) = self.decoder(dec_in, (h_dec, c_dec))
pred = self.fc(out)
outs.append(pred)
use_tf = (
teacher_forcing
and self.training
and (y is not None)
and (t < y.size(1))
and (torch.rand((), device=x.device) < tf_ratio)
)
dec_in = y[:, t:t+1] if use_tf else pred
return torch.cat(outs, dim=1)
Vanilla LSTM wrapper¶
In [122]:
from sklearn.base import BaseEstimator, RegressorMixin
import torch
import torch.nn as nn
class SimpleLSTMWrapper(BaseEstimator, RegressorMixin):
def __init__(
self,
input_dim=10,
output_dim=1,
dropout=0.2,
lr=1e-3,
epochs=50,
batch_size=512,
device=None,
verbose=True,
hidden_layers=3,
hidden_dim=64,
no_tasks=1,
pred_len=28,
l2_weight=1e-4,
patience=10,
min_epochs=30,
min_delta=1e-4,
loss_type="gauss_nll_var",
target_is_logvar=True,
nll_eps=1e-12,
clamp_logvar_min=-20.0,
clamp_logvar_max=20.0,
student_df=5.0,
):
self.input_dim = int(input_dim)
self.output_dim = int(output_dim)
self.dropout = float(dropout)
self.lr = float(lr)
self.epochs = int(epochs)
self.batch_size = int(batch_size)
self.device = device or (
"mps" if torch.backends.mps.is_available()
else "cuda" if torch.cuda.is_available()
else "cpu"
)
self.verbose = bool(verbose)
self.hidden_layers = int(hidden_layers)
self.no_tasks = int(no_tasks)
self.pred_len = int(pred_len)
self.l2_weight = float(l2_weight)
self.hidden_dim = int(hidden_dim)
self.patience = int(patience)
self.min_epochs = int(min_epochs)
self.min_delta = float(min_delta)
self.loss_type = str(loss_type)
self.target_is_logvar = bool(target_is_logvar)
self.nll_eps = float(nll_eps)
self.clamp_logvar_min = float(clamp_logvar_min)
self.clamp_logvar_max = float(clamp_logvar_max)
self.student_df = float(student_df)
self._build()
if self.verbose:
print("Using device:", self.device)
def _build(self):
self.model = LSTMSeq2Seq(
input_dim=self.input_dim,
output_dim=self.output_dim,
hidden_layers=self.hidden_layers,
dropout=self.dropout,
pred_len=self.pred_len,
hidden_dim=self.hidden_dim,
no_tasks=self.no_tasks,
).to(self.device)
self.optimizer = torch.optim.AdamW(
self.model.parameters(), lr=self.lr, weight_decay=self.l2_weight
)
self._mse = nn.MSELoss()
def _compute_loss(self, y_hat, y_true):
if self.loss_type == "mse":
return self._mse(y_hat, y_true)
z = torch.clamp(y_hat, self.clamp_logvar_min, self.clamp_logvar_max)
if self.target_is_logvar:
v = torch.exp(y_true)
else:
v = y_true
v = torch.clamp(v, min=self.nll_eps)
if self.loss_type == "gauss_nll_var":
loss = v * torch.exp(-z) + z
return loss.mean()
if self.loss_type == "student_t_nll_var":
nu = torch.tensor(self.student_df, device=z.device, dtype=z.dtype)
loss = 0.5 * (nu + 1.0) * torch.log1p(v / (nu * torch.exp(z))) + 0.5 * z
return loss.mean()
raise ValueError(f"Unknown loss_type: {self.loss_type}")
def fit(self, X, y, X_test, y_test):
if y.ndim != 3:
raise ValueError(f"y must be 3D (N, pred_len, output_dim); got {y.shape}")
if y.shape[1] != self.pred_len:
raise ValueError(f"y.shape[1] ({y.shape[1]}) must equal pred_len ({self.pred_len})")
if y.shape[2] != self.output_dim:
raise ValueError(f"y.shape[2] ({y.shape[2]}) must equal output_dim ({self.output_dim})")
X = torch.tensor(X, dtype=torch.float32, device=self.device)
y = torch.tensor(y, dtype=torch.float32, device=self.device)
X_val = torch.tensor(X_test, dtype=torch.float32, device=self.device)
y_val = torch.tensor(y_test, dtype=torch.float32, device=self.device)
loader = torch.utils.data.DataLoader(
torch.utils.data.TensorDataset(X, y),
batch_size=self.batch_size,
shuffle=True,
)
best_val = float("inf")
best_state = None
no_imp = 0
for epoch in range(1, self.epochs + 1):
self.model.train()
tr_loss = 0.0
nb = 0
for xb, yb in loader:
self.optimizer.zero_grad(set_to_none=True)
preds = self.model(xb, yb, teacher_forcing=True)
loss = self._compute_loss(preds, yb)
loss.backward()
self.optimizer.step()
tr_loss += float(loss.detach().cpu())
nb += 1
tr_loss /= max(1, nb)
self.model.eval()
with torch.no_grad():
val_preds = self.model(X_val, teacher_forcing=False)
vloss = float(self._compute_loss(val_preds, y_val).detach().cpu())
if self.verbose:
print(f"Epoch {epoch}: Train {self.loss_type} = {tr_loss:.4f} | Val {self.loss_type} = {vloss:.4f}")
if (best_val - vloss) > self.min_delta:
best_val = vloss
no_imp = 0
best_state = {k: v.detach().cpu() for k, v in self.model.state_dict().items()}
else:
no_imp += 1
if no_imp >= self.patience and epoch >= self.min_epochs:
if self.verbose:
print(f"Early stopping triggered at epoch {epoch}.")
break
if best_state is not None:
self.model.load_state_dict(best_state)
return self
def predict(self, X):
self.model.eval()
X = torch.tensor(X, dtype=torch.float32, device=self.device)
with torch.no_grad():
preds = self.model(X, teacher_forcing=False)
return preds.detach().cpu().numpy()
def get_params(self, deep=True):
return dict(
input_dim=self.input_dim,
output_dim=self.output_dim,
hidden_layers=self.hidden_layers,
dropout=self.dropout,
lr=self.lr,
epochs=self.epochs,
batch_size=self.batch_size,
device=self.device,
verbose=self.verbose,
no_tasks=self.no_tasks,
pred_len=self.pred_len,
l2_weight=self.l2_weight,
hidden_dim=self.hidden_dim,
patience=self.patience,
min_epochs=self.min_epochs,
min_delta=self.min_delta,
loss_type=self.loss_type,
target_is_logvar=self.target_is_logvar,
nll_eps=self.nll_eps,
clamp_logvar_min=self.clamp_logvar_min,
clamp_logvar_max=self.clamp_logvar_max,
student_df=self.student_df,
)
def set_params(self, **params):
arch_keys = {
"input_dim", "output_dim", "hidden_layers", "hidden_dim",
"dropout", "no_tasks", "pred_len", "device"
}
need_rebuild = any(k in arch_keys for k in params)
for k, v in params.items():
if hasattr(self, k):
setattr(self, k, v)
if need_rebuild:
self._build()
else:
for g in self.optimizer.param_groups:
g["lr"] = self.lr
g["weight_decay"] = self.l2_weight
return self
Test 1 task EURUSD with Vanilla LSTM without CV¶
In [359]:
import os
save_test_EURUSD_SLSTM_model_1_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SLSTM_model_1_task.pkl"
)
t1_EURUSD_SLSTM_results, t1_EURUSD_SLSTM_nested_results, t1_EURUSD_SLSTM_best_model, t1_EURUSD_SLSTM_best_params, _ = train_and_evaluate_model(
model_type="Simple_LSTM",
X_price=eur2_X_price,
X_time=eur2_X_time,
y=eur2_y,
no_tasks=1,
use_nested_cv=False,
flatten=False,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_EURUSD_SLSTM_model_1_file_path,
lr=5e-4,
epochs=50,
batch_size=16,
verbose=True,
hidden_layers=2,
time_horizon=1,
l2_weight= 1e-6,
hidden_dim=32
)
Using device: mps Batch size for y: 3782 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 8) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0000000000000002 Min value: 0.0 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_train_core (log_var_ratio scaled): Shape: (2723, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.1619931883384433 Min value: -3.7675739443866982 Checking X_price_val: Shape: (302, 60, 8) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7917474776.440305 Min value: -0.007013492710980488 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_val (log_var_ratio scaled): Shape: (302, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.9457568816440924 Min value: -3.7365559140878837 Checking X_price_test: Shape: (757, 60, 8) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.4141668678880164 Min value: -0.00012220230225973657 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_test (log_var_ratio scaled): Shape: (757, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.229980689853926 Min value: -4.487665162496176 Size of the X data for training (core): (2723, 60, 8) Size of the Y data for training (core): (2723, 1, 1) Epoch 1: Train MSE = 0.9493 | Val MSE = 0.7655 Epoch 2: Train MSE = 0.8263 | Val MSE = 0.7812 Epoch 3: Train MSE = 0.7749 | Val MSE = 0.7782 Epoch 4: Train MSE = 0.7608 | Val MSE = 0.8438 Epoch 5: Train MSE = 0.7569 | Val MSE = 0.8543 Epoch 6: Train MSE = 0.7416 | Val MSE = 0.8343 Epoch 7: Train MSE = 0.7488 | Val MSE = 0.8421 Epoch 8: Train MSE = 0.7583 | Val MSE = 0.8486 Epoch 9: Train MSE = 0.7380 | Val MSE = 0.8175 Epoch 10: Train MSE = 0.7381 | Val MSE = 0.8412 Epoch 11: Train MSE = 0.7326 | Val MSE = 0.8615 Epoch 12: Train MSE = 0.7414 | Val MSE = 0.8658 Epoch 13: Train MSE = 0.7321 | Val MSE = 0.8298 Epoch 14: Train MSE = 0.7423 | Val MSE = 0.8705 Epoch 15: Train MSE = 0.7351 | Val MSE = 0.8571 Epoch 16: Train MSE = 0.7291 | Val MSE = 0.8576 Epoch 17: Train MSE = 0.7367 | Val MSE = 0.8892 Epoch 18: Train MSE = 0.7251 | Val MSE = 0.8759 Epoch 19: Train MSE = 0.7269 | Val MSE = 0.8966 Epoch 20: Train MSE = 0.7357 | Val MSE = 0.9217 Epoch 21: Train MSE = 0.7407 | Val MSE = 0.8192 Epoch 22: Train MSE = 0.7213 | Val MSE = 0.8646 Epoch 23: Train MSE = 0.7246 | Val MSE = 0.8391 Epoch 24: Train MSE = 0.7232 | Val MSE = 0.8585 Epoch 25: Train MSE = 0.7198 | Val MSE = 0.8758 Epoch 26: Train MSE = 0.7225 | Val MSE = 0.8609 Epoch 27: Train MSE = 0.7313 | Val MSE = 0.8423 Epoch 28: Train MSE = 0.7170 | Val MSE = 0.8404 Epoch 29: Train MSE = 0.7143 | Val MSE = 0.8295 Epoch 30: Train MSE = 0.7323 | Val MSE = 0.8930 Epoch 31: Train MSE = 0.7175 | Val MSE = 0.8645 Epoch 32: Train MSE = 0.7158 | Val MSE = 0.9109 Epoch 33: Train MSE = 0.7194 | Val MSE = 0.8513 Epoch 34: Train MSE = 0.7177 | Val MSE = 0.8409 Epoch 35: Train MSE = 0.7151 | Val MSE = 0.9039 Epoch 36: Train MSE = 0.7193 | Val MSE = 0.8874 Epoch 37: Train MSE = 0.7151 | Val MSE = 0.8886 Epoch 38: Train MSE = 0.7181 | Val MSE = 0.8565 Epoch 39: Train MSE = 0.7115 | Val MSE = 0.8742 Epoch 40: Train MSE = 0.7122 | Val MSE = 0.9150 Epoch 41: Train MSE = 0.7452 | Val MSE = 0.8346 Epoch 42: Train MSE = 0.7596 | Val MSE = 0.7501 Epoch 43: Train MSE = 0.7878 | Val MSE = 0.8221 Epoch 44: Train MSE = 0.7534 | Val MSE = 0.8296 Epoch 45: Train MSE = 0.7414 | Val MSE = 0.9046 Epoch 46: Train MSE = 0.7357 | Val MSE = 0.8727 Epoch 47: Train MSE = 0.7797 | Val MSE = 0.8792 Epoch 48: Train MSE = 0.7537 | Val MSE = 0.8038 Epoch 49: Train MSE = 0.7428 | Val MSE = 0.8976 Epoch 50: Train MSE = 0.7305 | Val MSE = 0.8743 Parameters used in the single-fit model: input_dim: 8 output_dim: 1 hidden_layers: 2 dropout: 0.00000000 lr: 0.00050000 epochs: 50 batch_size: 16 device: mps verbose: True no_tasks: 1 pred_len: 1 l2_weight: 0.00000100 hidden_dim: 32 patience: 10 min_epochs: 50 min_delta: 0.00010000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.12991513 1 day(s) RMSE : 0.21536798 1 day(s) R2 : -0.01621268 1 day(s) Pearson r : 0.38450392 1 day(s) QLIKE : 0.56875954 full horizon MAE : 0.12991513 full horizon RMSE : 0.21536798 full horizon R2 : -0.01621268 full horizon Pearson r : 0.38450392 full horizon QLIKE : 0.56875954
Test 3 task EURUSD with Vanilla LSTM without CV¶
In [ ]:
import os
save_test3_EURUSD_SLSTM_model_1_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SLSTM_model_1_task.pkl"
)
t3_EURUSD_SLSTM_results, t3_EURUSD_SLSTM_nested_results, t3_EURUSD_SLSTM_best_model, t3_EURUSD_SLSTM_best_params, _ = train_and_evaluate_model(
model_type="Simple_LSTM",
X_price=eur3_X_price,
X_time=eur3_X_time,
y=eur3_y,
no_tasks=1,
use_nested_cv=False,
flatten=False,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test3_EURUSD_SLSTM_model_1_file_path,
lr=3e-3,
epochs=50,
batch_size=8,
verbose=True,
hidden_layers=2,
time_horizon=28,
l2_weight= 1e-6,
hidden_dim=32,
patience=10,
min_epochs=30,
min_delta=1e-4,
target_mode="log",
min_epochs=10
)
Vanila LSTM with AAPL4¶
In [789]:
import os
save_test3_EURUSD_SLSTM_model_1_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SLSTM_model_1_task.pkl"
)
t3_EURUSD_SLSTM_results, t3_EURUSD_SLSTM_nested_results, t3_EURUSD_SLSTM_best_model, t3_EURUSD_SLSTM_best_params, yy = train_and_evaluate_model(
model_type="Simple_LSTM",
X_price=appl4_X_price,
X_time=appl4_X_time,
y=appl4_y,
no_tasks=1,
use_nested_cv=False,
flatten=False,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test3_EURUSD_SLSTM_model_1_file_path,
lr=1e-3,
epochs=2,
batch_size=16,
verbose=True,
hidden_layers=3,
time_horizon=60,
l2_weight= 1e-5,
hidden_dim=16,
patience=5,
min_epochs=10,
target_mode="log_mse",
)
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 60 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714204 Min value: -0.03434294798653995 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_train_core (log_mse scaled): Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.23813116832301 Min value: -2.4157720513117233 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.051920323212192546 Min value: -0.0338203458440787 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_val (log_mse scaled): Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9216334253580323 Min value: -2.1781197603632063 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.19306601030720755 Min value: -0.03421247164703902 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_test (log_mse scaled): Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.923983794527607 Min value: -2.1781197603632063 Epoch 1: Train mse = 0.8519 | Val mse = 0.4163 Epoch 2: Train mse = 0.5989 | Val mse = 0.3806 Parameters used in the single-fit model: input_dim: 1 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00100000 epochs: 2 batch_size: 16 device: mps verbose: True no_tasks: 1 pred_len: 60 l2_weight: 0.00001000 hidden_dim: 16 patience: 5 min_epochs: 10 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.40419827 1 day(s) RMSE : 8.73950258 1 day(s) R2 : -0.02255430 1 day(s) Pearson r : 0.47385780 1 day(s) QLIKE : 0.53376280 3 day(s) MAE : 2.49176327 3 day(s) RMSE : 8.99793337 3 day(s) R2 : -0.02448651 3 day(s) Pearson r : 0.06239985 3 day(s) QLIKE : 0.55133113 5 day(s) MAE : 2.50199609 5 day(s) RMSE : 9.06711009 5 day(s) R2 : -0.02882063 5 day(s) Pearson r : 0.00954652 5 day(s) QLIKE : 0.55731272 10 day(s) MAE : 2.52208578 10 day(s) RMSE : 9.15150782 10 day(s) R2 : -0.03944146 10 day(s) Pearson r : 0.00009477 10 day(s) QLIKE : 0.56692869 20 day(s) MAE : 2.57378236 20 day(s) RMSE : 9.22357582 20 day(s) R2 : -0.05138572 20 day(s) Pearson r : -0.00331941 20 day(s) QLIKE : 0.57098214 full horizon MAE : 2.71853261 full horizon RMSE : 9.36254621 full horizon R2 : -0.06982518 full horizon Pearson r : -0.00747394 full horizon QLIKE : 0.57279256 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_EURUSD_SLSTM_model_1_task.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=1.35129, max=2.36078
Test on set 5¶
In [ ]:
import os
save_test_LSTM_model_EURUSD_5_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_LSTM_model_5_task.pkl"
)
t5_EURUSD_LSTM_results, t5_EURUSD_LSTM_nested_results, t5_EURUSD_LSTM_best_model, t5_EURUSD_LSTM_best_params, yy = train_and_evaluate_model(
model_type="Simple_LSTM",
X_price=eur7_X_price,
X_time=eur7_X_time,
y=eur7_y,
no_tasks=1,
use_nested_cv=False,
flatten=False,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
y_scale_type="standard",
save_model_path=save_test_LSTM_model_EURUSD_5_file_path,
lr=5e-4,
epochs=50,
batch_size=16,
verbose=True,
time_horizon=20,
dropout=0.0,
l2_weight=1e-5,
patience=10,
min_epochs=30,
hidden_dim=32,
hidden_layers=2,
min_delta=1e-4,
single_holdout=False,
target_mode="log_mse",
baseline_feature_idx=0,
baseline_window=20
)
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.998957169490066 Min value: -3.7200073694899434 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.40793072075585246 Min value: 0.00022460106458896233 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.1249999999999998 Min value: 0.0 Checking y_val (log_mse scaled): Shape: (302, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3212202110774225 Min value: -2.9768947428361368 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4442211143925813 Min value: -0.00012220230225973657 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.4999999999999996 Min value: 0.0 Checking y_test (log_mse scaled): Shape: (757, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737162 Min value: -5.3523385822659755 Size of the X data for training (core): (2723, 60, 1) Size of the Y data for training (core): (2723, 20, 1) Epoch 1: Train mse = 0.9332 | Val mse = 1.1903 Epoch 2: Train mse = 0.8735 | Val mse = 1.3563 Epoch 3: Train mse = 0.7191 | Val mse = 1.0511 Epoch 4: Train mse = 0.5432 | Val mse = 1.2263 Epoch 5: Train mse = 0.4570 | Val mse = 1.3830 Epoch 6: Train mse = 0.4258 | Val mse = 1.4828 Epoch 7: Train mse = 0.4119 | Val mse = 1.6086 Epoch 8: Train mse = 0.4015 | Val mse = 1.5344 Epoch 9: Train mse = 0.3932 | Val mse = 1.6665 Epoch 10: Train mse = 0.3917 | Val mse = 1.9910 Epoch 11: Train mse = 0.3870 | Val mse = 1.6116 Epoch 12: Train mse = 0.3821 | Val mse = 1.7916 Epoch 13: Train mse = 0.3805 | Val mse = 2.0449 Epoch 14: Train mse = 0.3766 | Val mse = 1.6966 Epoch 15: Train mse = 0.3740 | Val mse = 1.9325 Epoch 16: Train mse = 0.3727 | Val mse = 1.8227 Epoch 17: Train mse = 0.3685 | Val mse = 1.8514 Epoch 18: Train mse = 0.3636 | Val mse = 1.8435 Epoch 19: Train mse = 0.3411 | Val mse = 1.0935 Epoch 20: Train mse = 0.2731 | Val mse = 0.8778 Epoch 21: Train mse = 0.2561 | Val mse = 0.8084 Epoch 22: Train mse = 0.2479 | Val mse = 0.9703 Epoch 23: Train mse = 0.2434 | Val mse = 0.6779 Epoch 24: Train mse = 0.2389 | Val mse = 0.7215 Epoch 25: Train mse = 0.2356 | Val mse = 1.0828 Epoch 26: Train mse = 0.2339 | Val mse = 0.9044 Epoch 27: Train mse = 0.2324 | Val mse = 0.6572 Epoch 28: Train mse = 0.2305 | Val mse = 0.8841 Epoch 29: Train mse = 0.2290 | Val mse = 0.7275 Epoch 30: Train mse = 0.2284 | Val mse = 0.8457 Epoch 31: Train mse = 0.2251 | Val mse = 0.6612 Epoch 32: Train mse = 0.2253 | Val mse = 0.6714 Epoch 33: Train mse = 0.2233 | Val mse = 0.5662 Epoch 34: Train mse = 0.2214 | Val mse = 0.7252 Epoch 35: Train mse = 0.2206 | Val mse = 0.7690 Epoch 36: Train mse = 0.2196 | Val mse = 0.8389 Epoch 37: Train mse = 0.2162 | Val mse = 0.8236 Epoch 38: Train mse = 0.2181 | Val mse = 0.8312 Epoch 39: Train mse = 0.2138 | Val mse = 0.7495 Epoch 40: Train mse = 0.2109 | Val mse = 0.8183 Epoch 41: Train mse = 0.2100 | Val mse = 0.8247 Epoch 42: Train mse = 0.2097 | Val mse = 1.0023 Epoch 43: Train mse = 0.2089 | Val mse = 1.0293 Early stopping triggered at epoch 43. Parameters used in the single-fit model: input_dim: 1 output_dim: 1 hidden_layers: 2 dropout: 0.00000000 lr: 0.00050000 epochs: 50 batch_size: 16 device: mps verbose: True no_tasks: 1 pred_len: 20 l2_weight: 0.00001000 hidden_dim: 32 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.10193652 1 day(s) RMSE : 0.18510868 1 day(s) R2 : 0.24928355 1 day(s) Pearson r : 0.50078110 1 day(s) QLIKE : 0.40834671 3 day(s) MAE : 0.10287788 3 day(s) RMSE : 0.18729410 3 day(s) R2 : 0.22552363 3 day(s) Pearson r : 0.47827404 3 day(s) QLIKE : 0.46187840 5 day(s) MAE : 0.10268911 5 day(s) RMSE : 0.18814331 5 day(s) R2 : 0.20960387 5 day(s) Pearson r : 0.46310211 5 day(s) QLIKE : 0.47671554 10 day(s) MAE : 0.10412948 10 day(s) RMSE : 0.19223949 10 day(s) R2 : 0.16693590 10 day(s) Pearson r : 0.41922865 10 day(s) QLIKE : 0.54585040 20 day(s) MAE : 0.10518209 20 day(s) RMSE : 0.19491218 20 day(s) R2 : 0.13351054 20 day(s) Pearson r : 0.38140265 20 day(s) QLIKE : 0.62345596 full horizon MAE : 0.10518209 full horizon RMSE : 0.19491218 full horizon R2 : 0.13351054 full horizon Pearson r : 0.38140265 full horizon QLIKE : 0.62345596 Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00676322, max=0.993659
Test on 9¶
In [ ]:
import os
save_test_LSTM_model_EURUSD_9_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_LSTM_model_9_task.pkl"
)
t9_EURUSD_LSTM_results, t9_EURUSD_LSTM_nested_results, t9_EURUSD_LSTM_best_model, t9_EURUSD_LSTM_best_params, yy = train_and_evaluate_model(
model_type="Simple_LSTM",
X_price=eur9_X_price,
X_time=eur9_X_time,
y=eur9_y,
no_tasks=eur9_y.shape[2],
use_nested_cv=False,
flatten=False,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
y_scale_type="standard",
save_model_path=save_test_LSTM_model_EURUSD_5_file_path,
lr=5e-4,
epochs=50,
batch_size=16,
verbose=True,
time_horizon=20,
dropout=0.0,
l2_weight=1e-5,
patience=10,
min_epochs=30,
hidden_dim=32,
hidden_layers=2,
min_delta=1e-4,
single_holdout=False,
target_mode="log_mse",
baseline_feature_idx=0,
baseline_window=20
)
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525526 Min value: -1.1318676431757393 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.2941918291474397 Min value: -3.7200073694899167 Checking X_price_val: Shape: (302, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 64331685768.90831 Min value: -1.0471963706385605 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.321220211077452 Min value: -2.97689474283611 Checking X_price_test: Shape: (757, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144347 Min value: -0.8511164778727046 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737455 Min value: -5.35233858226595 Epoch 1: Train mse = 0.6468 | Val mse = 0.5773 Epoch 2: Train mse = 0.4624 | Val mse = 0.6031 Epoch 3: Train mse = 0.4480 | Val mse = 0.6352 Epoch 4: Train mse = 0.4230 | Val mse = 0.6680 Epoch 5: Train mse = 0.3322 | Val mse = 0.7117 Epoch 6: Train mse = 0.2521 | Val mse = 0.6238 Epoch 7: Train mse = 0.2214 | Val mse = 0.5920 Epoch 8: Train mse = 0.2054 | Val mse = 0.5598 Epoch 9: Train mse = 0.1655 | Val mse = 0.4524 Epoch 10: Train mse = 0.1461 | Val mse = 0.4780 Epoch 11: Train mse = 0.1374 | Val mse = 0.4302 Epoch 12: Train mse = 0.1318 | Val mse = 0.3833 Epoch 13: Train mse = 0.1272 | Val mse = 0.4426 Epoch 14: Train mse = 0.1247 | Val mse = 0.3116 Epoch 15: Train mse = 0.1227 | Val mse = 0.3783 Epoch 16: Train mse = 0.1225 | Val mse = 0.3825 Epoch 17: Train mse = 0.1207 | Val mse = 0.4912 Epoch 18: Train mse = 0.1188 | Val mse = 0.3433 Epoch 19: Train mse = 0.1191 | Val mse = 0.3788 Epoch 20: Train mse = 0.1177 | Val mse = 0.3572 Epoch 21: Train mse = 0.1175 | Val mse = 0.3651 Epoch 22: Train mse = 0.1163 | Val mse = 0.3483 Epoch 23: Train mse = 0.1153 | Val mse = 0.4938 Epoch 24: Train mse = 0.1156 | Val mse = 0.4594 Epoch 25: Train mse = 0.1148 | Val mse = 0.4525 Epoch 26: Train mse = 0.1138 | Val mse = 0.3970 Epoch 27: Train mse = 0.1129 | Val mse = 0.3589 Epoch 28: Train mse = 0.1125 | Val mse = 0.4284 Epoch 29: Train mse = 0.1120 | Val mse = 0.4249 Epoch 30: Train mse = 0.1115 | Val mse = 0.4223 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 2 output_dim: 2 hidden_layers: 2 dropout: 0.00000000 lr: 0.00050000 epochs: 50 batch_size: 16 device: mps verbose: True no_tasks: 2 pred_len: 20 l2_weight: 0.00001000 hidden_dim: 32 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.09374090 1 day(s) RMSE : 0.18784829 1 day(s) R2 : 0.22689790 1 day(s) Pearson r : 0.50586513 1 day(s) QLIKE : 0.52450778 3 day(s) MAE : 0.09600714 3 day(s) RMSE : 0.18754187 3 day(s) R2 : 0.22347317 3 day(s) Pearson r : 0.49688448 3 day(s) QLIKE : 0.49576478 5 day(s) MAE : 0.09612463 5 day(s) RMSE : 0.18644333 5 day(s) R2 : 0.22382268 5 day(s) Pearson r : 0.49338072 5 day(s) QLIKE : 0.48746374 10 day(s) MAE : 0.09965360 10 day(s) RMSE : 0.19063069 10 day(s) R2 : 0.18082099 10 day(s) Pearson r : 0.45304345 10 day(s) QLIKE : 0.51572708 20 day(s) MAE : 0.10650661 20 day(s) RMSE : 0.19703115 20 day(s) R2 : 0.11456821 20 day(s) Pearson r : 0.39918388 20 day(s) QLIKE : 0.60811456 full horizon MAE : 0.10650661 full horizon RMSE : 0.19703115 full horizon R2 : 0.11456821 full horizon Pearson r : 0.39918388 full horizon QLIKE : 0.60811456 --- Task 2 --- 1 day(s) MAE : 0.04615278 1 day(s) RMSE : 0.06330739 1 day(s) R2 : 0.43849963 1 day(s) Pearson r : 0.81162780 1 day(s) QLIKE : 0.03394786 3 day(s) MAE : 0.04945837 3 day(s) RMSE : 0.06864449 3 day(s) R2 : 0.33814153 3 day(s) Pearson r : 0.80222461 3 day(s) QLIKE : 0.03652340 5 day(s) MAE : 0.05072982 5 day(s) RMSE : 0.07041227 5 day(s) R2 : 0.30520816 5 day(s) Pearson r : 0.80100016 5 day(s) QLIKE : 0.03782397 10 day(s) MAE : 0.05212794 10 day(s) RMSE : 0.07247720 10 day(s) R2 : 0.26692851 10 day(s) Pearson r : 0.79293533 10 day(s) QLIKE : 0.03998561 20 day(s) MAE : 0.05433336 20 day(s) RMSE : 0.07680407 20 day(s) R2 : 0.16330013 20 day(s) Pearson r : 0.76894469 20 day(s) QLIKE : 0.04424315 full horizon MAE : 0.05433336 full horizon RMSE : 0.07680407 full horizon R2 : 0.16330013 full horizon Pearson r : 0.76894469 full horizon QLIKE : 0.04424315 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_EURUSD_LSTM_model_5_task.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00451238, max=1.36377
Y test¶
In [101]:
y_true_t0 = yy[:, 0, 0]
y_pred_t0 = yy[:, 0, 1]
print(f"y_true @t=0 std={np.nanstd(y_true_t0):.6g}, max={np.nanmax(y_true_t0):.6g}, min={np.nanmin(y_true_t0):.6g}")
print(f"y_pred @t=0 std={np.nanstd(y_pred_t0):.6g}, max={np.nanmax(y_pred_t0):.6g}, min={np.nanmin(y_pred_t0):.6g}")
y_true @t=0 std=0.213643, max=2.66457, min=9.56189e-05 y_pred @t=0 std=0.149159, max=1.13892, min=0.00509328
Test 1 task with Vanilla LSTM with CV¶
In [ ]:
import os
import os
save_checkpoint_SLSTM_model_1CV_file_path = os.path.join(
root_folder,
objects_relative_path,
"checkpoint_SLSTM_model_1CV_tasks.pkl"
)
save_test_SLSTM_model_1CV_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_SLSTM_model_1CV_tasks.pkl"
)
param_grid_test_SLSTM_1CV_aapl_cv = {
"dropout": [0.2],
"lr": [3e-5],
"batch_size": [128, 256],
"epochs": [3],
"resume": [True],
"verbose": [True]
}
t1CV_SLSTM_results, t1CV_SLSTM_nested_results, t1CV_SLSTM_best_model, t1CV_SLSTM_best_params = train_and_evaluate_model(
model_type="Simple_LSTM",
X_price=aapl_X_price,
X_time=aapl_X_time,
y=aapl_y,
no_tasks=1,
use_nested_cv=True,
outer_folds=2,
inner_folds=2,
flatten=False,
merge_price_time=True,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_SLSTM_model_1CV_file_path,
lr=1e-5,
epochs=150,
batch_size=64,
verbose=True,
hidden_layers=24,
param_grid=param_grid_test_SLSTM_1CV_aapl_cv,
checkpoint_path= save_checkpoint_SLSTM_model_1CV_file_path
)
All tasks EURUSD task with Vanilla LSTM without CV¶
In [ ]:
import os
save_test_EURUSD_SLSTM_model_9_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SLSTM_model_9_task.pkl"
)
t9_EURUSD_SLSTM_results, t9_EURUSD_SLSTM_nested_results, t9_EURUSD_SLSTM_best_model, t9_EURUSD_SLSTM_best_params = train_and_evaluate_model(
model_type="Simple_LSTM",
X_price=eur2_X_price,
X_time=eur2_X_time,
y=eur2_y,
no_tasks=eur2_y.shape[2],
use_nested_cv=False,
flatten=False,
merge_price_time=True,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_EURUSD_SLSTM_model_9_file_path,
lr=1e-5,
epochs=2,
batch_size=32,
verbose=True,
hidden_layers=6,
time_horizon=3
)
OHO with parameter search - Vanila MLP - 1 task - data 3 - horizon 1¶
In [186]:
import os
save_test_OHO_SLSTM_model_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_OHO_LSTM_model.pkl"
)
param_grid = {
#"lr": [5e-5, 5e-4],
#"dropout": [0, 0.1],
#"l2_weight": [5e-5, 5e-4],
#"batch_size": [256, 512],
"hidden_layers": [2, 3],
#"hidden_dim": [64, 256]
}
t1d3_OHO_SLSTM_results, t1d3_OHO_SLSTM_nested_results, t1d3_OHO_SLSTM_best_model, t1d3_OHO_SLSTM_best_params, t1d3_OHO_SLSTM_y_data = train_and_evaluate_model(
param_grid=param_grid,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Simple_LSTM",
# data for each scenario
X_price=eur3_X_price,
X_time=eur3_X_time,
y=eur3_y,
# specify these for each data
no_tasks=1,
flatten=False,
time_horizon=28,
# these have to be adjusted for some models
patience=2,
epochs=2,
min_epochs=2,
# saving model
save_model_path=save_test_OHO_SMLP_model_file_path,
# these go into the parameter grid
lr=1e-5,
dropout=0,
l2_weight=1e-4,
batch_size=64,
hidden_layers=2,
hidden_dim=16
)
model parameter(s) from the grid will overwrite any overlapping parameters provided directly to this function Using device: mps Batch size for y: 3782 Time steps for y: 28 Features for y: 1 Single holdout: one train/test split, param search on a single train/val (early-stop) split. Using device: mps Epoch 1: Train loss = 0.2067, Val loss = 0.2756 Epoch 2: Train loss = 0.2040, Val loss = 0.2726 Using device: mps Epoch 1: Train loss = 0.4229, Val loss = 0.5290 Epoch 2: Train loss = 0.4174, Val loss = 0.5233 Best parameters found in single holdout: hidden_layers: 2 Results for the best model from sigle hold out evaluation: --- Task 1 --- 1 day(s) MAE (log-var) : 3.60940475 1 day(s) RMSE (log-var) : 3.83397606 1 day(s) R2 (log-var) : -7.39862711 1 day(s) MAE (var) : 0.18918367 1 day(s) RMSE (var) : 0.28535484 1 day(s) R2 (var) : -0.78400614 1 day(s) QLIKE (var) : 0.58450556 5 day(s) MAE (log-var) : 3.70740767 5 day(s) RMSE (log-var) : 3.92831159 5 day(s) R2 (log-var) : -7.85309290 5 day(s) MAE (var) : 0.18821502 5 day(s) RMSE (var) : 0.28320721 5 day(s) R2 (var) : -0.79093344 5 day(s) QLIKE (var) : 0.58287868 10 day(s) MAE (log-var) : 3.71086702 10 day(s) RMSE (log-var) : 3.93084647 10 day(s) R2 (log-var) : -7.88733691 10 day(s) MAE (var) : 0.18771758 10 day(s) RMSE (var) : 0.28212613 10 day(s) R2 (var) : -0.79425193 10 day(s) QLIKE (var) : 0.57899115 20 day(s) MAE (log-var) : 3.70735928 20 day(s) RMSE (log-var) : 3.92562809 20 day(s) R2 (log-var) : -7.92870530 20 day(s) MAE (var) : 0.18704653 20 day(s) RMSE (var) : 0.28075910 20 day(s) R2 (var) : -0.79786326 20 day(s) QLIKE (var) : 0.57321106 full horizon MAE (log-var) : 3.70624502 full horizon RMSE (log-var) : 3.92372897 full horizon R2 (log-var) : -7.95094110 full horizon MAE (var) : 0.18670276 full horizon RMSE (var) : 0.28010806 full horizon R2 (var) : -0.79923600 full horizon QLIKE (var) : 0.57077142 Best single-holdout model saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_OHO_SMLP_model.pkl
KAN models¶
Intall TKAN¶
In [ ]:
!pip install tkan
Vanila KAN¶
Vanila KAN architecture¶
In [123]:
import torch
import torch.nn as nn
from efficient_kan import kan
class SimpleKAN(nn.Module):
def __init__(self, input_dim, output_dim, no_tasks, hidden_layers = 2, dropout=0.0, hidden_dim = 64, knots=8, spline_power=3,):
super(SimpleKAN, self).__init__()
self.no_tasks = no_tasks
self.hidden_dim = hidden_dim
self.hidden_layers = hidden_layers
self.dropout = dropout
self.knots = knots
self.spline_power = spline_power
self.input_dim = input_dim
self.output_dim = output_dim
layers = []
layers.append(
kan.KANLinear(
in_features=self.input_dim,
out_features=self.hidden_dim,
grid_size=self.knots,
spline_order=self.spline_power,
scale_noise=0.1,
scale_base=1.0,
scale_spline=1.0,
#enable_standalone_scale_spline=True,
base_activation=torch.nn.SiLU,
grid_eps=0.02,
grid_range=[-1, 1]
))
#layers.append(nn.LayerNorm(hidden_dim))
layers.append(nn.Dropout(self.dropout))
for i in range (self.hidden_layers):
layers.append(
kan.KANLinear(
in_features=self.hidden_dim,
out_features=self.hidden_dim,
grid_size=self.knots,
spline_order=self.spline_power,
scale_noise=0.1,
scale_base=1.0,
scale_spline=1.0,
#enable_standalone_scale_spline=True,
base_activation=torch.nn.SiLU,
grid_eps=0.02,
grid_range=[-1, 1]
))
#layers.append(nn.LayerNorm(hidden_dim))
layers.append(nn.Dropout(self.dropout))
layers.append(
kan.KANLinear(
in_features=self.hidden_dim,
out_features=self.output_dim,
grid_size=self.knots,
spline_order=self.spline_power,
scale_noise=0.1,
scale_base=1.0,
scale_spline=1.0,
#enable_standalone_scale_spline=True,
base_activation=torch.nn.SiLU,
grid_eps=0.02,
grid_range=[-1, 1]
))
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
Vanila KAN Wrapper¶
In [124]:
from sklearn.base import BaseEstimator, RegressorMixin
import numpy as np
import torch
import torch.nn as nn
class SimpleKANWrapper(BaseEstimator, RegressorMixin):
def __init__(
self,
input_dim=10,
output_dim=1,
dropout=0.1,
lr=1e-3,
epochs=100,
batch_size=512,
device=None,
verbose=True,
hidden_layers=6,
no_tasks=1,
knots=12,
spline_power=3,
l2_weight=1e-4,
hidden_dim=128,
patience=10,
min_epochs=50,
min_delta=1e-4,
loss_type="gauss_nll_var",
target_is_logvar=True,
nll_eps=1e-12,
clamp_logvar_min=-20.0,
clamp_logvar_max=20.0,
student_df=5.0,
):
self.input_dim = int(input_dim)
self.output_dim = int(output_dim)
self.dropout = float(dropout)
self.lr = float(lr)
self.epochs = int(epochs)
self.batch_size = int(batch_size)
self.device = device or ("mps" if torch.backends.mps.is_available()
else ("cuda" if torch.cuda.is_available() else "cpu"))
self.verbose = bool(verbose)
self.hidden_layers = int(hidden_layers)
self.no_tasks = int(no_tasks)
self.knots = int(knots)
self.spline_power = int(spline_power)
self.l2_weight = float(l2_weight)
self.hidden_dim = int(hidden_dim)
self.patience = int(patience)
self.min_epochs = int(min_epochs)
self.min_delta = float(min_delta)
self.loss_type = str(loss_type)
self.target_is_logvar = bool(target_is_logvar)
self.nll_eps = float(nll_eps)
self.clamp_logvar_min = float(clamp_logvar_min)
self.clamp_logvar_max = float(clamp_logvar_max)
self.student_df = float(student_df)
self._build()
if self.verbose:
print(f"Using device: {self.device}")
def _build(self):
self.model = SimpleKAN(
self.input_dim,
no_tasks=self.no_tasks,
output_dim=self.output_dim,
hidden_layers=self.hidden_layers,
dropout=self.dropout,
knots=self.knots,
spline_power=self.spline_power,
hidden_dim=self.hidden_dim
).to(self.device)
decay, no_decay = [], []
for name, p in self.model.named_parameters():
if not p.requires_grad:
continue
name_l = name.lower()
if name_l.endswith("bias") or "norm" in name_l:
no_decay.append(p)
else:
decay.append(p)
self.optimizer = torch.optim.AdamW(
[{"params": decay, "weight_decay": self.l2_weight},
{"params": no_decay, "weight_decay": 0.0}],
lr=self.lr
)
self._mse = nn.MSELoss()
def _compute_loss(self, y_hat, y_true):
if self.loss_type == "mse":
return self._mse(y_hat, y_true)
z = torch.clamp(y_hat, self.clamp_logvar_min, self.clamp_logvar_max)
if self.target_is_logvar:
v = torch.exp(y_true)
else:
v = y_true
v = torch.clamp(v, min=self.nll_eps)
if self.loss_type == "gauss_nll_var":
loss = v * torch.exp(-z) + z
return loss.mean()
if self.loss_type == "student_t_nll_var":
nu = torch.tensor(self.student_df, device=z.device, dtype=z.dtype)
loss = 0.5 * (nu + 1.0) * torch.log1p(v / (nu * torch.exp(z))) + 0.5 * z
return loss.mean()
raise ValueError(f"Unknown loss_type: {self.loss_type}")
def fit(self, X, y, X_test, y_test):
if y.ndim != 2:
raise ValueError(f"y must be 2D (N, output_dim); got {y.shape}")
if y.shape[1] != self.output_dim:
raise ValueError(f"y width ({y.shape[1]}) must match output_dim ({self.output_dim})")
if self.no_tasks < 1:
raise ValueError("no_tasks must be >= 1")
if y.shape[1] % self.no_tasks != 0:
raise ValueError(f"y.shape[1] ({y.shape[1]}) must be divisible by no_tasks ({self.no_tasks})")
task_output_dim = y.shape[1] // self.no_tasks # typically T
X = torch.tensor(X, dtype=torch.float32, device=self.device)
y = torch.tensor(y, dtype=torch.float32, device=self.device)
X_val = torch.tensor(X_test, dtype=torch.float32, device=self.device)
y_val = torch.tensor(y_test, dtype=torch.float32, device=self.device)
loader = torch.utils.data.DataLoader(
torch.utils.data.TensorDataset(X, y),
batch_size=self.batch_size,
shuffle=True
)
best_val = float("inf")
best_state = None
no_imp = 0
for epoch in range(1, self.epochs + 1):
self.model.train()
running = 0.0
n_batches = 0
for xb, yb in loader:
self.optimizer.zero_grad(set_to_none=True)
preds = self.model(xb) # (N, D)
loss = self._compute_loss(preds, yb)
loss.backward()
self.optimizer.step()
running += float(loss.detach().cpu())
n_batches += 1
train_loss = running / max(1, n_batches)
self.model.eval()
with torch.no_grad():
vpreds = self.model(X_val) # (N, D)
vpreds_task0 = vpreds[:, :task_output_dim]
y_val_task0 = y_val[:, :task_output_dim]
vloss = float(self._compute_loss(vpreds_task0, y_val_task0).detach().cpu())
if self.verbose:
print(f"Epoch {epoch}: Train {self.loss_type} = {train_loss:.4f} | Val {self.loss_type} = {vloss:.4f}")
if (best_val - vloss) > self.min_delta:
best_val = vloss
no_imp = 0
best_state = {k: v.detach().cpu() for k, v in self.model.state_dict().items()}
else:
no_imp += 1
if no_imp >= self.patience and epoch >= self.min_epochs:
if self.verbose:
print(f"Early stopping triggered at epoch {epoch}.")
break
if best_state is not None:
self.model.load_state_dict(best_state)
return self
def predict(self, X):
self.model.eval()
X = torch.tensor(X, dtype=torch.float32, device=self.device)
with torch.no_grad():
preds = self.model(X)
return preds.detach().cpu().numpy()
def get_params(self, deep=True):
return {
"input_dim": self.input_dim,
"output_dim": self.output_dim,
"hidden_layers": self.hidden_layers,
"dropout": self.dropout,
"lr": self.lr,
"epochs": self.epochs,
"batch_size": self.batch_size,
"device": self.device,
"verbose": self.verbose,
"no_tasks": self.no_tasks,
"knots": self.knots,
"spline_power": self.spline_power,
"l2_weight": self.l2_weight,
"hidden_dim": self.hidden_dim,
"patience": self.patience,
"min_epochs": self.min_epochs,
"min_delta": self.min_delta,
"loss_type": self.loss_type,
"target_is_logvar": self.target_is_logvar,
"nll_eps": self.nll_eps,
"clamp_logvar_min": self.clamp_logvar_min,
"clamp_logvar_max": self.clamp_logvar_max,
"student_df": self.student_df,
}
def set_params(self, **params):
arch_keys = {
"input_dim", "output_dim", "hidden_layers", "hidden_dim",
"dropout", "no_tasks", "knots", "spline_power", "device"
}
need_rebuild = any(k in arch_keys for k in params)
for k, v in params.items():
if hasattr(self, k):
setattr(self, k, v)
if need_rebuild:
self._build()
else:
for g in self.optimizer.param_groups:
g["lr"] = self.lr
if len(self.optimizer.param_groups) > 0:
self.optimizer.param_groups[0]["weight_decay"] = self.l2_weight
return self
Tasks 1 EURUSD with Vanila KAN without CV¶
In [788]:
import os
save_test_EURUSD_SKAN_model_1_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SKAN_model_1_task.pkl"
)
t1_EURUSD_SKAN_results, t1_EURUSD_SKAN_nested_results, t1_EURUSD_SKAN_best_model, t1_EURUSD_SKAN_best_params,_ = train_and_evaluate_model(
model_type="Simple_KAN",
X_price=eur2_X_price,
X_time=eur2_X_time,
y=eur2_y,
no_tasks=1,
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
save_model_path=save_test_EURUSD_SKAN_model_1_file_path,
lr=1e-3,
epochs=50,
batch_size=512,
verbose=True,
time_horizon=28,
dropout=0,
l2_weight=1e-5,
patience=10,
min_epochs=90,
hidden_dim=128,
hidden_layers=3
)
[mode=log_var_ratio] loss_type=mse, target_is_logvar=False, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 28 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525526 Min value: -3.1812809750988515 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_var_ratio scaled): Shape: (2723, 28, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.6528709038839593 Min value: -4.601403791417997 Checking X_price_val: Shape: (302, 60, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.40892116517157 Min value: -2.633090998920803 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_var_ratio scaled): Shape: (302, 28, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.4683943468351024 Min value: -4.156376337982124 Checking X_price_test: Shape: (757, 60, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144347 Min value: -2.633090998920803 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_var_ratio scaled): Shape: (757, 28, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.7701377027111294 Min value: -4.754042124912198 Epoch 1: Train mse = 0.9924 | Val mse = 1.0072 Epoch 2: Train mse = 0.9372 | Val mse = 0.8234 Epoch 3: Train mse = 0.8682 | Val mse = 0.7570 Epoch 4: Train mse = 0.8149 | Val mse = 0.7719 Epoch 5: Train mse = 0.7645 | Val mse = 0.7058 Epoch 6: Train mse = 0.7128 | Val mse = 0.6655 Epoch 7: Train mse = 0.6795 | Val mse = 0.6905 Epoch 8: Train mse = 0.6496 | Val mse = 0.7072 Epoch 9: Train mse = 0.6231 | Val mse = 0.6724 Epoch 10: Train mse = 0.5999 | Val mse = 0.6731 Epoch 11: Train mse = 0.5658 | Val mse = 0.6215 Epoch 12: Train mse = 0.5430 | Val mse = 0.6307 Epoch 13: Train mse = 0.5225 | Val mse = 0.6007 Epoch 14: Train mse = 0.5026 | Val mse = 0.6094 Epoch 15: Train mse = 0.4805 | Val mse = 0.5838 Epoch 16: Train mse = 0.4492 | Val mse = 0.5407 Epoch 17: Train mse = 0.4152 | Val mse = 0.5286 Epoch 18: Train mse = 0.3829 | Val mse = 0.4919 Epoch 19: Train mse = 0.3443 | Val mse = 0.4798 Epoch 20: Train mse = 0.3084 | Val mse = 0.4471 Epoch 21: Train mse = 0.2696 | Val mse = 0.4048 Epoch 22: Train mse = 0.2365 | Val mse = 0.3765 Epoch 23: Train mse = 0.2097 | Val mse = 0.3457 Epoch 24: Train mse = 0.1913 | Val mse = 0.3407 Epoch 25: Train mse = 0.1800 | Val mse = 0.3334 Epoch 26: Train mse = 0.1734 | Val mse = 0.3144 Epoch 27: Train mse = 0.1671 | Val mse = 0.3215 Epoch 28: Train mse = 0.1604 | Val mse = 0.3070 Epoch 29: Train mse = 0.1563 | Val mse = 0.3100 Epoch 30: Train mse = 0.1553 | Val mse = 0.3185 Epoch 31: Train mse = 0.1559 | Val mse = 0.3018 Epoch 32: Train mse = 0.1508 | Val mse = 0.3151 Epoch 33: Train mse = 0.1468 | Val mse = 0.3051 Epoch 34: Train mse = 0.1467 | Val mse = 0.3060 Epoch 35: Train mse = 0.1430 | Val mse = 0.3042 Epoch 36: Train mse = 0.1405 | Val mse = 0.3146 Epoch 37: Train mse = 0.1393 | Val mse = 0.3062 Epoch 38: Train mse = 0.1383 | Val mse = 0.3085 Epoch 39: Train mse = 0.1379 | Val mse = 0.3094 Epoch 40: Train mse = 0.1366 | Val mse = 0.3117 Epoch 41: Train mse = 0.1334 | Val mse = 0.3116 Epoch 42: Train mse = 0.1336 | Val mse = 0.3129 Epoch 43: Train mse = 0.1335 | Val mse = 0.3193 Epoch 44: Train mse = 0.1321 | Val mse = 0.3152 Epoch 45: Train mse = 0.1296 | Val mse = 0.3200 Epoch 46: Train mse = 0.1303 | Val mse = 0.3162 Epoch 47: Train mse = 0.1307 | Val mse = 0.3096 Epoch 48: Train mse = 0.1281 | Val mse = 0.3133 Epoch 49: Train mse = 0.1285 | Val mse = 0.3206 Epoch 50: Train mse = 0.1266 | Val mse = 0.3233 Parameters used in the single-fit model: input_dim: 360 output_dim: 28 hidden_layers: 3 dropout: 0.00000000 lr: 0.00100000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 8 spline_power: 3 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 90 min_delta: 0.00010000 loss_type: mse target_is_logvar: False nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.14432599 1 day(s) RMSE : 0.23817538 1 day(s) R2 : -0.24284247 1 day(s) Pearson r : 0.25984631 1 day(s) QLIKE : 0.53811900 3 day(s) MAE : 0.15167007 3 day(s) RMSE : 0.25033049 3 day(s) R2 : -0.38352644 3 day(s) Pearson r : 0.21167605 3 day(s) QLIKE : 0.61921456 5 day(s) MAE : 0.15322999 5 day(s) RMSE : 0.25113809 5 day(s) R2 : -0.40829107 5 day(s) Pearson r : 0.21530852 5 day(s) QLIKE : 0.60976263 10 day(s) MAE : 0.16571745 10 day(s) RMSE : 0.26692486 10 day(s) R2 : -0.60609502 10 day(s) Pearson r : 0.19253859 10 day(s) QLIKE : 0.65281540 20 day(s) MAE : 0.18666096 20 day(s) RMSE : 0.30214202 20 day(s) R2 : -1.08212877 20 day(s) Pearson r : 0.16535812 20 day(s) QLIKE : 0.68918064 full horizon MAE : 0.19926700 full horizon RMSE : 0.32702853 full horizon R2 : -1.45247494 full horizon Pearson r : 0.15008655 full horizon QLIKE : 0.70562558 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_EURUSD_SKAN_model_1_task.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00310523, max=2.49799
Tasks 1 EURUSD 3 with Vanila KAN without CV¶
In [98]:
import os
save_test_EURUSD_SKAN_model_1_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SKAN_model_1_task.pkl"
)
t1_EURUSD_SKAN_results, t1_EURUSD_SKAN_nested_results, t1_EURUSD_SKAN_best_model, t1_EURUSD_SKAN_best_params, test_y_data = train_and_evaluate_model(
model_type="Simple_KAN",
X_price=eur5_X_price,
X_time=eur5_X_time,
y=eur5_y,
no_tasks=1,
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_EURUSD_SKAN_model_1_file_path,
lr=5e-3,
epochs=50,
batch_size=64,
verbose=True,
time_horizon=20,
hidden_layers = 2,
l2_weight=1e-4,
dropout=0,
hidden_dim=64,
knots=8,
spline_power=5,
min_delta=1e-3,
min_epochs=30,
patience=10,
target_mode="log_mse")
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.998957169490066 Min value: -3.7200073694899434 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.40793072075585246 Min value: 0.00022460106458896233 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_val (log_mse scaled): Shape: (302, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3212202110774225 Min value: -2.9768947428361368 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4442211143925813 Min value: -0.00012220230225973657 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_test (log_mse scaled): Shape: (757, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737162 Min value: -5.3523385822659755 Size of the X data for training (core): (2723, 60) Size of the Y data for training (core): (2723, 20) Epoch 1: Train mse = 0.9124 | Val mse = 0.8917 Epoch 2: Train mse = 0.7542 | Val mse = 0.6721 Epoch 3: Train mse = 0.5237 | Val mse = 0.5092 Epoch 4: Train mse = 0.4263 | Val mse = 0.4890 Epoch 5: Train mse = 0.4032 | Val mse = 0.4653 Epoch 6: Train mse = 0.3846 | Val mse = 0.4604 Epoch 7: Train mse = 0.3755 | Val mse = 0.4313 Epoch 8: Train mse = 0.3559 | Val mse = 0.4283 Epoch 9: Train mse = 0.3431 | Val mse = 0.4341 Epoch 10: Train mse = 0.3385 | Val mse = 0.4018 Epoch 11: Train mse = 0.3312 | Val mse = 0.3959 Epoch 12: Train mse = 0.3298 | Val mse = 0.4230 Epoch 13: Train mse = 0.3139 | Val mse = 0.3809 Epoch 14: Train mse = 0.3104 | Val mse = 0.3866 Epoch 15: Train mse = 0.3121 | Val mse = 0.3854 Epoch 16: Train mse = 0.2973 | Val mse = 0.3705 Epoch 17: Train mse = 0.2914 | Val mse = 0.3943 Epoch 18: Train mse = 0.2847 | Val mse = 0.3766 Epoch 19: Train mse = 0.2865 | Val mse = 0.3950 Epoch 20: Train mse = 0.2843 | Val mse = 0.3683 Epoch 21: Train mse = 0.2739 | Val mse = 0.3815 Epoch 22: Train mse = 0.2758 | Val mse = 0.3941 Epoch 23: Train mse = 0.2762 | Val mse = 0.3779 Epoch 24: Train mse = 0.2681 | Val mse = 0.3984 Epoch 25: Train mse = 0.2612 | Val mse = 0.3973 Epoch 26: Train mse = 0.2587 | Val mse = 0.3908 Epoch 27: Train mse = 0.2590 | Val mse = 0.3958 Epoch 28: Train mse = 0.2598 | Val mse = 0.3875 Epoch 29: Train mse = 0.2530 | Val mse = 0.4029 Epoch 30: Train mse = 0.2519 | Val mse = 0.4106 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 hidden_layers: 2 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 64 device: mps verbose: True no_tasks: 1 knots: 8 spline_power: 5 l2_weight: 0.00010000 hidden_dim: 64 patience: 10 min_epochs: 30 min_delta: 0.00100000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.08973034 1 day(s) RMSE : 0.17806049 1 day(s) R2 : 0.30536371 1 day(s) Pearson r : 0.56662343 1 day(s) QLIKE : 0.39630250 3 day(s) MAE : 0.09623278 3 day(s) RMSE : 0.18561800 3 day(s) R2 : 0.23932327 3 day(s) Pearson r : 0.51445302 3 day(s) QLIKE : 0.42344969 5 day(s) MAE : 0.09740100 5 day(s) RMSE : 0.18788993 5 day(s) R2 : 0.21173131 5 day(s) Pearson r : 0.48806846 5 day(s) QLIKE : 0.42115411 10 day(s) MAE : 0.09881226 10 day(s) RMSE : 0.19051548 10 day(s) R2 : 0.18181084 10 day(s) Pearson r : 0.45800072 10 day(s) QLIKE : 0.42620625 20 day(s) MAE : 0.10040123 20 day(s) RMSE : 0.19403940 20 day(s) R2 : 0.14125309 20 day(s) Pearson r : 0.42072977 20 day(s) QLIKE : 0.45272889 full horizon MAE : 0.10040123 full horizon RMSE : 0.19403940 full horizon R2 : 0.14125309 full horizon Pearson r : 0.42072977 full horizon QLIKE : 0.45272889 Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00326985, max=1.12003
Data test¶
In [73]:
test_y_data
y_true_t0 = test_y_data[:, 0, 0]
y_pred_t0 = test_y_data[:, 0, 1]
print(f"y_true @t=0 std={np.nanstd(y_true_t0):.6g}, max={np.nanmax(y_true_t0):.6g}, min={np.nanmin(y_true_t0):.6g}")
print(f"y_pred @t=0 std={np.nanstd(y_pred_t0):.6g}, max={np.nanmax(y_pred_t0):.6g}, min={np.nanmin(y_pred_t0):.6g}")
y_true @t=0 std=0.213643, max=2.66457, min=9.56189e-05 y_pred @t=0 std=0.160636, max=2.02879, min=0.00436556
All tasks EURUSD with Vanila KAN without CV¶
In [ ]:
import os
save_test_EURUSD_SKAN_model_all_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SKAN_model_all_task.pkl"
)
tall_EURUSD_SKAN_results, tall_EURUSD_SKAN_nested_results, tall_EURUSD_SKAN_best_model, tall_EURUSD_SKAN_best_params, _ = train_and_evaluate_model(
model_type="Simple_KAN",
X_price=eur2_X_price,
X_time=eur2_X_time,
y=eur2_y,
no_tasks=eur2_y.shape[2],
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_EURUSD_SKAN_model_all_file_path,
lr=1e-3,
epochs=50,
batch_size=512,
verbose=True,
time_horizon=28,
dropout=0,
l2_weight=1e-5,
patience=10,
min_epochs=90,
hidden_dim=64,
hidden_layers=3
)
All tasks EURUSD 3 with Vanila KAN without CV¶
In [520]:
import os
save_test_EURUSD_SKAN_model_all_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_SKAN_model_all_task.pkl"
)
tall_EURUSD_SKAN_results, tall_EURUSD_SKAN_nested_results, tall_EURUSD_SKAN_best_model, tall_EURUSD_SKAN_best_params, _ = train_and_evaluate_model(
model_type="Simple_KAN",
X_price=eur8_X_price,
X_time=eur8_X_time,
y=eur8_y,
no_tasks=eur8_y.shape[2],
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_EURUSD_SKAN_model_all_file_path,
lr=5e-3,
epochs=50,
batch_size=64,
verbose=True,
time_horizon=20,
hidden_layers = 2,
l2_weight=1e-4,
dropout=0,
hidden_dim=64,
knots=8,
spline_power=5,
min_delta=1e-3,
min_epochs=30,
patience=10,
target_mode="identity"
)
[mode=identity] loss_type=mse, target_is_logvar=False, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 7 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_train_core (identity scaled): Shape: (2723, 20, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.303189333927858 Min value: -3.212230144094019 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.40793072075585246 Min value: 0.00022460106458896233 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_val (identity scaled): Shape: (302, 20, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.453687047909759 Min value: -2.193417706702724 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4442211143925813 Min value: -0.00012220230225973657 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_test (identity scaled): Shape: (757, 20, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.179992427084127 Min value: -2.6542578890940707 Epoch 1: Train mse = 0.8935 | Val mse = 1.1678 Epoch 2: Train mse = 0.8216 | Val mse = 1.1368 Epoch 3: Train mse = 0.8091 | Val mse = 1.1400 Epoch 4: Train mse = 0.7965 | Val mse = 1.1597 Epoch 5: Train mse = 0.7815 | Val mse = 1.1431 Epoch 6: Train mse = 0.7814 | Val mse = 1.1339 Epoch 7: Train mse = 0.7695 | Val mse = 1.2004 Epoch 8: Train mse = 0.7811 | Val mse = 1.1461 Epoch 9: Train mse = 0.7851 | Val mse = 1.1259 Epoch 10: Train mse = 0.7701 | Val mse = 1.1520 Epoch 11: Train mse = 0.7738 | Val mse = 1.1583 Epoch 12: Train mse = 0.7617 | Val mse = 1.1233 Epoch 13: Train mse = 0.7534 | Val mse = 1.1241 Epoch 14: Train mse = 0.7534 | Val mse = 1.1023 Epoch 15: Train mse = 0.7634 | Val mse = 1.0819 Epoch 16: Train mse = 0.7331 | Val mse = 1.0504 Epoch 17: Train mse = 0.7584 | Val mse = 1.1469 Epoch 18: Train mse = 0.7363 | Val mse = 1.0622 Epoch 19: Train mse = 0.7214 | Val mse = 1.0880 Epoch 20: Train mse = 0.7080 | Val mse = 1.0477 Epoch 21: Train mse = 0.6964 | Val mse = 1.0666 Epoch 22: Train mse = 0.7031 | Val mse = 1.0770 Epoch 23: Train mse = 0.6799 | Val mse = 1.0654 Epoch 24: Train mse = 0.6784 | Val mse = 1.0236 Epoch 25: Train mse = 0.6815 | Val mse = 1.0134 Epoch 26: Train mse = 0.6785 | Val mse = 1.0288 Epoch 27: Train mse = 0.6578 | Val mse = 1.0382 Epoch 28: Train mse = 0.6452 | Val mse = 1.0027 Epoch 29: Train mse = 0.6562 | Val mse = 1.0303 Epoch 30: Train mse = 0.6433 | Val mse = 1.0589 Epoch 31: Train mse = 0.6293 | Val mse = 1.0603 Epoch 32: Train mse = 0.6224 | Val mse = 1.1030 Epoch 33: Train mse = 0.6246 | Val mse = 1.0724 Epoch 34: Train mse = 0.6223 | Val mse = 1.0778 Epoch 35: Train mse = 0.6039 | Val mse = 1.0897 Epoch 36: Train mse = 0.5968 | Val mse = 1.1096 Epoch 37: Train mse = 0.5991 | Val mse = 1.0801 Epoch 38: Train mse = 0.5883 | Val mse = 1.0752 Early stopping triggered at epoch 38. Parameters used in the single-fit model: input_dim: 60 output_dim: 140 hidden_layers: 2 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 64 device: mps verbose: True no_tasks: 7 knots: 8 spline_power: 5 l2_weight: 0.00010000 hidden_dim: 64 patience: 10 min_epochs: 30 min_delta: 0.00100000 loss_type: mse target_is_logvar: False nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.11254098 1 day(s) RMSE : 0.19165340 1 day(s) R2 : 0.19526031 1 day(s) Pearson r : 0.51116886 1 day(s) QLIKE : 0.42413083 3 day(s) MAE : 0.12417479 3 day(s) RMSE : 0.20267571 3 day(s) R2 : 0.09309171 3 day(s) Pearson r : 0.44920669 3 day(s) QLIKE : 0.45873868 5 day(s) MAE : 0.12644817 5 day(s) RMSE : 0.20509392 5 day(s) R2 : 0.06076814 5 day(s) Pearson r : 0.42823231 5 day(s) QLIKE : 0.45942067 10 day(s) MAE : 0.12503650 10 day(s) RMSE : 0.20685821 10 day(s) R2 : 0.03541897 10 day(s) Pearson r : 0.38596066 10 day(s) QLIKE : 0.47337746 20 day(s) MAE : 0.12285896 20 day(s) RMSE : 0.20689343 20 day(s) R2 : 0.02371019 20 day(s) Pearson r : 0.35382735 20 day(s) QLIKE : 3.40556451 full horizon MAE : 0.12285896 full horizon RMSE : 0.20689343 full horizon R2 : 0.02371019 full horizon Pearson r : 0.35382735 full horizon QLIKE : 3.40556451 --- Task 2 --- 1 day(s) MAE : 0.05315282 1 day(s) RMSE : 0.07595518 1 day(s) R2 : 0.19173075 1 day(s) Pearson r : 0.77066528 1 day(s) QLIKE : 0.05236870 3 day(s) MAE : 0.05301878 3 day(s) RMSE : 0.07587042 3 day(s) R2 : 0.19146521 3 day(s) Pearson r : 0.76953026 3 day(s) QLIKE : 0.05235153 5 day(s) MAE : 0.05343230 5 day(s) RMSE : 0.07645308 5 day(s) R2 : 0.18087905 5 day(s) Pearson r : 0.76383990 5 day(s) QLIKE : 0.05331872 10 day(s) MAE : 0.05306473 10 day(s) RMSE : 0.07677222 10 day(s) R2 : 0.17747007 10 day(s) Pearson r : 0.75707023 10 day(s) QLIKE : 0.05329293 20 day(s) MAE : 0.05500979 20 day(s) RMSE : 0.08168575 20 day(s) R2 : 0.05355829 20 day(s) Pearson r : 0.74615357 20 day(s) QLIKE : 0.05653642 full horizon MAE : 0.05500979 full horizon RMSE : 0.08168575 full horizon R2 : 0.05355829 full horizon Pearson r : 0.74615357 full horizon QLIKE : 0.05653642 --- Task 3 --- 1 day(s) MAE : 0.03587545 1 day(s) RMSE : 0.05230429 1 day(s) R2 : -0.38877528 1 day(s) Pearson r : -0.09050495 1 day(s) QLIKE : 10.56899756 3 day(s) MAE : 0.03586274 3 day(s) RMSE : 0.05203309 3 day(s) R2 : -0.37406395 3 day(s) Pearson r : -0.09311736 3 day(s) QLIKE : 10.40308294 5 day(s) MAE : 0.03597379 5 day(s) RMSE : 0.05208722 5 day(s) R2 : -0.37599864 5 day(s) Pearson r : -0.09817719 5 day(s) QLIKE : 10.40219326 10 day(s) MAE : 0.03617650 10 day(s) RMSE : 0.05224649 10 day(s) R2 : -0.38254397 10 day(s) Pearson r : -0.10476950 10 day(s) QLIKE : 24.71630485 20 day(s) MAE : 0.03619907 20 day(s) RMSE : 0.05214390 20 day(s) R2 : -0.37618254 20 day(s) Pearson r : -0.09062318 20 day(s) QLIKE : 24.04990302 full horizon MAE : 0.03619907 full horizon RMSE : 0.05214390 full horizon R2 : -0.37618254 full horizon Pearson r : -0.09062318 full horizon QLIKE : 24.04990302 --- Task 4 --- 1 day(s) MAE : 0.27765524 1 day(s) RMSE : 0.38218691 1 day(s) R2 : -0.08138959 1 day(s) Pearson r : -0.01599706 1 day(s) QLIKE : 3.34360586 3 day(s) MAE : 0.28427266 3 day(s) RMSE : 0.38173683 3 day(s) R2 : -0.07873097 3 day(s) Pearson r : -0.00282762 3 day(s) QLIKE : 3.34474135 5 day(s) MAE : 0.28301028 5 day(s) RMSE : 0.38484701 5 day(s) R2 : -0.09612974 5 day(s) Pearson r : -0.02413473 5 day(s) QLIKE : 3.34639176 10 day(s) MAE : 0.28184384 10 day(s) RMSE : 0.38573416 10 day(s) R2 : -0.10062207 10 day(s) Pearson r : -0.02143539 10 day(s) QLIKE : 3.34714894 20 day(s) MAE : 0.28193405 20 day(s) RMSE : 0.38496422 20 day(s) R2 : -0.09579254 20 day(s) Pearson r : -0.01470628 20 day(s) QLIKE : 3.34648959 full horizon MAE : 0.28193405 full horizon RMSE : 0.38496422 full horizon R2 : -0.09579254 full horizon Pearson r : -0.01470628 full horizon QLIKE : 3.34648959 --- Task 5 --- 1 day(s) MAE : 0.04347799 1 day(s) RMSE : 0.05762496 1 day(s) R2 : -0.18857014 1 day(s) Pearson r : -0.06318965 1 day(s) QLIKE : 23.18812761 3 day(s) MAE : 0.04602275 3 day(s) RMSE : 0.05879286 3 day(s) R2 : -0.23705028 3 day(s) Pearson r : -0.08333902 3 day(s) QLIKE : 23.29301832 5 day(s) MAE : 0.04632597 5 day(s) RMSE : 0.05938474 5 day(s) R2 : -0.26174065 5 day(s) Pearson r : -0.09853643 5 day(s) QLIKE : 22.78345619 10 day(s) MAE : 0.04612268 10 day(s) RMSE : 0.05952563 10 day(s) R2 : -0.26693946 10 day(s) Pearson r : -0.09596024 10 day(s) QLIKE : 22.46415206 20 day(s) MAE : 0.04644178 20 day(s) RMSE : 0.05946367 20 day(s) R2 : -0.26352270 20 day(s) Pearson r : -0.09378716 20 day(s) QLIKE : 22.51881946 full horizon MAE : 0.04644178 full horizon RMSE : 0.05946367 full horizon R2 : -0.26352270 full horizon Pearson r : -0.09378716 full horizon QLIKE : 22.51881946 --- Task 6 --- 1 day(s) MAE : 0.01088549 1 day(s) RMSE : 0.01361418 1 day(s) R2 : -0.57315512 1 day(s) Pearson r : 0.56862277 1 day(s) QLIKE : 14.77846520 3 day(s) MAE : 0.01093249 3 day(s) RMSE : 0.01366028 3 day(s) R2 : -0.59907409 3 day(s) Pearson r : 0.56393234 3 day(s) QLIKE : 14.78101499 5 day(s) MAE : 0.01103161 5 day(s) RMSE : 0.01377561 5 day(s) R2 : -0.63785847 5 day(s) Pearson r : 0.55943659 5 day(s) QLIKE : 14.79182668 10 day(s) MAE : 0.01106192 10 day(s) RMSE : 0.01384776 10 day(s) R2 : -0.68891551 10 day(s) Pearson r : 0.54625433 10 day(s) QLIKE : 14.87080609 20 day(s) MAE : 0.01114399 20 day(s) RMSE : 0.01411499 20 day(s) R2 : -0.85328251 20 day(s) Pearson r : 0.51551530 20 day(s) QLIKE : 14.97944860 full horizon MAE : 0.01114399 full horizon RMSE : 0.01411499 full horizon R2 : -0.85328251 full horizon Pearson r : 0.51551530 full horizon QLIKE : 14.97944860 --- Task 7 --- 1 day(s) MAE : 5.54079197 1 day(s) RMSE : 10.48595817 1 day(s) R2 : -70.95962683 1 day(s) Pearson r : -0.22251553 1 day(s) QLIKE : 0.25166808 3 day(s) MAE : 5.46156698 3 day(s) RMSE : 10.29001352 3 day(s) R2 : -68.27097533 3 day(s) Pearson r : -0.22603855 3 day(s) QLIKE : 7.29050890 5 day(s) MAE : 5.45213765 5 day(s) RMSE : 10.24699458 5 day(s) R2 : -67.65661087 5 day(s) Pearson r : -0.22498053 5 day(s) QLIKE : 7.97528012 10 day(s) MAE : 5.34443041 10 day(s) RMSE : 9.89196082 10 day(s) R2 : -62.84563462 10 day(s) Pearson r : -0.20824670 10 day(s) QLIKE : 8.09185362 20 day(s) MAE : 5.12326207 20 day(s) RMSE : 9.24562318 20 day(s) R2 : -54.76894332 20 day(s) Pearson r : -0.14075054 20 day(s) QLIKE : 8.01082255 full horizon MAE : 5.12326207 full horizon RMSE : 9.24562318 full horizon R2 : -54.76894332 full horizon Pearson r : -0.14075054 full horizon QLIKE : 8.01082255 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_EURUSD_SKAN_model_all_task.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=-0.00694153, max=0.977942
OHO with parameter search - Vanila KAN - 1 task - data 3 - horizon 1¶
In [ ]:
import os
save_test_OHO_SKAN_model_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_OHO_SKAN_model.pkl"
)
param_grid = {
"lr": [5e-5, 1e-3],
#"dropout": [0, 0.1],
#"l2_weight": [5e-5, 5e-4],
"batch_size": [256, 512],
"hidden_layers": [1, 2],
"hidden_dim": [16, 32],
"knots": [5, 8],
"spline_power": [2, 3]
}
t1d3_OHO_SKAN_results, t1d3_OHO_SKAN_nested_results, t1d3_OHO_SKAN_best_model, t1d3_OHO_SKAN_best_params, t1d3_OHO_SKAN_y_data = train_and_evaluate_model(
param_grid=param_grid,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Simple_KAN",
# data for each scenario
X_price=eur3_X_price,
X_time=eur3_X_time,
y=eur3_y,
# specify these for each data
no_tasks=1,
flatten=True,
time_horizon=1,
# these have to be adjusted for some models
patience=10,
epochs=100,
min_epochs=50,
# saving model
save_model_path=save_test_OHO_SMLP_model_file_path,
# these go into the parameter grid
lr=1e-5,
dropout=0,
l2_weight=1e-4,
batch_size=64,
hidden_layers=3,
hidden_dim=256,
# specific to KAN:
knots=12,
spline_power=3
)
Custom KAN¶
Custom KAN Architecture¶
In [832]:
import torch
import torch.nn as nn
import efficient_kan as kan
class HierarchicalMultiTaskKAN(nn.Module):
def __init__(
self,
input_dim: int,
output_dim: int,
no_tasks: int,
knots: int = 8,
spline_power: int = 4,
dropout: float = 0.0,
hidden_layers: int = 2,
hidden_dim: int = 64,
head_hidden_dim: int = 64,
detach_task2plus: bool = False,
detach_task1: bool = False,
detach_aux: bool | None = None,
):
super().__init__()
if detach_aux is not None:
detach_task1 = bool(detach_aux)
assert output_dim % no_tasks == 0, "output_dim must be divisible by no_tasks"
assert no_tasks > 1, "no_tasks must be > 1"
self.input_dim = input_dim
self.output_dim = output_dim
self.no_tasks = no_tasks
self.knots = knots
self.spline_power = spline_power
self.dropout = dropout
self.hidden_layers = hidden_layers
self.hidden_dim = hidden_dim
self.head_hidden_dim = head_hidden_dim
self.task_output_dim = self.output_dim // self.no_tasks
def build_trunk():
layers = []
in_f = self.input_dim
for _ in range(self.hidden_layers):
layers.append(
kan.KANLinear(
in_features=in_f,
out_features=self.hidden_dim,
grid_size=self.knots,
spline_order=self.spline_power,
scale_noise=0.1,
scale_base=1.0,
scale_spline=1.0,
base_activation=torch.nn.SiLU,
grid_eps=0.02,
grid_range=[-1, 1],
)
)
layers.append(nn.Dropout(self.dropout))
in_f = self.hidden_dim
return nn.Sequential(*layers)
self.trunk_aux = build_trunk()
self.trunk_task1 = build_trunk()
self.trunk_main = build_trunk()
# ---- head factory
def make_head(in_dim: int):
return nn.ModuleDict({
"fc1": kan.KANLinear(
in_features=in_dim,
out_features=self.head_hidden_dim,
grid_size=self.knots,
spline_order=self.spline_power,
scale_noise=0.1,
scale_base=1.0,
scale_spline=1.0,
base_activation=torch.nn.SiLU,
grid_eps=0.02,
grid_range=[-1, 1],
),
"drop1": nn.Dropout(self.dropout),
"fc2": kan.KANLinear(
in_features=self.head_hidden_dim,
out_features=self.head_hidden_dim,
grid_size=self.knots,
spline_order=self.spline_power,
scale_noise=0.1,
scale_base=1.0,
scale_spline=1.0,
base_activation=torch.nn.SiLU,
grid_eps=0.02,
grid_range=[-1, 1],
),
"drop2": nn.Dropout(self.dropout),
"out": kan.KANLinear(
in_features=self.head_hidden_dim,
out_features=self.task_output_dim,
grid_size=self.knots,
spline_order=self.spline_power,
scale_noise=0.1,
scale_base=1.0,
scale_spline=1.0,
base_activation=torch.nn.SiLU,
grid_eps=0.02,
grid_range=[-1, 1],
),
})
self.task_layers = nn.ModuleDict()
for task_id in range(2, self.no_tasks):
self.task_layers[f"task_{task_id}"] = make_head(self.hidden_dim)
in_task1 = self.hidden_dim + max(0, (self.no_tasks - 2) * self.head_hidden_dim)
self.task_layers["task_1"] = make_head(in_task1)
in_task0 = self.hidden_dim + self.head_hidden_dim
self.task_layers["task_0"] = make_head(in_task0)
def forward(
self,
x: torch.Tensor,
*,
detach_task2plus: bool = False,
detach_task1: bool = False,
detach_aux: bool | None = None,
) -> torch.Tensor:
xa = self.trunk_aux(x).contiguous()
x1 = self.trunk_task1(x).contiguous()
xm = self.trunk_main(x).contiguous()
outputs = []
embeds_2plus = []
for task_id in range(2, self.no_tasks):
head = self.task_layers[f"task_{task_id}"]
h = head["drop1"](head["fc1"](xa))
h = head["drop2"](head["fc2"](h))
embeds_2plus.append(h)
y_i = head["out"](h)
outputs.append((task_id, y_i))
if embeds_2plus:
H = torch.cat(embeds_2plus, dim=-1).contiguous()
if detach_task2plus:
H = H.detach()
t1_in = torch.cat([x1, H], dim=-1).contiguous()
else:
t1_in = x1
head1 = self.task_layers["task_1"]
h1 = head1["drop1"](head1["fc1"](t1_in))
h1 = head1["drop2"](head1["fc2"](h1))
y1 = head1["out"](h1)
t0_in = torch.cat([xm, (h1.detach() if detach_task1 else h1)], dim=-1).contiguous()
head0 = self.task_layers["task_0"]
h0 = head0["drop1"](head0["fc1"](t0_in))
h0 = head0["drop2"](head0["fc2"](h0))
y0 = head0["out"](h0)
outputs = [y0, y1] + [y for (_tid, y) in sorted(outputs, key=lambda t: t[0])]
return torch.cat(outputs, dim=-1).contiguous()
def set_trainable_parts_KAN(self, phase: int):
for p in self.parameters():
p.requires_grad = False
if phase == 0:
for p in self.parameters():
p.requires_grad = True
return
if phase == 1:
for p in self.trunk_aux.parameters():
p.requires_grad = True
for tid in range(2, self.no_tasks):
for layer in self.task_layers[f"task_{tid}"].values():
if hasattr(layer, "parameters"):
for p in layer.parameters():
p.requires_grad = True
for layer in self.task_layers["task_1"].values():
if hasattr(layer, "parameters"):
for p in layer.parameters():
p.requires_grad = True
return
if phase == 2:
for p in self.trunk_main.parameters():
p.requires_grad = True
for layer in self.task_layers["task_0"].values():
if hasattr(layer, "parameters"):
for p in layer.parameters():
p.requires_grad = True
return
if phase == 3:
for p in self.trunk_task1.parameters():
p.requires_grad = True
for layer in self.task_layers["task_1"].values():
if hasattr(layer, "parameters"):
for p in layer.parameters():
p.requires_grad = True
return
raise ValueError("phase must be one of {0,1,2,3}")
Custom KAN Wrapper¶
In [833]:
from sklearn.base import BaseEstimator, RegressorMixin
import torch
import torch.nn as nn
class TorchHierarchicalKANWrapper(BaseEstimator, RegressorMixin):
def __init__(
self,
input_dim=10,
output_dim=2,
no_tasks=2,
knots=8,
spline_power=4,
hidden_dim=256,
hidden_layers=2,
dropout=0.0,
lr=1e-3,
l2_weight=1e-4,
epochs=50,
min_epochs=50,
batch_size=512,
loss_type="gmse",
target_is_logvar=True,
nll_eps=1e-12,
clamp_logvar_min=-20.0,
clamp_logvar_max=20.0,
student_df=5.0,
loss=None,
patience=10,
min_delta=1e-4,
warmup_aux_epochs=15,
joint_epochs=15,
device=None,
verbose=True,
checkpoint_path=None,
):
assert no_tasks > 1, "Number of tasks must be > 1 for hierarchical KAN."
self.input_dim = int(input_dim)
self.output_dim = int(output_dim)
self.no_tasks = int(no_tasks)
self.knots = int(knots)
self.spline_power = int(spline_power)
self.hidden_dim = int(hidden_dim)
self.hidden_layers = int(hidden_layers)
self.dropout = float(dropout)
self.lr = float(lr)
self.l2_weight = float(l2_weight)
self.epochs = int(epochs)
self.min_epochs = int(min_epochs)
self.batch_size = int(batch_size)
self.loss_type = str(loss_type if loss is None else loss)
self.target_is_logvar = bool(target_is_logvar)
self.nll_eps = float(nll_eps)
self.clamp_logvar_min = float(clamp_logvar_min)
self.clamp_logvar_max = float(clamp_logvar_max)
self.student_df = float(student_df)
self.patience = int(patience)
self.min_delta = float(min_delta)
self.warmup_aux_epochs = int(warmup_aux_epochs)
self.joint_epochs = int(joint_epochs)
self.device = device or ("mps" if torch.backends.mps.is_available()
else ("cuda" if torch.cuda.is_available() else "cpu"))
self.verbose = bool(verbose)
self.checkpoint_path = checkpoint_path
self.model = HierarchicalMultiTaskKAN(
input_dim=self.input_dim,
output_dim=self.output_dim,
no_tasks=self.no_tasks,
knots=self.knots,
spline_power=self.spline_power,
dropout=self.dropout,
hidden_layers=self.hidden_layers,
hidden_dim=self.hidden_dim,
).to(self.device)
if self.verbose:
print(f"Using device: {self.device}")
def _adamw_param_groups(self):
decay, no_decay = [], []
for name, p in self.model.named_parameters():
if not p.requires_grad:
continue
n = name.lower()
if n.endswith("bias") or "norm" in n or "grid" in n or "knot" in n or "spline" in n:
no_decay.append(p)
else:
decay.append(p)
return [
{"params": decay, "weight_decay": self.l2_weight},
{"params": no_decay, "weight_decay": 0.0},
]
def _compute_loss(self, y_hat, y_true):
if self.loss_type == "mse":
return nn.functional.mse_loss(y_hat, y_true, reduction="mean")
z = torch.clamp(y_hat, self.clamp_logvar_min, self.clamp_logvar_max)
v = torch.exp(y_true) if self.target_is_logvar else y_true
v = torch.clamp(v, min=self.nll_eps)
if self.loss_type == "gauss_nll_var":
loss = v * torch.exp(-z) + z
return loss.mean()
if self.loss_type == "student_t_nll_var":
nu = torch.tensor(self.student_df, device=z.device, dtype=z.dtype)
loss = 0.5 * (nu + 1.0) * torch.log1p(v / (nu * torch.exp(z))) + 0.5 * z
return loss.mean()
raise ValueError(f"Unknown loss_type: {self.loss_type}")
def fit(self, X, y, X_val, y_val):
if y.ndim != 2 or y_val.ndim != 2:
raise ValueError(f"y and y_val must be 2D (N, output_dim); got y={y.shape}, y_val={y_val.shape}")
if y.shape[1] != self.output_dim or y_val.shape[1] != self.output_dim:
raise ValueError(f"Last dim of y/y_val must equal output_dim={self.output_dim} "
f"(got {y.shape[1]} and {y_val.shape[1]})")
if self.output_dim % self.no_tasks != 0:
raise ValueError(f"output_dim ({self.output_dim}) must be divisible by no_tasks ({self.no_tasks})")
task_out = self.output_dim // self.no_tasks
X = torch.as_tensor(X, dtype=torch.float32, device=self.device).contiguous()
y = torch.as_tensor(y, dtype=torch.float32, device=self.device).contiguous()
X_val = torch.as_tensor(X_val, dtype=torch.float32, device=self.device).contiguous()
y_val = torch.as_tensor(y_val, dtype=torch.float32, device=self.device).contiguous()
optimizer = torch.optim.AdamW(self._adamw_param_groups(), lr=self.lr)
dataset = torch.utils.data.TensorDataset(X, y)
loader = torch.utils.data.DataLoader(dataset, batch_size=self.batch_size, shuffle=True)
aux_end = min(self.warmup_aux_epochs, self.epochs)
joint_end = min(aux_end + self.joint_epochs, self.epochs)
best_val = float("inf")
best_state = None
epochs_no_improve = 0
for epoch in range(self.epochs):
if epoch < aux_end:
phase = 1
elif epoch < joint_end:
phase = 0
else:
phase = 2
self.model.set_trainable_parts_KAN(phase)
self.model.train()
running = 0.0
n_batches = 0
for xb, yb in loader:
optimizer.zero_grad(set_to_none=True)
preds = self.model(xb, detach_aux=(phase == 2)).contiguous()
total = 0.0
if phase == 1:
for i in range(1, self.no_tasks):
s = i * task_out; e = s + task_out
total = total + self._compute_loss(preds[:, s:e], yb[:, s:e])
elif phase == 0:
for i in range(self.no_tasks):
s = i * task_out; e = s + task_out
total = total + self._compute_loss(preds[:, s:e], yb[:, s:e])
else:
total = total + self._compute_loss(preds[:, 0:task_out], yb[:, 0:task_out])
total.backward()
optimizer.step()
running += float(total.detach().cpu())
n_batches += 1
train_loss = running / max(1, n_batches)
self.model.eval()
with torch.no_grad():
val_preds = self.model(X_val, detach_aux=True).contiguous()
val_loss = float(self._compute_loss(val_preds[:, 0:task_out], y_val[:, 0:task_out]).detach().cpu())
if self.verbose:
print(f"Epoch {epoch+1:03d} | phase={phase} | train_loss={train_loss:.4f} | val_main={val_loss:.6f}")
if (best_val - val_loss) > self.min_delta:
best_val = val_loss
epochs_no_improve = 0
best_state = {k: v.detach().cpu() for k, v in self.model.state_dict().items()}
else:
epochs_no_improve += 1
if epochs_no_improve >= self.patience and (epoch + 1) >= self.min_epochs:
if self.verbose:
print("Early stopping triggered.")
break
if best_state is not None:
self.model.load_state_dict(best_state)
return self
def predict(self, X):
self.model.eval()
X = torch.as_tensor(X, dtype=torch.float32, device=self.device).contiguous()
with torch.no_grad():
preds = self.model(X, detach_aux=True).contiguous()
return preds.detach().cpu().numpy()
def get_params(self, deep=True):
return {
"input_dim": self.input_dim,
"output_dim": self.output_dim,
"no_tasks": self.no_tasks,
"knots": self.knots,
"spline_power": self.spline_power,
"hidden_dim": self.hidden_dim,
"hidden_layers": self.hidden_layers,
"dropout": self.dropout,
"lr": self.lr,
"l2_weight": self.l2_weight,
"epochs": self.epochs,
"min_epochs": self.min_epochs,
"batch_size": self.batch_size,
"loss_type": self.loss_type,
"target_is_logvar": self.target_is_logvar,
"nll_eps": self.nll_eps,
"clamp_logvar_min": self.clamp_logvar_min,
"clamp_logvar_max": self.clamp_logvar_max,
"student_df": self.student_df,
"patience": self.patience,
"min_delta": self.min_delta,
"warmup_aux_epochs": self.warmup_aux_epochs,
"joint_epochs": self.joint_epochs,
"device": self.device,
"verbose": self.verbose,
"checkpoint_path": self.checkpoint_path,
}
def set_params(self, **params):
arch_keys = {
"input_dim", "output_dim", "no_tasks", "knots", "spline_power",
"hidden_dim", "hidden_layers", "dropout", "device"
}
need_rebuild = any(k in arch_keys for k in params)
for k, v in params.items():
if hasattr(self, k):
setattr(self, k, v)
if need_rebuild:
self.model = HierarchicalMultiTaskKAN(
input_dim=self.input_dim,
output_dim=self.output_dim,
no_tasks=self.no_tasks,
knots=self.knots,
spline_power=self.spline_power,
dropout=self.dropout,
hidden_layers=self.hidden_layers,
hidden_dim=self.hidden_dim,
).to(self.device)
else:
pass
return self
All tasks EURUSD with Custom KAN without CV¶
In [831]:
import os
save_test_EURUSD_CKAN_model_all_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_CKAN_model_all_task.pkl"
)
tall_EURUSD_CKAN_results, tall_EURUSD_CKAN_nested_results, tall_EURUSD_CKAN_best_model, tall_EURUSD_CKAN_best_params, _ = train_and_evaluate_model(
model_type="Custom_KAN",
X_price=eur8_X_price,
X_time=eur8_X_time,
y=eur8_y,
no_tasks=eur8_y.shape[2],
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_EURUSD_CKAN_model_all_file_path,
lr=5e-4,
epochs=2,
batch_size=64,
verbose=True,
time_horizon=60,
hidden_layers=3,
dropout=0.01,
l2_weight=1e-5,
hidden_dim=128,
knots=10,
spline_power=5
)
[mode=log_var_ratio] loss_type=mse, target_is_logvar=False, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 60 Features for y: 6 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525025 Min value: -0.7148707238463011 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_train_core (log_var_ratio scaled): Shape: (2723, 60, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.682091899029062 Min value: -5.349321362921576 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.408921165171427 Min value: -0.7103978753601863 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_val (log_var_ratio scaled): Shape: (302, 60, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.485281447677025 Min value: -4.167019125166536 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144182 Min value: -0.7173043382357188 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_test (log_var_ratio scaled): Shape: (757, 60, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.6809168227569553 Min value: -4.594266491108728 Epoch 001 | phase=1 | train_loss=4.7375 | val_main=1.047299 Epoch 002 | phase=1 | train_loss=4.0372 | val_main=1.047360 Parameters used in the single-fit model: input_dim: 60 output_dim: 360 no_tasks: 6 knots: 8 spline_power: 3 hidden_dim: 128 hidden_layers: 3 dropout: 0.01000000 lr: 0.00050000 l2_weight: 0.00001000 epochs: 2 min_epochs: 50 batch_size: 64 loss_type: mse target_is_logvar: False nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 patience: 10 min_delta: 0.00010000 warmup_aux_epochs: 15 joint_epochs: 15 device: mps verbose: True checkpoint_path: None Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.18542461 1 day(s) RMSE : 0.45613475 1 day(s) R2 : -3.55836048 1 day(s) Pearson r : 0.36894578 1 day(s) QLIKE : 0.73267439 3 day(s) MAE : 0.18796719 3 day(s) RMSE : 0.46124314 3 day(s) R2 : -3.69699182 3 day(s) Pearson r : 0.33581505 3 day(s) QLIKE : 0.74091243 5 day(s) MAE : 0.19054060 5 day(s) RMSE : 0.46702085 5 day(s) R2 : -3.87012169 5 day(s) Pearson r : 0.30400454 5 day(s) QLIKE : 0.75254326 10 day(s) MAE : 0.19742627 10 day(s) RMSE : 0.47963427 10 day(s) R2 : -4.18577415 10 day(s) Pearson r : 0.24793903 10 day(s) QLIKE : 0.80287870 20 day(s) MAE : 0.20246563 20 day(s) RMSE : 0.48874286 20 day(s) R2 : -4.44811411 20 day(s) Pearson r : 0.19435343 20 day(s) QLIKE : 0.82371550 full horizon MAE : 0.20479702 full horizon RMSE : 0.49740216 full horizon R2 : -4.75990181 full horizon Pearson r : 0.15550393 full horizon QLIKE : 0.83855376 --- Task 2 --- 1 day(s) MAE : 0.03456308 1 day(s) RMSE : 0.05622747 1 day(s) R2 : -0.60492414 1 day(s) Pearson r : -0.12338524 1 day(s) QLIKE : 12.64939139 3 day(s) MAE : 0.03457554 3 day(s) RMSE : 0.05622132 3 day(s) R2 : -0.60416785 3 day(s) Pearson r : -0.09645022 3 day(s) QLIKE : 12.65268035 5 day(s) MAE : 0.03453973 5 day(s) RMSE : 0.05621465 5 day(s) R2 : -0.60270920 5 day(s) Pearson r : -0.10187965 5 day(s) QLIKE : 12.64823067 10 day(s) MAE : 0.03444604 10 day(s) RMSE : 0.05619665 10 day(s) R2 : -0.59950522 10 day(s) Pearson r : -0.08481286 10 day(s) QLIKE : 12.52097634 20 day(s) MAE : 0.03439762 20 day(s) RMSE : 0.05618340 20 day(s) R2 : -0.59766259 20 day(s) Pearson r : -0.08306283 20 day(s) QLIKE : 12.40859400 full horizon MAE : 0.03366243 full horizon RMSE : 0.05590914 full horizon R2 : -0.56735527 full horizon Pearson r : -0.06119730 full horizon QLIKE : 12.46081650 --- Task 3 --- 1 day(s) MAE : 0.69104914 1 day(s) RMSE : 0.80205301 1 day(s) R2 : -3.76251876 1 day(s) Pearson r : 0.13754765 1 day(s) QLIKE : 3.88281602 3 day(s) MAE : 0.69024720 3 day(s) RMSE : 0.80033865 3 day(s) R2 : -3.74168401 3 day(s) Pearson r : 0.13668476 3 day(s) QLIKE : 3.88906552 5 day(s) MAE : 0.69191772 5 day(s) RMSE : 0.80499197 5 day(s) R2 : -3.79588564 5 day(s) Pearson r : 0.13595274 5 day(s) QLIKE : 3.86761763 10 day(s) MAE : 0.69045463 10 day(s) RMSE : 0.80432006 10 day(s) R2 : -3.78541717 10 day(s) Pearson r : 0.14297675 10 day(s) QLIKE : 3.86424572 20 day(s) MAE : 0.69010078 20 day(s) RMSE : 0.80128957 20 day(s) R2 : -3.74751752 20 day(s) Pearson r : 0.14466326 20 day(s) QLIKE : 3.86334197 full horizon MAE : 0.69325460 full horizon RMSE : 0.80403789 full horizon R2 : -3.75552235 full horizon Pearson r : 0.14150736 full horizon QLIKE : 3.91305959 --- Task 4 --- 1 day(s) MAE : 0.03319704 1 day(s) RMSE : 0.06219768 1 day(s) R2 : -0.38468825 1 day(s) Pearson r : -0.13732561 1 day(s) QLIKE : 3.86984611 3 day(s) MAE : 0.03317330 3 day(s) RMSE : 0.06219413 3 day(s) R2 : -0.38432177 3 day(s) Pearson r : -0.13536904 3 day(s) QLIKE : 3.83069314 5 day(s) MAE : 0.03316404 5 day(s) RMSE : 0.06219851 5 day(s) R2 : -0.38414101 5 day(s) Pearson r : -0.13875023 5 day(s) QLIKE : 3.82974574 10 day(s) MAE : 0.03316043 10 day(s) RMSE : 0.06221985 10 day(s) R2 : -0.38422222 10 day(s) Pearson r : -0.14857166 10 day(s) QLIKE : 3.85084722 20 day(s) MAE : 0.03314247 20 day(s) RMSE : 0.06222549 20 day(s) R2 : -0.38361853 20 day(s) Pearson r : -0.15406077 20 day(s) QLIKE : 3.83816777 full horizon MAE : 0.03270034 full horizon RMSE : 0.06214025 full horizon R2 : -0.37117286 full horizon Pearson r : -0.14979936 full horizon QLIKE : 3.77332190 --- Task 5 --- 1 day(s) MAE : 0.00512759 1 day(s) RMSE : 0.00780967 1 day(s) R2 : -0.75768034 1 day(s) Pearson r : 0.16655465 1 day(s) QLIKE : 9.36727664 3 day(s) MAE : 0.00512759 3 day(s) RMSE : 0.00780967 3 day(s) R2 : -0.75768173 3 day(s) Pearson r : 0.14878561 3 day(s) QLIKE : 9.28601575 5 day(s) MAE : 0.00512760 5 day(s) RMSE : 0.00780968 5 day(s) R2 : -0.75768539 5 day(s) Pearson r : 0.14664661 5 day(s) QLIKE : 9.15466465 10 day(s) MAE : 0.00512759 10 day(s) RMSE : 0.00780967 10 day(s) R2 : -0.75768096 10 day(s) Pearson r : 0.12695445 10 day(s) QLIKE : 9.28828002 20 day(s) MAE : 0.00513559 20 day(s) RMSE : 0.00781229 20 day(s) R2 : -0.76094048 20 day(s) Pearson r : 0.11247400 20 day(s) QLIKE : 10.14935889 full horizon MAE : 0.00524527 full horizon RMSE : 0.00787223 full horizon R2 : -0.79837706 full horizon Pearson r : 0.03871303 full horizon QLIKE : 10.36337195 --- Task 6 --- 1 day(s) MAE : 7.46225560 1 day(s) RMSE : 11.25221273 1 day(s) R2 : -81.86068673 1 day(s) Pearson r : 0.11240491 1 day(s) QLIKE : 0.25865857 3 day(s) MAE : 5.99968232 3 day(s) RMSE : 9.42503100 3 day(s) R2 : -57.11456436 3 day(s) Pearson r : 0.10444405 3 day(s) QLIKE : 0.26802778 5 day(s) MAE : 5.67413671 5 day(s) RMSE : 9.46334962 5 day(s) R2 : -57.55704044 5 day(s) Pearson r : 0.06587238 5 day(s) QLIKE : 0.25707987 10 day(s) MAE : 6.23848177 10 day(s) RMSE : 11.77317360 10 day(s) R2 : -89.43853710 10 day(s) Pearson r : 0.03615388 10 day(s) QLIKE : 0.26042923 20 day(s) MAE : 5.95342050 20 day(s) RMSE : 10.79475762 20 day(s) R2 : -75.02314433 20 day(s) Pearson r : 0.03310982 20 day(s) QLIKE : 0.26129253 full horizon MAE : 5.78031217 full horizon RMSE : 10.06567958 full horizon R2 : -65.08543874 full horizon Pearson r : 0.03109776 full horizon QLIKE : 0.27303312 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_EURUSD_CKAN_model_all_task.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00805188, max=4.70148
All tasks EURUSD 3 with Custom KAN without CV¶
In [714]:
import os
save_test_EURUSD_CKAN_model_all_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_CKAN_model_all_task.pkl"
)
tall_EURUSD_CKAN_results, tall_EURUSD_CKAN_nested_results, tall_EURUSD_CKAN_best_model, tall_EURUSD_CKAN_best_params,test_y_data = train_and_evaluate_model(
model_type="Custom_KAN",
X_price=eur8_X_price,
X_time=eur8_X_time,
y=eur8_y,
no_tasks=eur8_y.shape[2],
use_nested_cv=False,
flatten=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_EURUSD_CKAN_model_all_file_path,
lr=5e-3,
epochs=100,
batch_size=256,
verbose=True,
time_horizon=20,
hidden_layers=4,
dropout=0.00,
l2_weight=1e-6,
hidden_dim=128,
knots=20,
spline_power=7,
target_mode="log_mse",
min_epochs=50
)
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 6 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525025 Min value: -0.7148707238463011 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.7474473273136155 Min value: -3.7200073694899167 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.408921165171427 Min value: -0.7103978753601863 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_val (log_mse scaled): Shape: (302, 20, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.321220211077452 Min value: -2.97689474283611 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144182 Min value: -0.7173043382357188 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_test (log_mse scaled): Shape: (757, 20, 6) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737455 Min value: -5.35233858226595 Epoch 001 | phase=1 | train_loss=4.7726 | val_main=1.229541 Epoch 002 | phase=1 | train_loss=4.4469 | val_main=1.228539 Epoch 003 | phase=1 | train_loss=4.2562 | val_main=1.229186 Epoch 004 | phase=1 | train_loss=4.0207 | val_main=1.228739 Epoch 005 | phase=1 | train_loss=3.8035 | val_main=1.229743 Epoch 006 | phase=1 | train_loss=3.5642 | val_main=1.229248 Epoch 007 | phase=1 | train_loss=3.5649 | val_main=1.229328 Epoch 008 | phase=1 | train_loss=3.3068 | val_main=1.229107 Epoch 009 | phase=1 | train_loss=3.0165 | val_main=1.228857 Epoch 010 | phase=1 | train_loss=2.9009 | val_main=1.228606 Epoch 011 | phase=1 | train_loss=2.9138 | val_main=1.228859 Epoch 012 | phase=1 | train_loss=2.7339 | val_main=1.229179 Epoch 013 | phase=1 | train_loss=2.5972 | val_main=1.228245 Epoch 014 | phase=1 | train_loss=2.4470 | val_main=1.228035 Epoch 015 | phase=1 | train_loss=2.2927 | val_main=1.227029 Epoch 016 | phase=0 | train_loss=3.2557 | val_main=0.986940 Epoch 017 | phase=0 | train_loss=2.9604 | val_main=0.822020 Epoch 018 | phase=0 | train_loss=2.6815 | val_main=0.749213 Epoch 019 | phase=0 | train_loss=2.6514 | val_main=0.675409 Epoch 020 | phase=0 | train_loss=2.4726 | val_main=0.543417 Epoch 021 | phase=0 | train_loss=2.3284 | val_main=0.410265 Epoch 022 | phase=0 | train_loss=2.1391 | val_main=0.383468 Epoch 023 | phase=0 | train_loss=1.9481 | val_main=0.397956 Epoch 024 | phase=0 | train_loss=1.8922 | val_main=0.336679 Epoch 025 | phase=0 | train_loss=1.7886 | val_main=0.350582 Epoch 026 | phase=0 | train_loss=1.7588 | val_main=0.344210 Epoch 027 | phase=0 | train_loss=1.6948 | val_main=0.355806 Epoch 028 | phase=0 | train_loss=1.5929 | val_main=0.341606 Epoch 029 | phase=0 | train_loss=1.6388 | val_main=0.342273 Epoch 030 | phase=0 | train_loss=1.6189 | val_main=0.345956 Epoch 031 | phase=2 | train_loss=0.2069 | val_main=0.354302 Epoch 032 | phase=2 | train_loss=0.1975 | val_main=0.355053 Epoch 033 | phase=2 | train_loss=0.1908 | val_main=0.351979 Epoch 034 | phase=2 | train_loss=0.1856 | val_main=0.359418 Epoch 035 | phase=2 | train_loss=0.1786 | val_main=0.360988 Epoch 036 | phase=2 | train_loss=0.1739 | val_main=0.364167 Epoch 037 | phase=2 | train_loss=0.1693 | val_main=0.358324 Epoch 038 | phase=2 | train_loss=0.1629 | val_main=0.361569 Epoch 039 | phase=2 | train_loss=0.1574 | val_main=0.377995 Epoch 040 | phase=2 | train_loss=0.1527 | val_main=0.370100 Epoch 041 | phase=2 | train_loss=0.1480 | val_main=0.375942 Epoch 042 | phase=2 | train_loss=0.1443 | val_main=0.383986 Epoch 043 | phase=2 | train_loss=0.1399 | val_main=0.389461 Epoch 044 | phase=2 | train_loss=0.1356 | val_main=0.377532 Epoch 045 | phase=2 | train_loss=0.1333 | val_main=0.390113 Epoch 046 | phase=2 | train_loss=0.1315 | val_main=0.390065 Epoch 047 | phase=2 | train_loss=0.1291 | val_main=0.381394 Epoch 048 | phase=2 | train_loss=0.1293 | val_main=0.404602 Epoch 049 | phase=2 | train_loss=0.1304 | val_main=0.397060 Epoch 050 | phase=2 | train_loss=0.1370 | val_main=0.413439 Early stopping triggered. Parameters used in the single-fit model: input_dim: 60 output_dim: 120 no_tasks: 6 knots: 8 spline_power: 3 hidden_dim: 128 hidden_layers: 4 dropout: 0.00000000 lr: 0.00500000 l2_weight: 0.00000100 epochs: 100 min_epochs: 50 batch_size: 256 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 patience: 10 min_delta: 0.00010000 warmup_aux_epochs: 15 joint_epochs: 15 device: mps verbose: True checkpoint_path: None Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.09136260 1 day(s) RMSE : 0.17393491 1 day(s) R2 : 0.33717959 1 day(s) Pearson r : 0.59127648 1 day(s) QLIKE : 0.40121835 3 day(s) MAE : 0.09374071 3 day(s) RMSE : 0.17936543 3 day(s) R2 : 0.28970714 3 day(s) Pearson r : 0.55008661 3 day(s) QLIKE : 0.44030811 5 day(s) MAE : 0.09399874 5 day(s) RMSE : 0.18101771 5 day(s) R2 : 0.26833984 5 day(s) Pearson r : 0.52983445 5 day(s) QLIKE : 0.44940892 10 day(s) MAE : 0.09613963 10 day(s) RMSE : 0.18763360 10 day(s) R2 : 0.20637668 10 day(s) Pearson r : 0.47954500 10 day(s) QLIKE : 0.47086224 20 day(s) MAE : 0.09943945 20 day(s) RMSE : 0.19234848 20 day(s) R2 : 0.15615461 20 day(s) Pearson r : 0.43424892 20 day(s) QLIKE : 0.49919692 full horizon MAE : 0.09943945 full horizon RMSE : 0.19234848 full horizon R2 : 0.15615461 full horizon Pearson r : 0.43424892 full horizon QLIKE : 0.49919692 --- Task 2 --- 1 day(s) MAE : 0.03622647 1 day(s) RMSE : 0.06335180 1 day(s) R2 : -1.03739512 1 day(s) Pearson r : -0.05437391 1 day(s) QLIKE : 18.04056766 3 day(s) MAE : 0.03591437 3 day(s) RMSE : 0.06036139 3 day(s) R2 : -0.84912485 3 day(s) Pearson r : -0.06309015 3 day(s) QLIKE : 18.23287484 5 day(s) MAE : 0.03784409 5 day(s) RMSE : 0.09981290 5 day(s) R2 : -4.05276416 5 day(s) Pearson r : -0.03935359 5 day(s) QLIKE : 18.30068912 10 day(s) MAE : 0.03767041 10 day(s) RMSE : 0.08765311 10 day(s) R2 : -2.89134231 10 day(s) Pearson r : -0.04813478 10 day(s) QLIKE : 18.40840440 20 day(s) MAE : 0.04446403 20 day(s) RMSE : 0.18997778 20 day(s) R2 : -17.26731078 20 day(s) Pearson r : -0.04160523 20 day(s) QLIKE : 18.67206182 full horizon MAE : 0.04446403 full horizon RMSE : 0.18997778 full horizon R2 : -17.26731078 full horizon Pearson r : -0.04160523 full horizon QLIKE : 18.67206182 --- Task 3 --- 1 day(s) MAE : 0.58053479 1 day(s) RMSE : 0.69206814 1 day(s) R2 : -2.54591453 1 day(s) Pearson r : 0.06981244 1 day(s) QLIKE : 10.25943273 3 day(s) MAE : 0.59120694 3 day(s) RMSE : 0.70432404 3 day(s) R2 : -2.67223147 3 day(s) Pearson r : 0.04661128 3 day(s) QLIKE : 10.32903089 5 day(s) MAE : 0.58823040 5 day(s) RMSE : 0.70549868 5 day(s) R2 : -2.68364808 5 day(s) Pearson r : 0.03798989 5 day(s) QLIKE : 10.77718823 10 day(s) MAE : 0.60552771 10 day(s) RMSE : 0.75233869 10 day(s) R2 : -3.18686353 10 day(s) Pearson r : -0.00140290 10 day(s) QLIKE : 10.93415770 20 day(s) MAE : 0.60521273 20 day(s) RMSE : 0.74937638 20 day(s) R2 : -3.15228925 20 day(s) Pearson r : -0.02209214 20 day(s) QLIKE : 10.62201733 full horizon MAE : 0.60521273 full horizon RMSE : 0.74937638 full horizon R2 : -3.15228925 full horizon Pearson r : -0.02209214 full horizon QLIKE : 10.62201733 --- Task 4 --- 1 day(s) MAE : 0.03495275 1 day(s) RMSE : 0.05977991 1 day(s) R2 : -0.27912815 1 day(s) Pearson r : -0.07340220 1 day(s) QLIKE : 10.83685775 3 day(s) MAE : 0.03472426 3 day(s) RMSE : 0.05960171 3 day(s) R2 : -0.27132211 3 day(s) Pearson r : -0.06539381 3 day(s) QLIKE : 10.71148951 5 day(s) MAE : 0.03468325 5 day(s) RMSE : 0.05954163 5 day(s) R2 : -0.26841654 5 day(s) Pearson r : -0.06179265 5 day(s) QLIKE : 10.87783253 10 day(s) MAE : 0.03531393 10 day(s) RMSE : 0.05965116 10 day(s) R2 : -0.27228877 10 day(s) Pearson r : -0.06275671 10 day(s) QLIKE : 11.36560265 20 day(s) MAE : 0.03715652 20 day(s) RMSE : 0.06013314 20 day(s) R2 : -0.29213351 20 day(s) Pearson r : -0.07384959 20 day(s) QLIKE : 11.72158916 full horizon MAE : 0.03715652 full horizon RMSE : 0.06013314 full horizon R2 : -0.29213351 full horizon Pearson r : -0.07384959 full horizon QLIKE : 11.72158916 --- Task 5 --- 1 day(s) MAE : 0.00568916 1 day(s) RMSE : 0.00814743 1 day(s) R2 : -0.91300298 1 day(s) Pearson r : 0.16577720 1 day(s) QLIKE : 18.03186896 3 day(s) MAE : 0.00656486 3 day(s) RMSE : 0.00990157 3 day(s) R2 : -1.82541855 3 day(s) Pearson r : 0.15869155 3 day(s) QLIKE : 18.09475390 5 day(s) MAE : 0.00692765 5 day(s) RMSE : 0.01040188 5 day(s) R2 : -2.11815763 5 day(s) Pearson r : 0.16151852 5 day(s) QLIKE : 18.13228880 10 day(s) MAE : 0.00721463 10 day(s) RMSE : 0.01079705 10 day(s) R2 : -2.35958052 10 day(s) Pearson r : 0.15117809 10 day(s) QLIKE : 18.28974646 20 day(s) MAE : 0.00671517 20 day(s) RMSE : 0.00991624 20 day(s) R2 : -1.83714769 20 day(s) Pearson r : 0.11115134 20 day(s) QLIKE : 18.28418225 full horizon MAE : 0.00671517 full horizon RMSE : 0.00991624 full horizon R2 : -1.83714769 full horizon Pearson r : 0.11115134 full horizon QLIKE : 18.28418225 --- Task 6 --- 1 day(s) MAE : 2.03278998 1 day(s) RMSE : 6.36610094 1 day(s) R2 : -25.52282018 1 day(s) Pearson r : 0.15374982 1 day(s) QLIKE : 0.05908256 3 day(s) MAE : 2.00541216 3 day(s) RMSE : 6.23090373 3 day(s) R2 : -24.39928517 3 day(s) Pearson r : 0.15527745 3 day(s) QLIKE : 0.05951197 5 day(s) MAE : 2.01815231 5 day(s) RMSE : 6.30991590 5 day(s) R2 : -25.03373559 5 day(s) Pearson r : 0.14721703 5 day(s) QLIKE : 0.06024045 10 day(s) MAE : 2.02928520 10 day(s) RMSE : 6.45504481 10 day(s) R2 : -26.18722964 10 day(s) Pearson r : 0.14504175 10 day(s) QLIKE : 0.06040400 20 day(s) MAE : 2.04805330 20 day(s) RMSE : 6.51018634 20 day(s) R2 : -26.65074749 20 day(s) Pearson r : 0.22048868 20 day(s) QLIKE : 0.05947961 full horizon MAE : 2.04805330 full horizon RMSE : 6.51018634 full horizon R2 : -26.65074749 full horizon Pearson r : 0.22048868 full horizon QLIKE : 0.05947961 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_EURUSD_CKAN_model_all_task.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00306101, max=0.921228
Test data¶
In [571]:
test_y_data
y_true_t0 = test_y_data[:, 0, 0]
y_pred_t0 = test_y_data[:, 0, 1]
print(f"y_true @t=0 std={np.nanstd(y_true_t0):.6g}, max={np.nanmax(y_true_t0):.6g}, min={np.nanmin(y_true_t0):.6g}")
print(f"y_pred @t=0 std={np.nanstd(y_pred_t0):.6g}, max={np.nanmax(y_pred_t0):.6g}, min={np.nanmin(y_pred_t0):.6g}")
y_true @t=0 std=0.213643, max=2.66457, min=9.56189e-05 y_pred @t=0 std=0.128761, max=0.734658, min=0.0040167
OHO with parameter search - Custom KAN - all task - data 3 - horizon 1¶
In [ ]:
import os
save_test_OHO_CKAN_model_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_OHO_CKAN_model.pkl"
)
param_grid = {
"lr": [1e-4, 5e-4],
#"dropout": [0, 0.1],
#"l2_weight": [5e-5, 5e-4],
#"batch_size": [256, 512],
#"hidden_layers": [1, 2],
#"hidden_dim": [16, 32],
#"knots": [5, 8],
#"spline_power": [3, 4]
}
t1d3_OHO_CKAN_results, t1d3_OHO_CKAN_nested_results, t1d3_OHO_CKAN_best_model, t1d3_OHO_CKAN_best_params, t1d3_OHO_CKAN_y_data = train_and_evaluate_model(
param_grid=param_grid,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Custom_KAN",
# data for each scenario
X_price=eur3_X_price,
X_time=eur3_X_time,
y=eur3_y,
# specify these for each data
no_tasks=eur3_y.shape[2],
flatten=True,
time_horizon=28,
# these have to be adjusted for some models
patience=10,
epochs=100,
min_epochs=50,
# saving model
save_model_path=save_test_OHO_SMLP_model_file_path,
# these go into the parameter grid
lr=1e-3,
dropout=0,
l2_weight=1e-4,
batch_size=512,
hidden_layers=2,
hidden_dim=64,
# specific to KAN:
knots=8,
spline_power=4,
# additional parameters for Custom KAN
warmup_aux_epochs=15,
joint_epochs=15
)
LSTM KAN¶
Some common helpers¶
In [125]:
from sklearn.base import BaseEstimator, RegressorMixin
import numpy as np
import tensorflow as tf
from tensorflow import keras
class _TeacherForcingScheduler(keras.callbacks.Callback):
def __init__(self, total_epochs, start=1.0, end=0.0, verbose=0):
super().__init__()
self.total_epochs = max(1, int(total_epochs))
self.start = float(start)
self.end = float(end)
self.verbose = int(verbose)
def on_epoch_begin(self, epoch, logs=None):
p = epoch / max(1, self.total_epochs - 1)
ratio = self.start + p * (self.end - self.start)
ratio = float(np.clip(ratio, 0.0, 1.0))
if hasattr(self.model, "tf_ratio"):
self.model.tf_ratio = ratio
if hasattr(self.model, "teacher_forcing"):
self.model.teacher_forcing = (ratio > 0.0)
if self.verbose:
print(f"[TF Scheduler] epoch={epoch+1} tf_ratio={ratio:.3f} -> TF={'ON' if ratio>0 else 'OFF'}")
LSTM KAN architecture¶
In [126]:
import tensorflow as tf
import keras
from keras import layers
from tkan import TKAN
@keras.saving.register_keras_serializable(package="tkan_seq2seq")
class TKANSeq2Seq(keras.Model):
def __init__(
self,
input_dim: int,
output_dim: int,
hidden_layers: int = 2,
pred_len: int = 28,
hidden_dim: int = 64,
dropout: float = 0.0,
teacher_forcing: bool = True,
sub_kan_configs=None,
knots: int = 8,
spline_power: int = 3,
name: str = "TKANSeq2Seq",
):
super().__init__(name=name)
if sub_kan_configs is None:
sub_kan_configs = [{
"spline_order": spline_power,
"grid_size": knots,
"base_activation": "silu",
"grid_range": (-1.0, 1.0),
}]
self.input_dim = int(input_dim)
self.output_dim = int(output_dim)
self.pred_len = int(pred_len)
self.hidden_dim = hidden_dim
self.dropout = dropout
self.teacher_forcing = bool(teacher_forcing)
self.sub_kan_configs = sub_kan_configs
self.loss_tracker = keras.metrics.Mean(name="loss")
self.tf_ratio = 1.0
enc_layers = hidden_layers
dec_layers = hidden_layers
self.enc_blocks = []
for _ in range(enc_layers):
self.enc_blocks.append(TKAN(
units=self.hidden_dim,
return_sequences=True,
return_state=False,
dropout=0.0,
recurrent_dropout=0.0,
sub_kan_configs=self.sub_kan_configs,
))
self.enc_last = TKAN(
units=self.hidden_dim,
return_sequences=False,
return_state=True,
dropout=0.0,
recurrent_dropout=0.0,
sub_kan_configs=self.sub_kan_configs,
)
self.dec_in_proj = layers.Dense(self.hidden_dim, activation=None, name="dec_in_proj")
self.dec_layers = []
for _ in range(dec_layers):
self.dec_layers.append(TKAN(
units=self.hidden_dim,
return_sequences=True,
return_state=True,
dropout=0.0,
recurrent_dropout=0.0,
sub_kan_configs=self.sub_kan_configs,
))
self.enc_dropout = layers.Dropout(self.dropout, name="enc_dropout")
self.dec_dropout = layers.Dropout(self.dropout, name="dec_dropout")
self.proj = layers.Dense(self.output_dim, name="out_proj")
self.start_token = self.add_weight(
name="start_token",
shape=(1, 1, self.output_dim),
initializer="zeros",
trainable=True,
)
def _encode(self, x, training: bool):
h = x
for blk in self.enc_blocks:
h = blk(h, training=training)
if training and self.dropout > 0:
h = self.enc_dropout(h)
enc_out, *enc_states = self.enc_last(h, training=training)
return enc_out, enc_states
def _vectorized_decode(self, y_in_seq, enc_states, training: bool):
x = self.dec_in_proj(y_in_seq)
if training and self.dropout > 0:
x = self.dec_dropout(x)
for i, blk in enumerate(self.dec_layers):
if i == 0 and enc_states:
x, *dec_states = blk(x, initial_state=enc_states, training=training)
else:
x, *dec_states = blk(x, training=training)
if training and self.dropout > 0:
x = self.dec_dropout(x)
return self.proj(x)
def _autoregressive_decode(self, enc_states, batch_size, dtype):
dec_in = tf.tile(tf.cast(self.start_token, dtype), [batch_size, 1, 1])
states_per_layer = [enc_states] + [None] * (len(self.dec_layers) - 1)
preds = []
for _ in range(self.pred_len):
x = self.dec_in_proj(tf.squeeze(dec_in, axis=1))
x = tf.expand_dims(x, axis=1)
out = x
new_states = []
for blk, st in zip(self.dec_layers, states_per_layer):
if st is not None:
out, *st_new = blk(out, initial_state=st, training=False)
else:
out, *st_new = blk(out, training=False)
new_states.append(st_new)
states_per_layer = new_states
y_t = self.proj(tf.squeeze(out, axis=1))
preds.append(tf.expand_dims(y_t, 1))
dec_in = tf.expand_dims(y_t, 1)
return tf.concat(preds, axis=1)
def call(self, inputs, targets=None, training=False):
_, enc_states = self._encode(inputs, training=training)
batch = tf.shape(inputs)[0]
dtype = inputs.dtype
use_vectorized = bool(training and self.teacher_forcing and (targets is not None) and (self.tf_ratio > 0.0))
if use_vectorized:
start = tf.tile(tf.cast(self.start_token, dtype), [batch, 1, 1])
y_in = tf.concat([start, targets[:, :-1, :]], axis=1)
return self._vectorized_decode(y_in, enc_states, training=True)
else:
return self._autoregressive_decode(enc_states, batch, dtype)
@property
def metrics(self):
return [self.loss_tracker]
def _compiled_metrics_results(self):
cm = getattr(self, "compiled_metrics", None)
if cm is None:
return {}
try:
return cm.result_dict()
except Exception:
try:
names = cm.metrics_names
values = cm.result()
return {n: v for n, v in zip(names, values)}
except Exception:
return {}
def train_step(self, data):
x, y, sw = keras.utils.unpack_x_y_sample_weight(data)
with tf.GradientTape() as tape:
y_pred = self(x, targets=y, training=True)
loss = self.compute_loss(x, y, y_pred, sw, training=True)
grads = tape.gradient(loss, self.trainable_variables)
self.optimizer.apply_gradients(zip(grads, self.trainable_variables))
self.loss_tracker.update_state(loss)
self.compute_metrics(x, y, y_pred, sw)
logs = {"loss": self.loss_tracker.result()}
logs.update(self._compiled_metrics_results())
return logs
def test_step(self, data):
x, y, sw = keras.utils.unpack_x_y_sample_weight(data)
y_pred = self(x, targets=None, training=False)
val_loss = self.compute_loss(x, y, y_pred, sw, training=False)
self.loss_tracker.update_state(val_loss)
self.compute_metrics(x, y, y_pred, sw)
logs = {"loss": self.loss_tracker.result()}
logs.update(self._compiled_metrics_results())
return logs
def get_config(self):
return {
"input_dim": self.input_dim,
"output_dim": self.output_dim,
"hidden_dim": self.hidden_dim,
"pred_len": self.pred_len,
"dropout": self.dropout,
"teacher_forcing": self.teacher_forcing,
"sub_kan_configs": self.sub_kan_configs,
}
LSTM KAN wrapper¶
In [127]:
from sklearn.base import BaseEstimator, RegressorMixin
import numpy as np
import tensorflow as tf
from tensorflow import keras
class TKANSeq2SeqWrapper(BaseEstimator, RegressorMixin):
def __init__(
self,
input_dim=10,
output_dim=1,
pred_len=28,
no_tasks=1,
hidden_layers=6,
hidden_dim=64,
dropout=0.2,
knots=8,
spline_power=3,
sub_kan_configs=None,
teacher_forcing=True,
tf_ratio_start=1.0,
tf_ratio_end=0.0,
lr=1e-3,
l2_weight=1e-4,
epochs=100,
batch_size=32,
verbose=1,
patience=10,
min_delta=1e-4,
validation_split=0.0,
seed=42,
metrics=("mse",),
run_eagerly=True,
min_epochs=10,
loss_type="gauss_nll_var",
target_is_logvar=True,
nll_eps=1e-12,
clamp_logvar_min=-20.0,
clamp_logvar_max=20.0,
student_df=5.0,
):
self.input_dim = input_dim
self.output_dim = output_dim
self.pred_len = pred_len
self.hidden_layers = hidden_layers
self.hidden_dim = hidden_dim
self.dropout = dropout
self.knots = knots
self.spline_power = spline_power
self.sub_kan_configs = sub_kan_configs
self.teacher_forcing = teacher_forcing
self.tf_ratio_start = tf_ratio_start
self.tf_ratio_end = tf_ratio_end
self.lr = lr
self.l2_weight = l2_weight
self.epochs = epochs
self.batch_size = batch_size
self.verbose = verbose
self.patience = patience
self.min_delta = min_delta
self.validation_split = validation_split
self.seed = seed
self.metrics = metrics
self.run_eagerly = run_eagerly
self.min_epochs = min_epochs
self.no_tasks = no_tasks
self.loss_type = str(loss_type)
self.target_is_logvar = bool(target_is_logvar)
self.nll_eps = float(nll_eps)
self.clamp_logvar_min = float(clamp_logvar_min)
self.clamp_logvar_max = float(clamp_logvar_max)
self.student_df = float(student_df)
self.model_ = None
self.history_ = None
dev = "GPU" if tf.config.list_physical_devices("GPU") else "CPU"
print(f"Using TensorFlow device: {dev}")
def _make_loss(self):
if self.loss_type == "mse":
return keras.losses.MeanSquaredError()
def _nll_loss(y_true, y_pred):
z = tf.clip_by_value(y_pred, self.clamp_logvar_min, self.clamp_logvar_max)
v = tf.exp(y_true) if self.target_is_logvar else y_true
v = tf.maximum(v, self.nll_eps)
if self.loss_type == "gauss_nll_var":
loss = v * tf.exp(-z) + z
return tf.reduce_mean(loss)
if self.loss_type == "student_t_nll_var":
nu = tf.cast(self.student_df, z.dtype)
loss = 0.5 * (nu + 1.0) * tf.math.log1p(v / (nu * tf.exp(z))) + 0.5 * z
return tf.reduce_mean(loss)
raise ValueError(f"Unknown loss_type: {self.loss_type}")
return _nll_loss
def _build(self):
tf.keras.utils.set_random_seed(self.seed)
model = TKANSeq2Seq(
input_dim=self.input_dim,
output_dim=self.output_dim,
hidden_layers=self.hidden_layers,
pred_len=self.pred_len,
hidden_dim=self.hidden_dim,
dropout=self.dropout,
teacher_forcing=self.teacher_forcing,
sub_kan_configs=self.sub_kan_configs,
knots=self.knots,
spline_power=self.spline_power,
)
model.tf_ratio = float(self.tf_ratio_start)
opt = keras.optimizers.AdamW(learning_rate=self.lr, weight_decay=self.l2_weight)
mets = []
if self.metrics:
for m in self.metrics:
mets.append(m if callable(m) else keras.metrics.get(m))
model.compile(
optimizer=opt,
loss=self._make_loss(),
metrics=mets,
run_eagerly=bool(self.run_eagerly),
jit_compile=False,
)
return model
def fit(self, X, y, X_val=None, y_val=None):
if self.model_ is None:
self.model_ = self._build()
X = np.asarray(X, dtype=np.float32)
y = np.asarray(y, dtype=np.float32)
use_val = (X_val is not None and y_val is not None) or self.validation_split > 0
monitor_name = "val_loss" if use_val else "loss"
callbacks = [
keras.callbacks.ReduceLROnPlateau(
monitor=monitor_name, mode="min", factor=0.5, patience=5,
verbose=bool(self.verbose), min_lr=1e-7,
),
keras.callbacks.EarlyStopping(
monitor=monitor_name, mode="min", patience=self.patience, min_delta=self.min_delta,
restore_best_weights=True, verbose=bool(self.verbose),
),
keras.callbacks.TerminateOnNaN(),
]
try:
if self.teacher_forcing and (self.tf_ratio_start != self.tf_ratio_end):
callbacks.append(
_TeacherForcingScheduler(
total_epochs=self.epochs,
start=self.tf_ratio_start,
end=self.tf_ratio_end,
verbose=int(self.verbose),
)
)
except NameError:
pass
if X_val is not None and y_val is not None:
X_val = np.asarray(X_val, dtype=np.float32)
y_val = np.asarray(y_val, dtype=np.float32)
val_data = (X_val, y_val)
val_split = 0.0
else:
val_data = None
val_split = float(self.validation_split)
self.history_ = self.model_.fit(
x=X, y=y,
batch_size=self.batch_size,
epochs=self.epochs,
verbose=self.verbose,
validation_data=val_data,
validation_split=val_split,
shuffle=True,
callbacks=callbacks,
)
return self
def predict(self, X):
if self.model_ is None:
raise RuntimeError("Model is not fitted yet.")
X = np.asarray(X, dtype=np.float32)
return np.asarray(self.model_.predict(X, batch_size=self.batch_size, verbose=0))
def score(self, X, y):
if self.model_ is None:
raise RuntimeError("Model is not fitted yet.")
X = np.asarray(X, dtype=np.float32)
y = np.asarray(y, dtype=np.float32)
y_pred = self.model_.predict(X, batch_size=self.batch_size, verbose=0)
mae = float(np.mean(np.abs(y_pred - y)))
return -mae
def get_params(self, deep=True):
return {
"input_dim": self.input_dim,
"output_dim": self.output_dim,
"pred_len": self.pred_len,
"hidden_layers": self.hidden_layers,
"hidden_dim": self.hidden_dim,
"dropout": self.dropout,
"knots": self.knots,
"spline_power": self.spline_power,
"sub_kan_configs": self.sub_kan_configs,
"teacher_forcing": self.teacher_forcing,
"tf_ratio_start": self.tf_ratio_start,
"tf_ratio_end": self.tf_ratio_end,
"lr": self.lr,
"l2_weight": self.l2_weight,
"epochs": self.epochs,
"batch_size": self.batch_size,
"verbose": self.verbose,
"patience": self.patience,
"min_delta": self.min_delta,
"validation_split": self.validation_split,
"seed": self.seed,
"metrics": self.metrics,
"run_eagerly": self.run_eagerly,
"no_tasks": self.no_tasks,
"min_epochs": self.min_epochs,
"loss_type": self.loss_type,
"target_is_logvar": self.target_is_logvar,
"nll_eps": self.nll_eps,
"clamp_logvar_min": self.clamp_logvar_min,
"clamp_logvar_max": self.clamp_logvar_max,
"student_df": self.student_df,
}
def set_params(self, **params):
for k, v in params.items():
setattr(self, k, v)
self.model_ = None
return self
def __repr__(self):
return (f"TKANSeq2SeqWrapper(input_dim={self.input_dim}, output_dim={self.output_dim}, "
f"pred_len={self.pred_len}, hidden_layers={self.hidden_layers}, hidden_dim={self.hidden_dim}, "
f"dropout={self.dropout}, lr={self.lr}, l2_weight={self.l2_weight}, "
f"epochs={self.epochs}, batch_size={self.batch_size}, loss_type={self.loss_type}, "
f"target_is_logvar={self.target_is_logvar})")
Check optimizer¶
In [128]:
import tensorflow as tf
from tensorflow import keras
print("TF:", tf.__version__)
print("Keras:", keras.__version__)
print("has AdamW:", hasattr(keras.optimizers, "AdamW"))
TF: 2.20.0 Keras: 3.11.3 has AdamW: True
Test 1 task EURUSD with KAN LSTM without CV¶
In [ ]:
import os
save_test_EURUSD_KLSTM_model_1_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_KLSTM_model_1_task.pkl"
)
t1_EURUSD_KLSTM_results, t1_EURUSD_KLSTM_nested_results, t1_EURUSD_KLSTM_best_model, t1_EURUSD_KLSTM_best_params = train_and_evaluate_model(
model_type="LSTM_KAN",
X_price=eur2_X_price,
X_time=eur2_X_time,
y=eur2_y,
no_tasks=1,
use_nested_cv=False,
flatten=False,
merge_price_time=True,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_EURUSD_KLSTM_model_1_file_path,
lr=1e-5,
epochs=10,
batch_size=32,
verbose=True,
hidden_layers=6,
time_horizon=3
)
Test 3 task EURUSD with KAN LSTM without CV¶
In [185]:
import os
save_test_EURUSD_KLSTM_model_1_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_KLSTM_model_1_task.pkl"
)
t1_EURUSD_KLSTM_results, t1_EURUSD_KLSTM_nested_results, t1_EURUSD_KLSTM_best_model, t1_EURUSD_KLSTM_best_params, _ = train_and_evaluate_model(
model_type="LSTM_KAN",
X_price=eur5_X_price,
X_time=eur5_X_time,
y=eur5_y,
no_tasks=1,
use_nested_cv=False,
flatten=False,
merge_price_time=True,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_EURUSD_KLSTM_model_1_file_path,
lr=1e-3,
epochs=10,
batch_size=512,
verbose=True,
time_horizon=28,
l2_weight=1e-5,
dropout=0.0,
hidden_dim=32,
hidden_layers=2,
knots=8,
spline_power=5,
target_mode="log_mse",
)
merge_price_time=True with normalize_X=True but normalize_Time=False -> enabling normalize_Time=True for scale alignment. [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using TensorFlow device: CPU Batch size for y: 3782 Time steps for y: 28 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: 0.0 Checking y_train_core (log_mse scaled): Shape: (2723, 28, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.999757059156098 Min value: -3.719395912581499 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.40793072075585246 Min value: 0.00022460106458896233 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.1249999999999998 Min value: 0.0 Checking y_val (log_mse scaled): Shape: (302, 28, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3220010936712705 Min value: -2.976262445400759 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4442211143925813 Min value: -0.00012220230225973657 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.4999999999999996 Min value: 0.0 Checking y_test (log_mse scaled): Shape: (757, 28, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.386413592859664 Min value: -5.35177290393992 [TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON Epoch 1/10 6/6 ━━━━━━━━━━━━━━━━━━━━ 27s 4s/step - task0_mae: 0.8754 - loss: 0.0000e+00 - val_loss: 0.0000e+00 - learning_rate: 0.0010 [TF Scheduler] epoch=2 tf_ratio=0.889 -> TF=ON Epoch 2/10 6/6 ━━━━━━━━━━━━━━━━━━━━ 24s 4s/step - task0_mae: 1.0434 - loss: 0.0000e+00 - val_loss: 0.0000e+00 - learning_rate: 0.0010 [TF Scheduler] epoch=3 tf_ratio=0.778 -> TF=ON Epoch 3/10 6/6 ━━━━━━━━━━━━━━━━━━━━ 25s 4s/step - task0_mae: 1.0745 - loss: 0.0000e+00 - val_loss: 0.0000e+00 - learning_rate: 0.0010 [TF Scheduler] epoch=4 tf_ratio=0.667 -> TF=ON Epoch 4/10 6/6 ━━━━━━━━━━━━━━━━━━━━ 24s 4s/step - task0_mae: 1.1223 - loss: 0.0000e+00 - val_loss: 0.0000e+00 - learning_rate: 0.0010 [TF Scheduler] epoch=5 tf_ratio=0.556 -> TF=ON Epoch 5/10 6/6 ━━━━━━━━━━━━━━━━━━━━ 39s 7s/step - task0_mae: 1.1496 - loss: 0.0000e+00 - val_loss: 0.0000e+00 - learning_rate: 0.0010 [TF Scheduler] epoch=6 tf_ratio=0.444 -> TF=ON Epoch 6/10 6/6 ━━━━━━━━━━━━━━━━━━━━ 0s 4s/step - task0_mae: 0.6425 - loss: 0.0000e+00 Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0005000000237487257. 6/6 ━━━━━━━━━━━━━━━━━━━━ 24s 4s/step - task0_mae: 1.2422 - loss: 0.0000e+00 - val_loss: 0.0000e+00 - learning_rate: 0.0010 [TF Scheduler] epoch=7 tf_ratio=0.333 -> TF=ON Epoch 7/10 6/6 ━━━━━━━━━━━━━━━━━━━━ 24s 4s/step - task0_mae: 1.2529 - loss: 0.0000e+00 - val_loss: 0.0000e+00 - learning_rate: 5.0000e-04 [TF Scheduler] epoch=8 tf_ratio=0.222 -> TF=ON Epoch 8/10 6/6 ━━━━━━━━━━━━━━━━━━━━ 24s 4s/step - task0_mae: 1.2768 - loss: 0.0000e+00 - val_loss: 0.0000e+00 - learning_rate: 5.0000e-04 [TF Scheduler] epoch=9 tf_ratio=0.111 -> TF=ON Epoch 9/10 6/6 ━━━━━━━━━━━━━━━━━━━━ 24s 4s/step - task0_mae: 1.3005 - loss: 0.0000e+00 - val_loss: 0.0000e+00 - learning_rate: 5.0000e-04 [TF Scheduler] epoch=10 tf_ratio=0.000 -> TF=OFF Epoch 10/10 6/6 ━━━━━━━━━━━━━━━━━━━━ 28s 5s/step - task0_mae: 1.1675 - loss: 0.0000e+00 - val_loss: 0.0000e+00 - learning_rate: 5.0000e-04 Restoring model weights from the end of the best epoch: 1. Parameters used in the single-fit model: input_dim: 15 output_dim: 1 pred_len: 28 hidden_layers: 2 hidden_dim: 32 dropout: 0.00000000 knots: 8 spline_power: 3 sub_kan_configs: None teacher_forcing: True tf_ratio_start: 1.00000000 tf_ratio_end: 0.00000000 lr: 0.00100000 l2_weight: 0.00001000 epochs: 10 batch_size: 512 verbose: True patience: 10 min_delta: 0.00010000 validation_split: 0.00000000 seed: 42 metrics: ('mae',) run_eagerly: True no_tasks: 1 min_epochs: 50 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.12668956 1 day(s) RMSE : 0.22896372 1 day(s) R2 : -0.14856526 1 day(s) Pearson r : -0.04227649 1 day(s) QLIKE : 0.58526546 3 day(s) MAE : 0.12495201 3 day(s) RMSE : 0.22610730 3 day(s) R2 : -0.12872758 3 day(s) Pearson r : -0.00310846 3 day(s) QLIKE : 0.58486418 5 day(s) MAE : 0.12347921 5 day(s) RMSE : 0.22334783 5 day(s) R2 : -0.11386038 5 day(s) Pearson r : 0.01339354 5 day(s) QLIKE : 0.58250906 10 day(s) MAE : 0.12193738 10 day(s) RMSE : 0.21978296 10 day(s) R2 : -0.08888299 10 day(s) Pearson r : -0.00494824 10 day(s) QLIKE : 0.58367020 20 day(s) MAE : 0.12049498 20 day(s) RMSE : 0.21627614 20 day(s) R2 : -0.06684808 20 day(s) Pearson r : 0.00091059 20 day(s) QLIKE : 0.57910560 full horizon MAE : 0.11990987 full horizon RMSE : 0.21490506 full horizon R2 : -0.05907460 full horizon Pearson r : -0.00068505 full horizon QLIKE : 0.57684500 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_EURUSD_KLSTM_model_1_task.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.108293, max=0.167544
All tasks EURUSD task with KAN LSTM without CV¶
In [ ]:
import os
save_test_EURUSD_KLSTM_model_all_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_KLSTM_model_all_task.pkl"
)
tall_EURUSD_KLSTM_results, tall_EURUSD_KLSTM_nested_results, tall_EURUSD_KLSTM_best_model, tall_EURUSD_KLSTM_best_params, _ = train_and_evaluate_model(
model_type="LSTM_KAN",
X_price=eur2_X_price,
X_time=eur2_X_time,
y=eur2_y,
no_tasks=eur2_y.shape[2],
use_nested_cv=False,
flatten=False,
merge_price_time=True,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_EURUSD_KLSTM_model_all_file_path,
lr=1e-5,
epochs=10,
batch_size=32,
verbose=True,
hidden_layers=6,
time_horizon=3,
dropout=0.0,
l2_weight=8e-5
)
OHO with parameter search - KAN LSTM - all task - data 3 - horizon 1¶
In [196]:
import os
save_test_OHO_LSTM_KAN_model_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_OHO_LSTM_KAN_model.pkl"
)
param_grid = {
"lr": [1e-4, 5e-4],
#"dropout": [0, 0.1],
#"l2_weight": [5e-5, 5e-4],
#"batch_size": [256, 512],
#"hidden_layers": [1, 2],
#"hidden_dim": [16, 32],
#"knots": [5, 8],
#"spline_power": [3, 4]
}
t1d3_OHO_LSTM_KAN_results, t1d3_OHO_LSTM_KAN_nested_results, t1d3_OHO_LSTM_KAN_best_model, t1d3_OHO_LSTM_KAN_best_params, t1d3_OHO_LSTM_KAN_y_data = train_and_evaluate_model(
param_grid=param_grid,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="LSTM_KAN",
# data for each scenario
X_price=eur3_X_price,
X_time=eur3_X_time,
y=eur3_y,
# specify these for each data
no_tasks=eur3_y.shape[2],
flatten=False,
time_horizon=28,
# these have to be adjusted for some models
patience=2,
epochs=2,
min_epochs=50,
# saving model
save_model_path=save_test_OHO_LSTM_KAN_model_file_path,
# these go into the parameter grid
lr=1e-3,
dropout=0,
l2_weight=1e-4,
batch_size=512,
hidden_layers=2,
hidden_dim=64,
# specific to KAN:
knots=8,
spline_power=4
)
model parameter(s) from the grid will overwrite any overlapping parameters provided directly to this function Using TensorFlow device: CPU Batch size for y: 3782 Time steps for y: 28 Features for y: 8 Single holdout: one train/test split, param search on a single train/val (early-stop) split. Using TensorFlow device: CPU [TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON Epoch 1/2 6/6 ━━━━━━━━━━━━━━━━━━━━ 38s 6s/step - mean_absolute_error: 0.5906 - task0_mae: 1.4416 - loss: 0.4629 - val_loss: 0.5554 - learning_rate: 1.0000e-04 [TF Scheduler] epoch=2 tf_ratio=0.000 -> TF=OFF Epoch 2/2 6/6 ━━━━━━━━━━━━━━━━━━━━ 43s 7s/step - mean_absolute_error: 0.5121 - task0_mae: 1.2399 - loss: 0.4046 - val_loss: 0.4209 - learning_rate: 1.0000e-04 Using TensorFlow device: CPU [TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON Epoch 1/2 6/6 ━━━━━━━━━━━━━━━━━━━━ 34s 6s/step - mean_absolute_error: 0.3171 - task0_mae: 0.6151 - loss: 0.1605 - val_loss: 0.1526 - learning_rate: 5.0000e-04 [TF Scheduler] epoch=2 tf_ratio=0.000 -> TF=OFF Epoch 2/2 6/6 ━━━━━━━━━━━━━━━━━━━━ 41s 7s/step - mean_absolute_error: 0.1534 - task0_mae: 0.2156 - loss: 0.0450 - val_loss: 0.0399 - learning_rate: 5.0000e-04 Best parameters found in single holdout: lr: 0.0005 Results for the best model from sigle hold out evaluation: --- Task 1 --- 1 day(s) MAE (log-var) : 3.93651289 1 day(s) RMSE (log-var) : 4.14713040 1 day(s) R2 (log-var) : -8.82663641 1 day(s) MAE (var) : 0.19000056 1 day(s) RMSE (var) : 0.28589766 1 day(s) R2 (var) : -0.79079991 1 day(s) QLIKE (var) : 0.57916934 5 day(s) MAE (log-var) : 2.55176348 5 day(s) RMSE (log-var) : 2.85592445 5 day(s) R2 (log-var) : -3.67925370 5 day(s) MAE (var) : 0.17845661 5 day(s) RMSE (var) : 0.27554818 5 day(s) R2 (var) : -0.69537565 5 day(s) QLIKE (var) : 1.06056685 10 day(s) MAE (log-var) : 1.94874356 10 day(s) RMSE (log-var) : 2.29416732 10 day(s) R2 (log-var) : -2.02725954 10 day(s) MAE (var) : 0.16535712 10 day(s) RMSE (var) : 0.26422668 10 day(s) R2 (var) : -0.57380208 10 day(s) QLIKE (var) : 1.27184295 20 day(s) MAE (log-var) : 1.52568635 20 day(s) RMSE (log-var) : 1.88862504 20 day(s) R2 (log-var) : -1.06662496 20 day(s) MAE (var) : 0.15141187 20 day(s) RMSE (var) : 0.25169113 20 day(s) R2 (var) : -0.44485672 20 day(s) QLIKE (var) : 1.30833529 full horizon MAE (log-var) : 1.38949726 full horizon RMSE (log-var) : 1.74890400 full horizon R2 (log-var) : -0.77829021 full horizon MAE (var) : 0.14618016 full horizon RMSE (var) : 0.24673757 full horizon R2 (var) : -0.39607093 full horizon QLIKE (var) : 1.25994395 --- Task 2 --- 1 day(s) MAE (log-var) : 1.80365233 1 day(s) RMSE (log-var) : 1.85142712 1 day(s) R2 (log-var) : -18.46555842 1 day(s) MAE (var) : 0.95131805 1 day(s) RMSE (var) : 1.20654478 1 day(s) R2 (var) : -1.64131612 1 day(s) QLIKE (var) : 0.09891012 5 day(s) MAE (log-var) : 0.86759052 5 day(s) RMSE (log-var) : 1.08020955 5 day(s) R2 (log-var) : -5.68971925 5 day(s) MAE (var) : 0.63248601 5 day(s) RMSE (var) : 0.98264787 5 day(s) R2 (var) : -0.76586653 5 day(s) QLIKE (var) : 0.27586387 10 day(s) MAE (log-var) : 0.58529117 10 day(s) RMSE (log-var) : 0.82017658 10 day(s) R2 (log-var) : -2.85904176 10 day(s) MAE (var) : 0.48841831 10 day(s) RMSE (var) : 0.87660320 10 day(s) R2 (var) : -0.40756332 10 day(s) QLIKE (var) : 0.29418165 20 day(s) MAE (log-var) : 0.44162949 20 day(s) RMSE (log-var) : 0.65131334 20 day(s) R2 (log-var) : -1.42378039 20 day(s) MAE (var) : 0.41383111 20 day(s) RMSE (var) : 0.81424810 20 day(s) R2 (var) : -0.21438597 20 day(s) QLIKE (var) : 0.24546811 full horizon MAE (log-var) : 0.40146950 full horizon RMSE (log-var) : 0.59498486 full horizon R2 (log-var) : -1.01122624 full horizon MAE (var) : 0.39369370 full horizon RMSE (var) : 0.79655456 full horizon R2 (var) : -0.15852655 full horizon QLIKE (var) : 0.21697894 --- Task 3 --- 1 day(s) MAE (log-var) : 0.97092714 1 day(s) RMSE (log-var) : 1.05502547 1 day(s) R2 (log-var) : -5.44888903 1 day(s) MAE (var) : 0.12374214 1 day(s) RMSE (var) : 0.14973537 1 day(s) R2 (var) : -2.14115990 1 day(s) QLIKE (var) : 0.08853388 5 day(s) MAE (log-var) : 0.64576940 5 day(s) RMSE (log-var) : 0.78171473 5 day(s) R2 (log-var) : -2.53812826 5 day(s) MAE (var) : 0.09683277 5 day(s) RMSE (var) : 0.12843972 5 day(s) R2 (var) : -1.31183482 5 day(s) QLIKE (var) : 0.11218053 10 day(s) MAE (log-var) : 0.52067592 10 day(s) RMSE (log-var) : 0.65917843 10 day(s) R2 (log-var) : -1.50738010 10 day(s) MAE (var) : 0.08451465 10 day(s) RMSE (var) : 0.11679067 10 day(s) R2 (var) : -0.90353055 10 day(s) QLIKE (var) : 0.11757253 20 day(s) MAE (log-var) : 0.44621262 20 day(s) RMSE (log-var) : 0.57329767 20 day(s) R2 (log-var) : -0.90828395 20 day(s) MAE (var) : 0.07655790 20 day(s) RMSE (var) : 0.10768740 20 day(s) R2 (var) : -0.64486780 20 day(s) QLIKE (var) : 0.11152702 full horizon MAE (log-var) : 0.42401668 full horizon RMSE (log-var) : 0.54497061 full horizon R2 (log-var) : -0.72917501 full horizon MAE (var) : 0.07412867 full horizon RMSE (var) : 0.10462966 full horizon R2 (var) : -0.56525399 full horizon QLIKE (var) : 0.10703553 --- Task 4 --- 1 day(s) MAE (log-var) : 0.03318924 1 day(s) RMSE (log-var) : 0.05082743 1 day(s) R2 (log-var) : -0.51433180 1 day(s) MAE (var) : 0.03465350 1 day(s) RMSE (var) : 0.05373536 1 day(s) R2 (var) : -0.50271982 1 day(s) QLIKE (var) : 0.00086767 5 day(s) MAE (log-var) : 0.03295480 5 day(s) RMSE (log-var) : 0.04874052 5 day(s) R2 (log-var) : -0.39112055 5 day(s) MAE (var) : 0.03441683 5 day(s) RMSE (var) : 0.05163753 5 day(s) R2 (var) : -0.38632885 5 day(s) QLIKE (var) : 0.00087492 10 day(s) MAE (log-var) : 0.03282187 10 day(s) RMSE (log-var) : 0.04770258 10 day(s) R2 (log-var) : -0.33053036 10 day(s) MAE (var) : 0.03428216 10 day(s) RMSE (var) : 0.05059532 10 day(s) R2 (var) : -0.32902886 10 day(s) QLIKE (var) : 0.00087477 20 day(s) MAE (log-var) : 0.03275883 20 day(s) RMSE (log-var) : 0.04728500 20 day(s) R2 (log-var) : -0.30637260 20 day(s) MAE (var) : 0.03421827 20 day(s) RMSE (var) : 0.05017771 20 day(s) R2 (var) : -0.30624346 20 day(s) QLIKE (var) : 0.00087307 full horizon MAE (log-var) : 0.03273719 full horizon RMSE (log-var) : 0.04720377 full horizon R2 (log-var) : -0.30048989 full horizon MAE (var) : 0.03419606 full horizon RMSE (var) : 0.05009705 full horizon R2 (var) : -0.30070398 full horizon QLIKE (var) : 0.00087322 --- Task 5 --- 1 day(s) MAE (log-var) : 0.55275843 1 day(s) RMSE (log-var) : 0.57902098 1 day(s) R2 (log-var) : -1.48209932 1 day(s) MAE (var) : 1.02586376 1 day(s) RMSE (var) : 1.10287229 1 day(s) R2 (var) : -2.00032076 1 day(s) QLIKE (var) : 0.05634189 5 day(s) MAE (log-var) : 0.37983284 5 day(s) RMSE (log-var) : 0.43177457 5 day(s) R2 (log-var) : -0.37974825 5 day(s) MAE (var) : 0.73443925 5 day(s) RMSE (var) : 0.80693283 5 day(s) R2 (var) : -0.60533308 5 day(s) QLIKE (var) : 0.07214640 10 day(s) MAE (log-var) : 0.31151573 10 day(s) RMSE (log-var) : 0.40830946 10 day(s) R2 (log-var) : -0.23322096 10 day(s) MAE (var) : 0.59823603 10 day(s) RMSE (var) : 0.73258637 10 day(s) R2 (var) : -0.32209512 10 day(s) QLIKE (var) : 0.07332714 20 day(s) MAE (log-var) : 0.27027386 20 day(s) RMSE (log-var) : 0.40080039 20 day(s) R2 (log-var) : -0.18780149 20 day(s) MAE (var) : 0.51304923 20 day(s) RMSE (var) : 0.70045206 20 day(s) R2 (var) : -0.20788526 20 day(s) QLIKE (var) : 0.06875442 full horizon MAE (log-var) : 0.25833183 full horizon RMSE (log-var) : 0.39891947 full horizon R2 (log-var) : -0.17609724 full horizon MAE (var) : 0.48833760 full horizon RMSE (var) : 0.69161317 full horizon R2 (var) : -0.17669355 full horizon QLIKE (var) : 0.06616395 --- Task 6 --- 1 day(s) MAE (log-var) : 0.03596696 1 day(s) RMSE (log-var) : 0.06391233 1 day(s) R2 (log-var) : -0.46208614 1 day(s) MAE (var) : 0.03801219 1 day(s) RMSE (var) : 0.06848914 1 day(s) R2 (var) : -0.44387652 1 day(s) QLIKE (var) : 0.00143396 5 day(s) MAE (log-var) : 0.03351514 5 day(s) RMSE (log-var) : 0.05947228 5 day(s) R2 (log-var) : -0.26546364 5 day(s) MAE (var) : 0.03556753 5 day(s) RMSE (var) : 0.06405089 5 day(s) R2 (var) : -0.26231253 5 day(s) QLIKE (var) : 0.00145894 10 day(s) MAE (log-var) : 0.03341353 10 day(s) RMSE (log-var) : 0.05792654 10 day(s) R2 (log-var) : -0.19978405 10 day(s) MAE (var) : 0.03546976 10 day(s) RMSE (var) : 0.06249478 10 day(s) R2 (var) : -0.20102017 10 day(s) QLIKE (var) : 0.00145458 20 day(s) MAE (log-var) : 0.03302684 20 day(s) RMSE (log-var) : 0.05773608 20 day(s) R2 (log-var) : -0.19117151 20 day(s) MAE (var) : 0.03508066 20 day(s) RMSE (var) : 0.06231513 20 day(s) R2 (var) : -0.19344267 20 day(s) QLIKE (var) : 0.00144681 full horizon MAE (log-var) : 0.03284114 full horizon RMSE (log-var) : 0.05785597 full horizon R2 (log-var) : -0.19526123 full horizon MAE (var) : 0.03489332 full horizon RMSE (var) : 0.06244109 full horizon R2 (var) : -0.19746465 full horizon QLIKE (var) : 0.00144543 --- Task 7 --- 1 day(s) MAE (log-var) : 0.04759812 1 day(s) RMSE (log-var) : 0.04886986 1 day(s) R2 (log-var) : -19.27078629 1 day(s) MAE (var) : 0.04661437 1 day(s) RMSE (var) : 0.04790358 1 day(s) R2 (var) : -18.57648857 1 day(s) QLIKE (var) : 0.00006117 5 day(s) MAE (log-var) : 0.02352775 5 day(s) RMSE (log-var) : 0.02868812 5 day(s) R2 (log-var) : -6.10328262 5 day(s) MAE (var) : 0.02324642 5 day(s) RMSE (var) : 0.02828649 5 day(s) R2 (var) : -5.93874059 5 day(s) QLIKE (var) : 0.00017135 10 day(s) MAE (log-var) : 0.01597419 10 day(s) RMSE (log-var) : 0.02173750 10 day(s) R2 (log-var) : -3.16167225 10 day(s) MAE (var) : 0.01583594 10 day(s) RMSE (var) : 0.02146572 10 day(s) R2 (var) : -3.07588998 10 day(s) QLIKE (var) : 0.00018902 20 day(s) MAE (log-var) : 0.01289671 20 day(s) RMSE (log-var) : 0.01786056 20 day(s) R2 (log-var) : -1.96736162 20 day(s) MAE (var) : 0.01283933 20 day(s) RMSE (var) : 0.01769270 20 day(s) R2 (var) : -1.92091726 20 day(s) QLIKE (var) : 0.00016001 full horizon MAE (log-var) : 0.01213233 full horizon RMSE (log-var) : 0.01663233 full horizon R2 (log-var) : -1.68180270 full horizon MAE (var) : 0.01210145 full horizon RMSE (var) : 0.01650622 full horizon R2 (var) : -1.64720697 full horizon QLIKE (var) : 0.00013741 --- Task 8 --- 1 day(s) MAE (log-var) : 15.24759175 1 day(s) RMSE (log-var) : 15.31449974 1 day(s) R2 (log-var) : -152.48936511 1 day(s) MAE (var) : 447.66567997 1 day(s) RMSE (var) : 1158.67960227 1 day(s) R2 (var) : -0.17546504 1 day(s) QLIKE (var) : 1.27194453 5 day(s) MAE (log-var) : 21.67547224 5 day(s) RMSE (log-var) : 22.26225903 5 day(s) R2 (log-var) : -323.06177861 5 day(s) MAE (var) : 446.55337948 5 day(s) RMSE (var) : 1158.42842366 5 day(s) R2 (var) : -0.17453112 5 day(s) QLIKE (var) : 7.31252967 10 day(s) MAE (log-var) : 25.53451828 10 day(s) RMSE (log-var) : 26.09846510 10 day(s) R2 (log-var) : -443.42283556 10 day(s) MAE (var) : 445.44172123 10 day(s) RMSE (var) : 1158.21455403 10 day(s) R2 (var) : -0.17358786 10 day(s) QLIKE (var) : 4.84700475 20 day(s) MAE (log-var) : 29.70220325 20 day(s) RMSE (log-var) : 30.25634569 20 day(s) R2 (log-var) : -596.24563481 20 day(s) MAE (var) : 443.12924324 20 day(s) RMSE (var) : 1157.67494551 20 day(s) R2 (var) : -0.17166929 20 day(s) QLIKE (var) : 3.89911597 full horizon MAE (log-var) : 31.36066033 full horizon RMSE (log-var) : 31.85186402 full horizon R2 (log-var) : -661.53959262 full horizon MAE (var) : 441.90029645 full horizon RMSE (var) : 1157.38516751 full horizon R2 (var) : -0.17065623 full horizon QLIKE (var) : 3.12527219 Best single-holdout model saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_OHO_LSTM_KAN_model.pkl
Custom LSTM KAN¶
Custom KAN LSTM architecture¶
In [129]:
import tensorflow as tf
import keras
from keras import layers
from tkan import TKAN
@keras.saving.register_keras_serializable(package="tkan_seq2seq")
class TKANHierarchicalSeq2Seq(keras.Model):
def __init__(
self,
input_dim: int,
output_dim: int,
no_tasks: int = 3,
task_output_dim: int = 1,
hidden_layers: int = 2,
pred_len: int = 28,
hidden_dim: int = 32,
dropout: float = 0.2,
teacher_forcing: bool = True,
sub_kan_configs=None,
knots: int = 8,
spline_power: int = 3,
name: str = "TKANHierarchicalSeq2Seq",
):
super().__init__(name=name)
if sub_kan_configs is None:
sub_kan_configs = [{
"spline_order": spline_power,
"grid_size": knots,
"base_activation": "silu",
"grid_range": (-1.0, 1.0),
}]
assert output_dim == no_tasks * task_output_dim
self.loss_tracker = keras.metrics.Mean(name="loss")
self.input_dim = int(input_dim)
self.output_dim = int(output_dim)
self.no_tasks = int(no_tasks)
self.task_output_dim = int(task_output_dim)
self.pred_len = int(pred_len)
self.hidden_dim = int(hidden_dim)
self.dropout = float(dropout)
self.teacher_forcing = bool(teacher_forcing)
self.sub_kan_configs = sub_kan_configs
self.tf_ratio = 1.0
enc_layers = int(hidden_layers)
dec_layers = int(hidden_layers)
self.enc_blocks = []
for _ in range(enc_layers):
self.enc_blocks.append(TKAN(
units=self.hidden_dim,
return_sequences=True,
return_state=False,
dropout=0.0,
recurrent_dropout=0.0,
sub_kan_configs=self.sub_kan_configs,
))
self.enc_last = TKAN(
units=self.hidden_dim,
return_sequences=False,
return_state=True,
dropout=0.0,
recurrent_dropout=0.0,
sub_kan_configs=self.sub_kan_configs,
)
self.dec_in_proj = []
self.dec_layers = []
self.out_proj = []
self.start_tokens = []
for t in range(self.no_tasks):
if t == 0:
dec_in_dim = 2 * self.task_output_dim
elif t == 1:
dec_in_dim = max(1, (self.no_tasks - 2) * self.task_output_dim)
else:
dec_in_dim = self.task_output_dim
self.dec_in_proj.append(layers.Dense(self.hidden_dim, activation=None, name=f"dec{t}_in_proj"))
task_stack = []
for _ in range(dec_layers):
task_stack.append(TKAN(
units=self.hidden_dim,
return_sequences=True,
return_state=True,
dropout=0.0,
recurrent_dropout=0.0,
sub_kan_configs=self.sub_kan_configs,
))
self.dec_layers.append(task_stack)
self.out_proj.append(layers.Dense(self.task_output_dim, name=f"out_proj_task_{t}"))
if t >= 2:
self.start_tokens.append(
self.add_weight(
name=f"start_token_task_{t}",
shape=(1, 1, dec_in_dim),
initializer="zeros",
trainable=True,
)
)
else:
self.start_tokens.append(None)
self.enc_dropout = layers.Dropout(self.dropout, name="enc_dropout")
self.dec_dropout = layers.Dropout(self.dropout, name="dec_dropout")
self.enc_to_task0 = layers.Dense(self.task_output_dim, name="enc_to_task0")
def _encode(self, x, training: bool):
h = x
for blk in self.enc_blocks:
h = blk(h, training=training)
if training and self.dropout > 0.0:
h = self.enc_dropout(h)
enc_out, *enc_states = self.enc_last(h, training=training)
return enc_out, enc_states
def _vectorized_decode_task(self, task_idx, y_in_seq, enc_states, training: bool):
x = self.dec_in_proj[task_idx](y_in_seq)
if training and self.dropout > 0.0:
x = self.dec_dropout(x)
out = x
states = enc_states
for i, blk in enumerate(self.dec_layers[task_idx]):
if i == 0 and states:
out, *states = blk(out, initial_state=states, training=training)
else:
out, *states = blk(out, training=training)
if training and self.dropout > 0.0:
out = self.dec_dropout(out)
return self.out_proj[task_idx](out)
def _autoregressive_decode_aux(self, task_idx, enc_states, batch_size, dtype):
assert task_idx >= 2
if self.start_tokens[task_idx] is not None:
dec_in = tf.tile(tf.cast(self.start_tokens[task_idx], dtype), [batch_size, 1, 1])
else:
dec_in = tf.zeros([batch_size, 1, self.task_output_dim], dtype=dtype)
num_layers = len(self.dec_layers[task_idx])
zero_state = tf.nest.map_structure(lambda s: tf.zeros_like(s), enc_states)
states_per_layer = [enc_states] + [zero_state] * (num_layers - 1)
ta = tf.TensorArray(dtype=dtype, size=self.pred_len)
def cond(t, *_):
return t < self.pred_len
def body(t, dec_in, states_per_layer, ta):
x = self.dec_in_proj[task_idx](tf.squeeze(dec_in, axis=1))
x = tf.expand_dims(x, 1)
out = x
new_states = []
for blk, st in zip(self.dec_layers[task_idx], states_per_layer):
out, *st_new = blk(out, initial_state=st, training=False)
new_states.append(st_new)
y_t = self.out_proj[task_idx](tf.squeeze(out, axis=1))
ta = ta.write(t, y_t)
return t + 1, tf.expand_dims(y_t, 1), new_states, ta
_, _, _, ta = tf.while_loop(
cond, body,
loop_vars=[tf.constant(0), dec_in, states_per_layer, ta],
parallel_iterations=1
)
return tf.transpose(ta.stack(), [1, 0, 2])
def _build_task1_input_seq(self, preds_or_targets_list, dtype):
if preds_or_targets_list:
return tf.concat(preds_or_targets_list, axis=-1)
return tf.zeros([tf.shape(preds_or_targets_list)[0], self.pred_len, self.task_output_dim], dtype=dtype)
def call(self, inputs, targets=None, training=False):
enc_out, enc_states = self._encode(inputs, training=training)
batch = tf.shape(inputs)[0]
dtype = inputs.dtype
use_vectorized = bool(training and self.teacher_forcing and (targets is not None) and (self.tf_ratio > 0.0))
aux_preds = []
if use_vectorized:
for t in range(2, self.no_tasks):
if self.start_tokens[t] is not None:
start = tf.tile(tf.cast(self.start_tokens[t], dtype), [batch, 1, 1])
else:
start = tf.zeros([batch, 1, self.task_output_dim], dtype=dtype)
s = t * self.task_output_dim
e = s + self.task_output_dim
y_gt = targets[:, :, s:e]
y_in = tf.concat([start, y_gt[:, :-1, :]], axis=1)
aux_pred_t = self._vectorized_decode_task(t, y_in, enc_states, training=True)
aux_preds.append(aux_pred_t)
if self.no_tasks > 2:
parts = [targets[:, :, tt*self.task_output_dim:(tt+1)*self.task_output_dim]
for tt in range(2, self.no_tasks)]
y_in1 = tf.concat(parts, axis=-1)
else:
y_in1 = tf.zeros([batch, self.pred_len, self.task_output_dim], dtype=dtype)
task1 = self._vectorized_decode_task(1, y_in1, enc_states, training=True)
enc_proj = self.enc_to_task0(enc_out)
enc_seq = tf.tile(tf.expand_dims(enc_proj, 1), [1, self.pred_len, 1])
s1 = 1 * self.task_output_dim
e1 = s1 + self.task_output_dim
t1_seq = targets[:, :, s1:e1]
y_in0 = tf.concat([t1_seq, enc_seq], axis=-1)
task0 = self._vectorized_decode_task(0, y_in0, enc_states, training=True)
else:
for t in range(2, self.no_tasks):
aux_pred_t = self._autoregressive_decode_aux(t, enc_states, batch, dtype)
aux_preds.append(aux_pred_t)
if self.no_tasks > 2:
y_in1 = tf.concat(aux_preds, axis=-1)
else:
y_in1 = tf.zeros([batch, self.pred_len, self.task_output_dim], dtype=dtype)
task1 = self._vectorized_decode_task(1, y_in1, enc_states, training=False)
enc_proj = self.enc_to_task0(enc_out)
enc_seq = tf.tile(tf.expand_dims(enc_proj, 1), [1, self.pred_len, 1])
y_in0 = tf.concat([task1, enc_seq], axis=-1)
task0 = self._vectorized_decode_task(0, y_in0, enc_states, training=False)
outs = [task0, task1] + aux_preds
return tf.concat(outs, axis=-1)
@property
def metrics(self):
return [self.loss_tracker]
def _compiled_metrics_results(self):
cm = getattr(self, "compiled_metrics", None)
if cm is None:
return {}
try:
return cm.result_dict()
except Exception:
try:
names = cm.metrics_names
values = cm.result()
return {n: v for n, v in zip(names, values)}
except Exception:
return {}
def train_step(self, data):
x, y, sw = keras.utils.unpack_x_y_sample_weight(data)
with tf.GradientTape() as tape:
y_pred = self(x, targets=y, training=True)
loss = self.compute_loss(x, y, y_pred, sw, training=True)
grads = tape.gradient(loss, self.trainable_variables)
self.optimizer.apply_gradients(zip(grads, self.trainable_variables))
self.loss_tracker.update_state(loss)
self.compute_metrics(x, y, y_pred, sw)
logs = {"loss": self.loss_tracker.result()}
logs.update(self._compiled_metrics_results())
return logs
def test_step(self, data):
x, y, sw = keras.utils.unpack_x_y_sample_weight(data)
y_pred = self(x, targets=None, training=False)
s, e = 0, self.task_output_dim
val_loss = self.compute_loss(x, y[:, :, s:e], y_pred[:, :, s:e], sw, training=False)
self.loss_tracker.update_state(val_loss)
self.compute_metrics(x, y, y_pred, sw)
logs = {"loss": self.loss_tracker.result()}
logs.update(self._compiled_metrics_results())
return logs
def get_config(self):
return {
"input_dim": self.input_dim,
"output_dim": self.output_dim,
"no_tasks": self.no_tasks,
"task_output_dim": self.task_output_dim,
"pred_len": self.pred_len,
"hidden_dim": self.hidden_dim,
"dropout": self.dropout,
"teacher_forcing": self.teacher_forcing,
"sub_kan_configs": self.sub_kan_configs,
}
Custom KAN LSTM wrapper¶
In [130]:
from sklearn.base import BaseEstimator, RegressorMixin
import numpy as np
import tensorflow as tf
from tensorflow import keras
class TKANHierMTLWrapper(BaseEstimator, RegressorMixin):
def __init__(
self,
input_dim=10,
no_tasks=3,
task_output_dim=1,
pred_len=28,
hidden_layers=3,
hidden_dim=32,
dropout=0.2,
knots=8,
spline_power=3,
sub_kan_configs=None,
teacher_forcing=True,
tf_ratio_start=1.0,
tf_ratio_end=0.0,
lr=1e-3,
l2_weight=1e-4,
epochs=10,
batch_size=512,
verbose=1,
patience=10,
min_delta=1e-4,
validation_split=0.0,
seed=42,
metrics=("mse",),
run_eagerly=False,
output_dim=None,
min_epochs=10,
loss_type="mse",
target_is_logvar=False,
nll_eps=1e-12,
clamp_logvar_min=-20.0,
clamp_logvar_max=20.0,
student_df=5.0,
):
self.input_dim = int(input_dim)
self.no_tasks = int(no_tasks)
self.task_output_dim = int(task_output_dim)
self.pred_len = int(pred_len)
self.hidden_layers = int(hidden_layers)
self.hidden_dim = int(hidden_dim)
self.dropout = float(dropout)
self.knots = int(knots)
self.spline_power = int(spline_power)
self.sub_kan_configs = sub_kan_configs
self.teacher_forcing = bool(teacher_forcing)
self.tf_ratio_start = float(tf_ratio_start)
self.tf_ratio_end = float(tf_ratio_end)
self.lr = float(lr)
self.l2_weight = float(l2_weight)
self.epochs = int(epochs)
self.batch_size = int(batch_size)
self.verbose = int(verbose)
self.patience = int(patience)
self.min_delta = float(min_delta)
self.validation_split = float(validation_split)
self.seed = int(seed)
self.metrics = metrics
self.run_eagerly = bool(run_eagerly)
self.min_epochs = int(min_epochs)
if output_dim is None:
self.output_dim = self.no_tasks * self.task_output_dim
else:
self.output_dim = int(output_dim)
if self.output_dim != self.no_tasks * self.task_output_dim:
self.task_output_dim = int(max(1, self.output_dim // max(1, self.no_tasks)))
self.output_dim = self.no_tasks * self.task_output_dim
self.loss_type = str(loss_type)
self.target_is_logvar = bool(target_is_logvar)
self.nll_eps = float(nll_eps)
self.clamp_logvar_min = float(clamp_logvar_min)
self.clamp_logvar_max = float(clamp_logvar_max)
self.student_df = float(student_df)
self.model_ = None
self.history_ = None
dev = "GPU" if tf.config.list_physical_devices("GPU") else "CPU"
print(f"Using TensorFlow device: {dev}")
def _make_loss(self):
if self.loss_type == "mse":
# MSE over all dimensions
def _mse(y_true, y_pred):
return tf.reduce_mean(tf.square(y_true - y_pred))
return _mse
def _nll_loss(y_true, y_pred):
# y_pred is log-variance when using NLL variants
z = tf.clip_by_value(y_pred, self.clamp_logvar_min, self.clamp_logvar_max)
v = tf.exp(y_true) if self.target_is_logvar else y_true
v = tf.maximum(v, self.nll_eps)
if self.loss_type == "gauss_nll_var":
# up to constants: v*exp(-z) + z
return tf.reduce_mean(v * tf.exp(-z) + z)
if self.loss_type == "student_t_nll_var":
nu = tf.cast(self.student_df, z.dtype)
return tf.reduce_mean(0.5 * (nu + 1.0) * tf.math.log1p(v / (nu * tf.exp(z))) + 0.5 * z)
raise ValueError(f"Unknown loss_type: {self.loss_type}")
return _nll_loss
def _build(self):
tf.keras.utils.set_random_seed(self.seed)
model = TKANHierarchicalSeq2Seq(
input_dim=self.input_dim,
output_dim=self.output_dim,
no_tasks=self.no_tasks,
task_output_dim=self.task_output_dim,
hidden_layers=self.hidden_layers,
pred_len=self.pred_len,
hidden_dim=self.hidden_dim,
dropout=self.dropout,
teacher_forcing=self.teacher_forcing,
sub_kan_configs=self.sub_kan_configs,
knots=self.knots,
spline_power=self.spline_power,
)
model.tf_ratio = float(self.tf_ratio_start)
opt = keras.optimizers.AdamW(learning_rate=self.lr, weight_decay=self.l2_weight)
loss_fn = self._make_loss()
model._loss_fn = loss_fn
mets = []
if self.metrics:
for m in self.metrics:
mets.append(m if callable(m) else keras.metrics.get(m))
model.compile(
optimizer=opt,
loss=loss_fn,
metrics=mets,
run_eagerly=bool(self.run_eagerly),
jit_compile=False,
)
return model
def fit(self, X, y, X_val=None, y_val=None):
X = np.asarray(X, dtype=np.float32)
y = np.asarray(y, dtype=np.float32)
desired_input_dim = int(X.shape[2])
desired_pred_len = int(y.shape[1])
desired_output_dim = int(y.shape[2])
if desired_output_dim % max(1, self.no_tasks) != 0:
self.no_tasks = 1
self.task_output_dim = desired_output_dim
else:
self.task_output_dim = desired_output_dim // max(1, self.no_tasks)
rebuild_needed = (
self.model_ is None or
self.pred_len != desired_pred_len or
self.output_dim != desired_output_dim or
self.input_dim != desired_input_dim
)
if rebuild_needed:
self.pred_len = desired_pred_len
self.output_dim = desired_output_dim
self.input_dim = desired_input_dim
self.model_ = self._build()
use_val = (X_val is not None and y_val is not None) or self.validation_split > 0
monitor_name = "val_loss" if use_val else "loss"
callbacks = [
keras.callbacks.ReduceLROnPlateau(
monitor=monitor_name, mode="min", factor=0.5, patience=5,
verbose=bool(self.verbose), min_lr=1e-7,
),
keras.callbacks.EarlyStopping(
monitor=monitor_name, mode="min", patience=self.patience, min_delta=self.min_delta,
restore_best_weights=True, verbose=bool(self.verbose),
),
keras.callbacks.TerminateOnNaN(),
]
if self.teacher_forcing and (self.tf_ratio_start != self.tf_ratio_end):
callbacks.append(
_TeacherForcingScheduler(
total_epochs=self.epochs,
start=self.tf_ratio_start,
end=self.tf_ratio_end,
verbose=int(self.verbose),
)
)
if X_val is not None and y_val is not None:
X_val = np.asarray(X_val, dtype=np.float32)
y_val = np.asarray(y_val, dtype=np.float32)
val_data = (X_val, y_val); val_split = 0.0
else:
val_data = None; val_split = float(self.validation_split)
self.history_ = self.model_.fit(
x=X, y=y,
batch_size=self.batch_size,
epochs=self.epochs,
verbose=self.verbose,
validation_data=val_data,
validation_split=val_split,
shuffle=True,
callbacks=callbacks,
)
return self
def predict(self, X):
if self.model_ is None:
raise RuntimeError("Model is not fitted yet.")
X = np.asarray(X, dtype=np.float32)
return np.asarray(self.model_.predict(X, batch_size=self.batch_size, verbose=0))
def score(self, X, y):
if self.model_ is None:
raise RuntimeError("Model is not fitted yet.")
X = np.asarray(X, dtype=np.float32)
y = np.asarray(y, dtype=np.float32)
y_pred = self.model_.predict(X, batch_size=self.batch_size, verbose=0)
mae = float(np.mean(np.abs(y_pred - y)))
return -mae
def get_params(self, deep=True):
return {
"input_dim": self.input_dim,
"no_tasks": self.no_tasks,
"task_output_dim": self.task_output_dim,
"pred_len": self.pred_len,
"hidden_layers": self.hidden_layers,
"hidden_dim": self.hidden_dim,
"dropout": self.dropout,
"knots": self.knots,
"spline_power": self.spline_power,
"sub_kan_configs": self.sub_kan_configs,
"teacher_forcing": self.teacher_forcing,
"tf_ratio_start": self.tf_ratio_start,
"tf_ratio_end": self.tf_ratio_end,
"lr": self.lr,
"l2_weight": self.l2_weight,
"epochs": self.epochs,
"batch_size": self.batch_size,
"verbose": self.verbose,
"patience": self.patience,
"min_delta": self.min_delta,
"validation_split": self.validation_split,
"seed": self.seed,
"metrics": self.metrics,
"run_eagerly": self.run_eagerly,
"min_epochs": self.min_epochs,
"output_dim": self.output_dim,
"loss_type": self.loss_type,
"target_is_logvar": self.target_is_logvar,
"nll_eps": self.nll_eps,
"clamp_logvar_min": self.clamp_logvar_min,
"clamp_logvar_max": self.clamp_logvar_max,
"student_df": self.student_df,
}
def set_params(self, **params):
for k, v in params.items():
setattr(self, k, v)
if "output_dim" in params or "no_tasks" in params or "task_output_dim" in params:
if self.output_dim != self.no_tasks * self.task_output_dim:
self.task_output_dim = int(max(1, self.output_dim // max(1, self.no_tasks)))
self.output_dim = self.no_tasks * self.task_output_dim
self.model_ = None
return self
def __repr__(self):
return (
f"TKANHierMTLWrapper(input_dim={self.input_dim}, no_tasks={self.no_tasks}, "
f"task_output_dim={self.task_output_dim}, pred_len={self.pred_len}, hidden_layers={self.hidden_layers}, "
f"hidden_dim={self.hidden_dim}, dropout={self.dropout}, lr={self.lr}, l2_weight={self.l2_weight}, "
f"epochs={self.epochs}, batch_size={self.batch_size}, loss_type={self.loss_type}, "
f"target_is_logvar={self.target_is_logvar})"
)
All tasks 3 EURUSD task with Custom KAN LSTM without CV¶
In [ ]:
import os
save_test_EURUSD_KCLSTM_model_all_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_EURUSD_KCLSTM_model_all_task.pkl"
)
tall_EURUSD_KCLSTM_results, tall_EURUSD_KCLSTM_nested_results, tall_EURUSD_KCLSTM_best_model, tall_EURUSD_KCLSTM_best_params, _ = train_and_evaluate_model(
model_type="Custom_KAN_LSTM",
X_price=eur7_X_price,
X_time=eur7_X_time,
y=eur7_y,
no_tasks=eur7_y.shape[2],
use_nested_cv=False,
flatten=False,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
save_model_path=save_test_EURUSD_KCLSTM_model_all_file_path,
lr=5e-4,
epochs=30,
batch_size=16,
verbose=True,
time_horizon=1,
hidden_layers=2,
l2_weight=1e-5,
dropout=0,
hidden_dim=64,
knots=8,
spline_power=5,
min_delta=1e-3,
min_epochs=10,
patience=10,
target_mode="log_mse"
)
OHO with parameter search - Custom KAN LSTM - all task - data 3 - horizon 1¶
In [201]:
import os
save_test_OHO_CLSTM_KAN_model_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_OHO_CLSTM_KAN_model.pkl"
)
param_grid = {
"lr": [1e-4, 5e-4],
#"dropout": [0, 0.1],
#"l2_weight": [5e-5, 5e-4],
#"batch_size": [256, 512],
#"hidden_layers": [1, 2],
#"hidden_dim": [16, 32],
#"knots": [5, 8],
#"spline_power": [3, 4]
}
t1d3_OHO_CLSTM_KAN_results, t1d3_OHO_CLSTM_KAN_nested_results, t1d3_OHO_CLSTM_KAN_best_model, t1d3_OHO_CLSTM_KAN_best_params, t1d3_OHO_CLSTM_KAN_y_data = train_and_evaluate_model(
param_grid=param_grid,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Custom_KAN_LSTM",
# data for each scenario
X_price=eur3_X_price,
X_time=eur3_X_time,
y=eur3_y,
# specify these for each data
no_tasks=eur3_y.shape[2],
flatten=False,
time_horizon=28,
# these have to be adjusted for some models
patience=2,
epochs = 2,
min_epochs=50,
# saving model
save_model_path=save_test_OHO_LSTM_KAN_model_file_path,
# these go into the parameter grid
lr=1e-3,
dropout=0,
l2_weight=1e-4,
batch_size=512,
hidden_layers=2,
hidden_dim=64,
# specific to KAN:
knots=8,
spline_power=4
)
model parameter(s) from the grid will overwrite any overlapping parameters provided directly to this function Using TensorFlow device: CPU Batch size for y: 3782 Time steps for y: 28 Features for y: 8 Single holdout: one train/test split, param search on a single train/val (early-stop) split. Using TensorFlow device: CPU [TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON Epoch 1/2 6/6 ━━━━━━━━━━━━━━━━━━━━ 182s 26s/step - mean_absolute_error: 0.3246 - task0_mae: 0.6071 - task1_mae: 0.1114 - task2_mae: 0.0997 - task3_mae: 0.1119 - task4_mae: 1.1774 - task5_mae: 0.1263 - task6_mae: 0.3181 - task7_mae: 0.0453 - loss: 0.2243 - val_loss: 0.2467 - learning_rate: 1.0000e-04 [TF Scheduler] epoch=2 tf_ratio=0.000 -> TF=OFF Epoch 2/2 6/6 ━━━━━━━━━━━━━━━━━━━━ 28s 5s/step - mean_absolute_error: 0.2464 - task0_mae: 0.4439 - task1_mae: 0.1136 - task2_mae: 0.2011 - task3_mae: 0.1285 - task4_mae: 0.8715 - task5_mae: 0.0374 - task6_mae: 0.1335 - task7_mae: 0.0418 - loss: 0.1154 - val_loss: 0.1386 - learning_rate: 1.0000e-04 Using TensorFlow device: CPU [TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON Epoch 1/2 6/6 ━━━━━━━━━━━━━━━━━━━━ 191s 27s/step - mean_absolute_error: 0.1727 - task0_mae: 0.1310 - task1_mae: 0.1124 - task2_mae: 0.3709 - task3_mae: 0.1350 - task4_mae: 0.1279 - task5_mae: 0.0564 - task6_mae: 0.3823 - task7_mae: 0.0656 - loss: 0.0466 - val_loss: 0.0582 - learning_rate: 5.0000e-04 [TF Scheduler] epoch=2 tf_ratio=0.000 -> TF=OFF Epoch 2/2 6/6 ━━━━━━━━━━━━━━━━━━━━ 25s 4s/step - mean_absolute_error: 0.1299 - task0_mae: 0.1180 - task1_mae: 0.0899 - task2_mae: 0.1134 - task3_mae: 0.1117 - task4_mae: 0.1285 - task5_mae: 0.1273 - task6_mae: 0.3019 - task7_mae: 0.0482 - loss: 0.0451 - val_loss: 0.0317 - learning_rate: 5.0000e-04 Best parameters found in single holdout: lr: 0.0005 Results for the best model from sigle hold out evaluation: --- Task 1 --- 1 day(s) MAE (log-var) : 2.68392154 1 day(s) RMSE (log-var) : 2.89003553 1 day(s) R2 (log-var) : -3.77216960 1 day(s) MAE (var) : 0.18449505 1 day(s) RMSE (var) : 0.28185531 1 day(s) R2 (var) : -0.74051713 1 day(s) QLIKE (var) : 0.57941347 5 day(s) MAE (log-var) : 1.40092186 5 day(s) RMSE (log-var) : 1.81345628 5 day(s) R2 (log-var) : -0.88667608 5 day(s) MAE (var) : 0.14422600 5 day(s) RMSE (var) : 0.24300331 5 day(s) R2 (var) : -0.31854578 5 day(s) QLIKE (var) : 1.26226930 10 day(s) MAE (log-var) : 1.23373676 10 day(s) RMSE (log-var) : 1.69352301 10 day(s) R2 (log-var) : -0.64961152 10 day(s) MAE (var) : 0.14910073 10 day(s) RMSE (var) : 0.23191342 10 day(s) R2 (var) : -0.21240731 10 day(s) QLIKE (var) : 1.40578805 20 day(s) MAE (log-var) : 1.22618712 20 day(s) RMSE (log-var) : 1.69946145 20 day(s) R2 (log-var) : -0.67337324 20 day(s) MAE (var) : 0.17361695 20 day(s) RMSE (var) : 0.23995094 20 day(s) R2 (var) : -0.31320911 20 day(s) QLIKE (var) : 1.35271367 full horizon MAE (log-var) : 1.23733805 full horizon RMSE (log-var) : 1.71275718 full horizon R2 (log-var) : -0.70554149 full horizon MAE (var) : 0.18502283 full horizon RMSE (var) : 0.24558428 full horizon R2 (var) : -0.38305052 full horizon QLIKE (var) : 1.27434934 --- Task 2 --- 1 day(s) MAE (log-var) : 1.49976941 1 day(s) RMSE (log-var) : 1.55775678 1 day(s) R2 (log-var) : -12.78011770 1 day(s) MAE (var) : 0.89188969 1 day(s) RMSE (var) : 1.16067724 1 day(s) R2 (var) : -1.44431081 1 day(s) QLIKE (var) : 0.10216207 5 day(s) MAE (log-var) : 0.73904009 5 day(s) RMSE (log-var) : 0.92530447 5 day(s) R2 (log-var) : -3.90864005 5 day(s) MAE (var) : 0.58696845 5 day(s) RMSE (var) : 0.94750572 5 day(s) R2 (var) : -0.64182067 5 day(s) QLIKE (var) : 0.21971033 10 day(s) MAE (log-var) : 0.53852635 10 day(s) RMSE (log-var) : 0.72906182 10 day(s) R2 (log-var) : -2.04925304 10 day(s) MAE (var) : 0.48107652 10 day(s) RMSE (var) : 0.87044439 10 day(s) R2 (var) : -0.38785437 10 day(s) QLIKE (var) : 0.20781071 20 day(s) MAE (log-var) : 0.42913278 20 day(s) RMSE (log-var) : 0.60130404 20 day(s) R2 (log-var) : -1.06586319 20 day(s) MAE (var) : 0.42024657 20 day(s) RMSE (var) : 0.82278062 20 day(s) R2 (var) : -0.23997045 20 day(s) QLIKE (var) : 0.17457075 full horizon MAE (log-var) : 0.39829941 full horizon RMSE (log-var) : 0.55990279 full horizon R2 (log-var) : -0.78104281 full horizon MAE (var) : 0.40363663 full horizon RMSE (var) : 0.80955204 full horizon R2 (var) : -0.19664264 full horizon QLIKE (var) : 0.15863610 --- Task 3 --- 1 day(s) MAE (log-var) : 0.63407002 1 day(s) RMSE (log-var) : 0.75159483 1 day(s) R2 (log-var) : -2.27285367 1 day(s) MAE (var) : 0.09747066 1 day(s) RMSE (var) : 0.12830638 1 day(s) R2 (var) : -1.30641666 1 day(s) QLIKE (var) : 0.08576727 5 day(s) MAE (log-var) : 0.44778890 5 day(s) RMSE (log-var) : 0.54231695 5 day(s) R2 (log-var) : -0.70287721 5 day(s) MAE (var) : 0.08036708 5 day(s) RMSE (var) : 0.10140491 5 day(s) R2 (var) : -0.44104002 5 day(s) QLIKE (var) : 0.15289800 10 day(s) MAE (log-var) : 0.49817380 10 day(s) RMSE (log-var) : 0.59310579 10 day(s) R2 (log-var) : -1.02991813 10 day(s) MAE (var) : 0.09642817 10 day(s) RMSE (var) : 0.11417958 10 day(s) R2 (var) : -0.81936755 10 day(s) QLIKE (var) : 0.16426887 20 day(s) MAE (log-var) : 0.52944955 20 day(s) RMSE (log-var) : 0.62538758 20 day(s) R2 (log-var) : -1.27081181 20 day(s) MAE (var) : 0.10607202 20 day(s) RMSE (var) : 0.12216754 20 day(s) R2 (var) : -1.11696117 20 day(s) QLIKE (var) : 0.14315402 full horizon MAE (log-var) : 0.53808112 full horizon RMSE (log-var) : 0.63415987 full horizon R2 (log-var) : -1.34147919 full horizon MAE (var) : 0.10872219 full horizon RMSE (var) : 0.12425843 full horizon R2 (var) : -1.20763318 full horizon QLIKE (var) : 0.13151890 --- Task 4 --- 1 day(s) MAE (log-var) : 0.03286585 1 day(s) RMSE (log-var) : 0.04834228 1 day(s) R2 (log-var) : -0.36986897 1 day(s) MAE (var) : 0.03432793 1 day(s) RMSE (var) : 0.05124296 1 day(s) R2 (var) : -0.36655219 1 day(s) QLIKE (var) : 0.00086131 5 day(s) MAE (log-var) : 0.03295668 5 day(s) RMSE (log-var) : 0.04681986 5 day(s) R2 (log-var) : -0.28364452 5 day(s) MAE (var) : 0.03441939 5 day(s) RMSE (var) : 0.04969088 5 day(s) R2 (var) : -0.28377441 5 day(s) QLIKE (var) : 0.00087816 10 day(s) MAE (log-var) : 0.03305270 10 day(s) RMSE (log-var) : 0.04778992 10 day(s) R2 (log-var) : -0.33540691 10 day(s) MAE (var) : 0.03451335 10 day(s) RMSE (var) : 0.05065884 10 day(s) R2 (var) : -0.33236820 10 day(s) QLIKE (var) : 0.00087934 20 day(s) MAE (log-var) : 0.03323816 20 day(s) RMSE (log-var) : 0.04926447 20 day(s) R2 (log-var) : -0.41803812 20 day(s) MAE (var) : 0.03469864 20 day(s) RMSE (var) : 0.05214531 20 day(s) R2 (var) : -0.41069451 20 day(s) QLIKE (var) : 0.00088549 full horizon MAE (log-var) : 0.03326666 full horizon RMSE (log-var) : 0.04962742 full horizon R2 (log-var) : -0.43746401 full horizon MAE (var) : 0.03472675 full horizon RMSE (var) : 0.05251193 full horizon R2 (var) : -0.42912474 full horizon QLIKE (var) : 0.00088601 --- Task 5 --- 1 day(s) MAE (log-var) : 0.61014020 1 day(s) RMSE (log-var) : 0.65341802 1 day(s) R2 (log-var) : -2.16091462 1 day(s) MAE (var) : 1.10116269 1 day(s) RMSE (var) : 1.20438065 1 day(s) R2 (var) : -2.57803657 1 day(s) QLIKE (var) : 0.05631448 5 day(s) MAE (log-var) : 0.39431164 5 day(s) RMSE (log-var) : 0.46457062 5 day(s) R2 (log-var) : -0.59731000 5 day(s) MAE (var) : 0.74949987 5 day(s) RMSE (var) : 0.85836889 5 day(s) R2 (var) : -0.81651224 5 day(s) QLIKE (var) : 0.08340053 10 day(s) MAE (log-var) : 0.31517200 10 day(s) RMSE (log-var) : 0.44616248 10 day(s) R2 (log-var) : -0.47247559 10 day(s) MAE (var) : 0.59856108 10 day(s) RMSE (var) : 0.80750781 10 day(s) R2 (var) : -0.60634378 10 day(s) QLIKE (var) : 0.09106712 20 day(s) MAE (log-var) : 0.30372081 20 day(s) RMSE (log-var) : 0.45819954 20 day(s) R2 (log-var) : -0.55237589 20 day(s) MAE (var) : 0.60194535 20 day(s) RMSE (var) : 0.84437492 20 day(s) R2 (var) : -0.75525207 20 day(s) QLIKE (var) : 0.08539854 full horizon MAE (log-var) : 0.30480557 full horizon RMSE (log-var) : 0.46442322 full horizon R2 (log-var) : -0.59404514 full horizon MAE (var) : 0.61554893 full horizon RMSE (var) : 0.86381763 full horizon R2 (var) : -0.83561242 full horizon QLIKE (var) : 0.08036330 --- Task 6 --- 1 day(s) MAE (log-var) : 0.03225871 1 day(s) RMSE (log-var) : 0.05770228 1 day(s) R2 (log-var) : -0.19176217 1 day(s) MAE (var) : 0.03430740 1 day(s) RMSE (var) : 0.06228860 1 day(s) R2 (var) : -0.19427347 1 day(s) QLIKE (var) : 0.00142748 5 day(s) MAE (log-var) : 0.03689556 5 day(s) RMSE (log-var) : 0.05567842 5 day(s) R2 (log-var) : -0.10916008 5 day(s) MAE (var) : 0.03906877 5 day(s) RMSE (var) : 0.06004638 5 day(s) R2 (var) : -0.10940534 5 day(s) QLIKE (var) : 0.00152487 10 day(s) MAE (log-var) : 0.04362043 10 day(s) RMSE (log-var) : 0.05692159 10 day(s) R2 (log-var) : -0.15851584 10 day(s) MAE (var) : 0.04607867 10 day(s) RMSE (var) : 0.06104370 10 day(s) R2 (var) : -0.14589429 10 day(s) QLIKE (var) : 0.00165412 20 day(s) MAE (log-var) : 0.05167693 20 day(s) RMSE (log-var) : 0.06099680 20 day(s) R2 (log-var) : -0.32951652 20 day(s) MAE (var) : 0.05456920 20 day(s) RMSE (var) : 0.06502317 20 day(s) R2 (var) : -0.29942378 20 day(s) QLIKE (var) : 0.00175218 full horizon MAE (log-var) : 0.05446669 full horizon RMSE (log-var) : 0.06258972 full horizon R2 (log-var) : -0.39885434 full horizon MAE (var) : 0.05751674 full horizon RMSE (var) : 0.06660715 full horizon R2 (var) : -0.36258460 full horizon QLIKE (var) : 0.00175172 --- Task 7 --- 1 day(s) MAE (log-var) : 0.04744608 1 day(s) RMSE (log-var) : 0.04865661 1 day(s) R2 (log-var) : -19.09426421 1 day(s) MAE (var) : 0.04646888 1 day(s) RMSE (var) : 0.04769835 1 day(s) R2 (var) : -18.40910950 1 day(s) QLIKE (var) : 0.00005801 5 day(s) MAE (log-var) : 0.02263539 5 day(s) RMSE (log-var) : 0.02813074 5 day(s) R2 (log-var) : -5.82994363 5 day(s) MAE (var) : 0.02236589 5 day(s) RMSE (var) : 0.02773187 5 day(s) R2 (var) : -5.66930994 5 day(s) QLIKE (var) : 0.00018393 10 day(s) MAE (log-var) : 0.01567987 10 day(s) RMSE (log-var) : 0.02151366 10 day(s) R2 (log-var) : -3.07640772 10 day(s) MAE (var) : 0.01554860 10 day(s) RMSE (var) : 0.02124548 10 day(s) R2 (var) : -2.99267876 10 day(s) QLIKE (var) : 0.00019731 20 day(s) MAE (log-var) : 0.01349725 20 day(s) RMSE (log-var) : 0.01830391 20 day(s) R2 (log-var) : -2.11650883 20 day(s) MAE (var) : 0.01345188 20 day(s) RMSE (var) : 0.01815117 20 day(s) R2 (var) : -2.07425685 20 day(s) QLIKE (var) : 0.00016813 full horizon MAE (log-var) : 0.01313063 full horizon RMSE (log-var) : 0.01742568 full horizon R2 (log-var) : -1.94374735 full horizon MAE (var) : 0.01311558 full horizon RMSE (var) : 0.01732071 full horizon R2 (var) : -1.91490228 full horizon QLIKE (var) : 0.00014496 --- Task 8 --- 1 day(s) MAE (log-var) : 4.39056004 1 day(s) RMSE (log-var) : 5.11053478 1 day(s) R2 (log-var) : -16.09249091 1 day(s) MAE (var) : 151237.71190155 1 day(s) RMSE (var) : 709930.53809119 1 day(s) R2 (var) : -441279.47368114 1 day(s) QLIKE (var) : 3.03231370 5 day(s) MAE (log-var) : 6.36474624 5 day(s) RMSE (log-var) : 8.06197646 5 day(s) R2 (log-var) : -41.49838232 5 day(s) MAE (var) : 415981889109.44134521 5 day(s) RMSE (var) : 13783819208236.89062500 5 day(s) R2 (var) : -166289663855899049984.00000000 5 day(s) QLIKE (var) : 20.96723486 10 day(s) MAE (log-var) : 8.94181164 10 day(s) RMSE (log-var) : 11.77921956 10 day(s) R2 (log-var) : -89.53144808 10 day(s) MAE (var) : 946233916984.50512695 10 day(s) RMSE (var) : 49298950099814.54687500 10 day(s) R2 (var) : -2126243086793607741440.00000000 10 day(s) QLIKE (var) : 26.60596695 20 day(s) MAE (log-var) : 13.84901662 20 day(s) RMSE (log-var) : 17.79384013 20 day(s) R2 (log-var) : -205.56626927 20 day(s) MAE (var) : 478929888948.30700684 20 day(s) RMSE (var) : 34866086521225.21093750 20 day(s) R2 (var) : -1062767301019427930112.00000000 20 day(s) QLIKE (var) : 21.51755678 full horizon MAE (log-var) : 16.31740725 full horizon RMSE (log-var) : 20.32954844 full horizon R2 (log-var) : -268.89671259 full horizon MAE (var) : 342931639823.38146973 full horizon RMSE (var) : 29467473182178.47656250 full horizon R2 (var) : -758855959331431251968.00000000 full horizon QLIKE (var) : 18.76747863 Best single-holdout model saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_OHO_LSTM_KAN_model.pkl
Benchmark models¶
TimesNet¶
TimesNet wrapper¶
In [131]:
import os
from types import SimpleNamespace
from time import perf_counter
import numpy as np
import torch
import torch.nn as nn
from models.TimesNet import Model as TimesNetModel
class TimesNetWrapper:
def __init__(
self,
input_len, output_len, input_dim, output_dim, no_tasks,
device=None, epochs=10, batch_size=16, verbose=False, lr=1e-3,
checkpoint_path=None, l2_weight=1e-5, patience=10, min_epochs=50,
min_delta=1e-4, dropout=0.0,
hidden_dim=None,
hidden_layers=None,
d_model=None,
d_ff=None,
n_heads=None,
e_layers=None,
factor=2, top_k=3, num_kernels=3, individual=False,
moving_avg=25, embed="timeF", freq="d", activation="gelu",
output_attention=False, distil=True, mix=True,
task_name="long_term_forecast", features="MS",
loss_type="gauss_nll_var",
target_is_logvar=True,
nll_eps=1e-12,
clamp_logvar_min=-20.0,
clamp_logvar_max=20.0,
student_df=5.0,
):
self.input_len = input_len
self.output_len = output_len
self.input_dim = input_dim
self.output_dim = output_dim
self.no_tasks = no_tasks
self.device = device or ("mps" if torch.backends.mps.is_available()
else ("cuda" if torch.cuda.is_available() else "cpu"))
print(f"[TimesNetWrapper] Using device: {self.device}")
self.epochs = epochs
self.batch_size = batch_size
self.verbose = verbose
self.lr = lr
self.checkpoint_path = checkpoint_path
self.l2_weight = l2_weight
self.patience = patience
self.min_epochs = min_epochs
self.min_delta = min_delta
self.dropout = dropout
default_d_model = 64
default_d_ff = 256
default_n_heads = 4
default_e_layers = 2
if d_model is None and hidden_dim is not None:
d_model = hidden_dim
if d_ff is None and hidden_dim is not None:
d_ff = max(4 * (d_model if d_model is not None else default_d_model),
hidden_dim)
if n_heads is None and hidden_layers is not None:
n_heads = max(1, hidden_layers)
if e_layers is None and hidden_layers is not None:
e_layers = max(1, hidden_layers)
self.d_model = d_model if d_model is not None else default_d_model
self.d_ff = d_ff if d_ff is not None else default_d_ff
self.n_heads = n_heads if n_heads is not None else default_n_heads
self.e_layers = e_layers if e_layers is not None else default_e_layers
if self.d_model % self.n_heads != 0:
for h in range(min(self.n_heads, self.d_model), 0, -1):
if self.d_model % h == 0:
self.n_heads = h
break
if self.verbose:
print(f"[TimesNetWrapper] Adjusted n_heads to {self.n_heads} so d_model % n_heads == 0.")
self.factor = factor
self.top_k = top_k
self.num_kernels = num_kernels
self.individual = individual
self.moving_avg = moving_avg
self.embed = embed
self.freq = freq
self.activation = activation
self.output_attention = output_attention
self.distil = distil
self.mix = mix
self.task_name = task_name
self.features = features
self.apply_output_projection = (self.input_dim != self.output_dim)
self.loss_type = str(loss_type)
self.target_is_logvar = bool(target_is_logvar)
self.nll_eps = float(nll_eps)
self.clamp_logvar_min = float(clamp_logvar_min)
self.clamp_logvar_max = float(clamp_logvar_max)
self.student_df = float(student_df)
self._rebuild_model_and_opt()
def _make_config(self):
return {
"seq_len": self.input_len,
"label_len": self.input_len // 2,
"pred_len": self.output_len,
"enc_in": self.input_dim,
"dec_in": self.input_dim,
"c_out": self.input_dim,
"individual": self.individual,
"d_model": self.d_model,
"d_ff": self.d_ff,
"n_heads": self.n_heads,
"e_layers": self.e_layers,
"dropout": self.dropout,
"factor": self.factor,
"top_k": self.top_k,
"num_kernels": self.num_kernels,
"task_name": self.task_name,
"features": self.features,
"moving_avg": self.moving_avg,
"embed": self.embed,
"freq": self.freq,
"activation": self.activation,
"output_attention": self.output_attention,
"distil": self.distil,
"mix": self.mix,
"device": self.device,
}
def _rebuild_model_and_opt(self):
cfg = self._make_config()
self.model = TimesNetModel(configs=SimpleNamespace(**cfg)).to(self.device)
self.out_head = (nn.Linear(self.input_dim, self.output_dim).to(self.device)
if self.apply_output_projection else nn.Identity())
params = list(self.model.parameters()) + list(self.out_head.parameters())
self.optimizer = torch.optim.Adam(params, lr=self.lr, weight_decay=self.l2_weight)
self._mse = nn.MSELoss()
def _compute_loss(self, y_hat, y_true):
if self.loss_type == "mse":
return self._mse(y_hat, y_true)
z = torch.clamp(y_hat, self.clamp_logvar_min, self.clamp_logvar_max)
if self.loss_type == "gauss_nll_var":
v = torch.exp(y_true) if self.target_is_logvar else y_true
v = torch.clamp(v, min=self.nll_eps)
loss = v * torch.exp(-z) + z
return loss.mean()
elif self.loss_type == "student_t_nll_var":
v = torch.exp(y_true) if self.target_is_logvar else y_true
v = torch.clamp(v, min=self.nll_eps)
nu = torch.tensor(self.student_df, device=z.device, dtype=z.dtype)
loss = 0.5 * (nu + 1.0) * torch.log1p(v / (nu * torch.exp(z))) + 0.5 * z
return loss.mean()
else:
raise ValueError(f"Unknown loss_type: {self.loss_type}")
def _save_checkpoint(self, epoch):
if self.checkpoint_path:
torch.save({
'epoch': epoch,
'model_state_dict': self.model.state_dict(),
'head_state_dict': self.out_head.state_dict() if isinstance(self.out_head, nn.Linear) else None,
'optimizer_state_dict': self.optimizer.state_dict()
}, self.checkpoint_path)
if self.verbose:
print(f"Saved checkpoint at epoch {epoch} -> {self.checkpoint_path}")
def _load_checkpoint(self):
if self.checkpoint_path and os.path.isfile(self.checkpoint_path):
ckpt = torch.load(self.checkpoint_path, map_location=self.device)
self.model.load_state_dict(ckpt['model_state_dict'])
if ckpt.get('head_state_dict') is not None and isinstance(self.out_head, nn.Linear):
self.out_head.load_state_dict(ckpt['head_state_dict'])
self.optimizer.load_state_dict(ckpt['optimizer_state_dict'])
if self.verbose:
print(f"Resumed from checkpoint at epoch {ckpt['epoch']}")
return ckpt['epoch'] + 1
return 0
def fit(self, X_train, y_train, X_val, y_val):
self.model.train()
start_epoch = self._load_checkpoint()
best_val_loss = float('inf')
epochs_no_improve = 0
for epoch in range(start_epoch, self.epochs):
epoch_loss = 0.0
t0 = perf_counter()
for i in range(0, len(X_train), self.batch_size):
xb = torch.tensor(X_train[i:i+self.batch_size], dtype=torch.float32, device=self.device)
yb = torch.tensor(y_train[i:i+self.batch_size], dtype=torch.float32, device=self.device)
self.optimizer.zero_grad()
x_dec = torch.zeros((xb.size(0), self.output_len, self.input_dim),
dtype=xb.dtype, device=self.device)
pred_in = self.model(xb, None, x_dec, None)
pred = self.out_head(pred_in)
loss = self._compute_loss(pred, yb)
loss.backward()
self.optimizer.step()
epoch_loss += loss.item()
self.model.eval()
with torch.no_grad():
Xv = torch.tensor(X_val, dtype=torch.float32, device=self.device)
yv = torch.tensor(y_val, dtype=torch.float32, device=self.device)
x_dec_v = torch.zeros((Xv.size(0), self.output_len, self.input_dim),
dtype=Xv.dtype, device=self.device)
pred_in_v = self.model(Xv, None, x_dec_v, None)
pred_v = self.out_head(pred_in_v)
val_loss = self._compute_loss(pred_v, yv).item()
if self.verbose:
print(f"Epoch {epoch+1}/{self.epochs} Train {epoch_loss:.4f} Val {val_loss:.4f} {perf_counter()-t0:.1f}s")
improved = (best_val_loss - val_loss) > self.min_delta
if improved:
best_val_loss = val_loss
epochs_no_improve = 0
self._save_checkpoint(epoch)
else:
epochs_no_improve += 1
if (epoch+1) >= self.min_epochs and epochs_no_improve >= self.patience:
if self.verbose:
print("Early stopping.")
break
self.model.train()
@torch.no_grad()
def predict(self, X_test):
self.model.eval()
Xt = torch.tensor(X_test, dtype=torch.float32, device=self.device)
x_dec = torch.zeros((Xt.size(0), self.output_len, self.input_dim),
dtype=Xt.dtype, device=self.device)
pred_in = self.model(Xt, None, x_dec, None)
pred = self.out_head(pred_in)
return pred.cpu().numpy()
def get_params(self, deep=True):
return {
"input_len": self.input_len, "output_len": self.output_len,
"input_dim": self.input_dim, "output_dim": self.output_dim, "no_tasks": self.no_tasks,
"device": self.device, "epochs": self.epochs, "batch_size": self.batch_size,
"verbose": self.verbose, "lr": self.lr, "checkpoint_path": self.checkpoint_path,
"l2_weight": self.l2_weight, "patience": self.patience, "min_epochs": self.min_epochs, "min_delta": self.min_delta,
"d_model": self.d_model, "d_ff": self.d_ff, "n_heads": self.n_heads, "e_layers": self.e_layers,
"dropout": self.dropout, "factor": self.factor, "top_k": self.top_k, "num_kernels": self.num_kernels,
"individual": self.individual, "moving_avg": self.moving_avg, "embed": self.embed, "freq": self.freq,
"activation": self.activation, "output_attention": self.output_attention, "distil": self.distil, "mix": self.mix,
"task_name": self.task_name, "features": self.features,
"hidden_dim": None, "hidden_layers": None,
"loss_type": self.loss_type, "target_is_logvar": self.target_is_logvar,
"nll_eps": self.nll_eps, "clamp_logvar_min": self.clamp_logvar_min,
"clamp_logvar_max": self.clamp_logvar_max, "student_df": self.student_df,
}
def set_params(self, **params):
for k, v in params.items():
setattr(self, k, v)
if "hidden_dim" in params and params.get("hidden_dim") is not None:
if "d_model" not in params: self.d_model = params["hidden_dim"]
if "d_ff" not in params: self.d_ff = max(4 * self.d_model, params["hidden_dim"])
if "hidden_layers" in params and params.get("hidden_layers") is not None:
if "n_heads" not in params: self.n_heads = max(1, params["hidden_layers"])
if "e_layers" not in params: self.e_layers = max(1, params["hidden_layers"])
if self.d_model % self.n_heads != 0:
for h in range(min(self.n_heads, self.d_model), 0, -1):
if self.d_model % h == 0:
self.n_heads = h
break
if self.verbose:
print(f"[TimesNetWrapper] Adjusted n_heads to {self.n_heads} so d_model % n_heads == 0.")
self.apply_output_projection = (self.input_dim != self.output_dim)
self._rebuild_model_and_opt()
return self
All tasks EURUSD with TimesNet without CV¶
In [715]:
import os
save_eur_test_TimesNet_model_file_path_9 = os.path.join(root_folder, objects_relative_path, "eur_test_TimesNet_model_9.pkl")
t_TN9_results_eur, t_TN9_nested_results_eur, t_TN9_best_model_eur, t_TN9_best_params_eur, _ = train_and_evaluate_model(
model_type="TimesNet",
X_price=eur8_X_price,
X_time=eur8_X_time,
y=eur8_y,
no_tasks=1,
use_nested_cv=False,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
flatten=False,
save_model_path=save_eur_test_TimesNet_model_file_path_9,
lr=5e-3,
epochs=50,
batch_size=512,
verbose=True,
time_horizon=20,
d_model=256,
d_ff=64,
n_heads=4,
e_layers=4,
target_mode="log_mse"
)
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [TimesNetWrapper] Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525025 Min value: -0.7148707238463011 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.998957169490066 Min value: -3.7200073694899434 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.408921165171427 Min value: -0.7103978753601863 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_val (log_mse scaled): Shape: (302, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3212202110774225 Min value: -2.9768947428361368 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144182 Min value: -0.7173043382357188 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_test (log_mse scaled): Shape: (757, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737162 Min value: -5.3523385822659755 Epoch 1/50 Train 116.1386 Val 3.6022 24.5s Epoch 2/50 Train 17.5851 Val 1.3612 23.4s Epoch 3/50 Train 6.9005 Val 0.9997 23.3s Epoch 4/50 Train 5.4968 Val 0.9572 23.5s Epoch 5/50 Train 5.7792 Val 0.9673 23.9s Epoch 6/50 Train 5.4373 Val 1.0994 26.1s Epoch 7/50 Train 5.6509 Val 0.9637 28.6s Epoch 8/50 Train 5.4379 Val 1.0060 30.2s Epoch 9/50 Train 5.5104 Val 0.9600 28.2s Epoch 10/50 Train 5.5407 Val 1.0396 27.7s Epoch 11/50 Train 5.8708 Val 0.9908 27.6s Epoch 12/50 Train 5.8469 Val 0.9676 27.7s Epoch 13/50 Train 6.1779 Val 0.9668 28.4s Epoch 14/50 Train 5.4213 Val 1.0066 27.9s Epoch 15/50 Train 5.4624 Val 0.9785 28.5s Epoch 16/50 Train 5.4305 Val 0.9739 30.7s Epoch 17/50 Train 5.4321 Val 0.9870 28.9s Epoch 18/50 Train 5.4423 Val 0.9947 29.2s Epoch 19/50 Train 5.4490 Val 0.9788 29.2s Epoch 20/50 Train 5.4371 Val 0.9779 29.6s Epoch 21/50 Train 5.4350 Val 0.9828 28.9s Epoch 22/50 Train 5.4374 Val 0.9778 29.1s Epoch 23/50 Train 5.4611 Val 0.9614 28.7s Epoch 24/50 Train 5.4581 Val 1.0960 29.9s Epoch 25/50 Train 5.7326 Val 0.9690 30.8s Epoch 26/50 Train 5.6026 Val 1.1078 29.9s Epoch 27/50 Train 5.9645 Val 1.0725 30.2s Epoch 28/50 Train 5.8813 Val 0.9926 33.6s Epoch 29/50 Train 6.0835 Val 1.7720 30.3s Epoch 30/50 Train 8.8096 Val 2.1499 31.7s Epoch 31/50 Train 10.4402 Val 1.5574 31.7s Epoch 32/50 Train 12.7504 Val 1.5455 31.7s Epoch 33/50 Train 13.7888 Val 4.0920 34.8s Epoch 34/50 Train 19.8239 Val 6.0151 31.5s Epoch 35/50 Train 29.8146 Val 4.0086 33.9s Epoch 36/50 Train 23.7000 Val 1.7130 33.6s Epoch 37/50 Train 10.6270 Val 2.6456 36.4s Epoch 38/50 Train 12.6774 Val 1.3597 34.6s Epoch 39/50 Train 7.6017 Val 1.6400 33.7s Epoch 40/50 Train 8.3244 Val 1.2084 33.3s Epoch 41/50 Train 8.2133 Val 1.2465 32.1s Epoch 42/50 Train 6.9128 Val 0.9602 34.1s Epoch 43/50 Train 5.6617 Val 0.9564 37.6s Epoch 44/50 Train 5.4336 Val 1.0557 34.1s Epoch 45/50 Train 5.5941 Val 0.9582 31.9s Epoch 46/50 Train 5.4980 Val 0.9761 37.5s Epoch 47/50 Train 5.4151 Val 0.9941 36.3s Epoch 48/50 Train 5.4274 Val 0.9645 33.2s Epoch 49/50 Train 5.4228 Val 0.9822 35.6s Epoch 50/50 Train 5.4173 Val 0.9785 34.9s Parameters used in the single-fit model: input_len: 60 output_len: 20 input_dim: 1 output_dim: 1 no_tasks: 1 device: mps epochs: 50 batch_size: 512 verbose: True lr: 0.00500000 checkpoint_path: None l2_weight: 0.00000000 patience: 10 min_epochs: 50 min_delta: 0.00010000 d_model: 256 d_ff: 64 n_heads: 4 e_layers: 4 dropout: 0.00000000 factor: 2 top_k: 3 num_kernels: 3 individual: False moving_avg: 25 embed: timeF freq: d activation: gelu output_attention: False distil: True mix: True task_name: long_term_forecast features: MS hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.11953831 1 day(s) RMSE : 0.21673289 1 day(s) R2 : -0.02913410 1 day(s) Pearson r : 0.34434761 1 day(s) QLIKE : 0.50089609 3 day(s) MAE : 0.12021996 3 day(s) RMSE : 0.21784774 3 day(s) R2 : -0.04777035 3 day(s) Pearson r : 0.32170582 3 day(s) QLIKE : 0.50529856 5 day(s) MAE : 0.12008998 5 day(s) RMSE : 0.21790367 5 day(s) R2 : -0.06022091 5 day(s) Pearson r : 0.30049259 5 day(s) QLIKE : 0.50839794 10 day(s) MAE : 0.12002516 10 day(s) RMSE : 0.21831475 10 day(s) R2 : -0.07438354 10 day(s) Pearson r : 0.27805659 10 day(s) QLIKE : 0.51306488 20 day(s) MAE : 0.12000741 20 day(s) RMSE : 0.21868201 20 day(s) R2 : -0.09071540 20 day(s) Pearson r : 0.25643350 20 day(s) QLIKE : 0.51539713 full horizon MAE : 0.12000741 full horizon RMSE : 0.21868201 full horizon R2 : -0.09071540 full horizon Pearson r : 0.25643350 full horizon QLIKE : 0.51539713 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/eur_test_TimesNet_model_9.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.0606662, max=0.544609
OHO with parameter search - TimesNet - all task - data 3 - horizon 1¶
In [ ]:
import os
save_test_TimesNet_model_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_TimesNet_model.pkl"
)
param_grid = {
"lr": [5e-3],
#"dropout": [0, 0.1],
#"l2_weight": [5e-5, 5e-4],
#"batch_size": [256, 512],
"d_model": [64],
"d_ff": [256],
"n_heads": [4],
"e_layers": [2],
}
t1d3_OHO_TimesNet_results, t1d3_OHO_TimesNet_nested_results, t1d3_OHO_TimesNet_best_model, t1d3_OHO_TimesNet_best_params, t1d3_OHO_TimesNet_y_data = train_and_evaluate_model(
param_grid=param_grid,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="TimesNet",
# data for each scenario
X_price=eur4_X_price,
X_time=eur4_X_time,
y=eur4_y,
# specify these for each data
no_tasks=eur4_y.shape[2],
flatten=False,
time_horizon=28,
# these have to be adjusted for some models
patience=2,
epochs = 1,
min_epochs=50,
# saving model
save_model_path=save_test_OHO_LSTM_KAN_model_file_path,
# these go into the parameter grid
lr=1e-3,
dropout=0,
l2_weight=1e-4,
batch_size=4,
# specific to TimeNet
d_model=64,
d_ff=256,
n_heads=4,
e_layers=2
)
model parameter(s) from the grid will overwrite any overlapping parameters provided directly to this function [TimesNetWrapper] Using device: mps Batch size for y: 3782 Time steps for y: 28 Features for y: 2 Single holdout: one train/test split, param search on a single train/val (early-stop) split. [TimesNetWrapper] Using device: mps Epoch 1/1 Train 8.9520 Val 0.0155 25.7s Best parameters found in single holdout: d_ff: 254 d_model: 64 e_layers: 2 lr: 0.005 n_heads: 4 Results for the best model from sigle hold out evaluation: --- Task 1 --- 1 day(s) MAE (log-var) : 0.81650851 1 day(s) RMSE (log-var) : 1.27389565 1 day(s) R2 (log-var) : 0.07279190 1 day(s) MAE (var) : 0.10757523 1 day(s) RMSE (var) : 0.20148224 1 day(s) R2 (var) : 0.11059530 1 day(s) QLIKE (var) : 0.48870430 5 day(s) MAE (log-var) : 0.82052811 5 day(s) RMSE (log-var) : 1.28208969 5 day(s) R2 (log-var) : 0.05698200 5 day(s) MAE (var) : 0.10804338 5 day(s) RMSE (var) : 0.20161883 5 day(s) R2 (var) : 0.09231951 5 day(s) QLIKE (var) : 0.49507399 10 day(s) MAE (log-var) : 0.82326124 10 day(s) RMSE (log-var) : 1.28656853 10 day(s) R2 (log-var) : 0.04793778 10 day(s) MAE (var) : 0.10852924 10 day(s) RMSE (var) : 0.20207523 10 day(s) R2 (var) : 0.07950176 10 day(s) QLIKE (var) : 0.49945789 20 day(s) MAE (log-var) : 0.82599889 20 day(s) RMSE (log-var) : 1.29168608 20 day(s) R2 (log-var) : 0.03331686 20 day(s) MAE (var) : 0.10954185 20 day(s) RMSE (var) : 0.20214579 20 day(s) R2 (var) : 0.06799474 20 day(s) QLIKE (var) : 0.50297977 full horizon MAE (log-var) : 0.82944822 full horizon RMSE (log-var) : 1.29706636 full horizon R2 (log-var) : 0.02187336 full horizon MAE (var) : 0.11047549 full horizon RMSE (var) : 0.20230433 full horizon R2 (var) : 0.06147194 full horizon QLIKE (var) : 0.50562124 --- Task 2 --- 1 day(s) MAE (log-var) : 0.30469243 1 day(s) RMSE (log-var) : 0.42313402 1 day(s) R2 (log-var) : -0.01673905 1 day(s) MAE (var) : 0.34857166 1 day(s) RMSE (var) : 0.74975049 1 day(s) R2 (var) : -0.01992211 1 day(s) QLIKE (var) : 0.09972194 5 day(s) MAE (log-var) : 0.30282973 5 day(s) RMSE (log-var) : 0.42099436 5 day(s) R2 (log-var) : -0.01611864 5 day(s) MAE (var) : 0.34547222 5 day(s) RMSE (var) : 0.74698410 5 day(s) R2 (var) : -0.02043361 5 day(s) QLIKE (var) : 0.09904869 10 day(s) MAE (log-var) : 0.30265280 10 day(s) RMSE (log-var) : 0.42110758 10 day(s) R2 (log-var) : -0.01730427 10 day(s) MAE (var) : 0.34485346 10 day(s) RMSE (var) : 0.74660340 10 day(s) R2 (var) : -0.02103765 10 day(s) QLIKE (var) : 0.09913759 20 day(s) MAE (log-var) : 0.30305197 20 day(s) RMSE (log-var) : 0.42209122 20 day(s) R2 (log-var) : -0.01794885 20 day(s) MAE (var) : 0.34485706 20 day(s) RMSE (var) : 0.74667121 20 day(s) R2 (var) : -0.02117940 20 day(s) QLIKE (var) : 0.09938547 full horizon MAE (log-var) : 0.30374141 full horizon RMSE (log-var) : 0.42343072 full horizon R2 (log-var) : -0.01862380 full horizon MAE (var) : 0.34569119 full horizon RMSE (var) : 0.74810879 full horizon R2 (var) : -0.02189069 full horizon QLIKE (var) : 0.09994036 Best single-holdout model saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_OHO_LSTM_KAN_model.pkl
ITransformer¶
ITransformer Wrapper¶
In [132]:
import os
from types import SimpleNamespace
from time import perf_counter
import torch
import torch.nn as nn
from models.iTransformer import Model as ITransformerModel
class ITransformerWrapper:
def __init__(
self,
input_len, output_len, input_dim, output_dim, no_tasks,
device=None,
epochs=10, batch_size=32, verbose=False, lr=1e-3, checkpoint_path=None,
dropout=0.0, l2_weight=1e-5, patience=10, min_epochs=50, min_delta=1e-4,
hidden_dim=None,
hidden_layers=None,
d_model=None,
d_ff=None,
n_heads=None,
e_layers=None,
attn="prob", activation="gelu", output_attention=False, distil=True,
embed="timeF", freq="d", features="MS", moving_avg=25, factor=3,
task_name="long_term_forecast",
loss_type="mse",
target_is_logvar=True,
nll_eps=1e-12,
clamp_logvar_min=-20.0,
clamp_logvar_max=20.0,
student_df=5.0,
):
self.input_len = input_len
self.output_len = output_len
self.input_dim = input_dim
self.output_dim = output_dim
self.no_tasks = no_tasks
self.device = device or ("mps" if torch.backends.mps.is_available()
else ("cuda" if torch.cuda.is_available() else "cpu"))
print(f"[ITransformerWrapper] Using device: {self.device}")
self.epochs = epochs
self.batch_size = batch_size
self.verbose = verbose
self.lr = lr
self.checkpoint_path = checkpoint_path
self.dropout = dropout
self.l2_weight = l2_weight
self.patience = patience
self.min_epochs = min_epochs
self.min_delta = min_delta
default_d_model = 64
default_d_ff = 256
default_n_heads = 4
default_e_layers = 2
if d_model is None and hidden_dim is not None:
d_model = hidden_dim
if d_ff is None and hidden_dim is not None:
d_ff = max(4 * (d_model if d_model is not None else default_d_model), hidden_dim)
if n_heads is None and hidden_layers is not None:
n_heads = max(1, hidden_layers)
if e_layers is None and hidden_layers is not None:
e_layers = max(1, hidden_layers)
self.d_model = d_model if d_model is not None else default_d_model
self.d_ff = d_ff if d_ff is not None else default_d_ff
self.n_heads = n_heads if n_heads is not None else default_n_heads
self.e_layers = e_layers if e_layers is not None else default_e_layers
if self.d_model % self.n_heads != 0:
for h in range(min(self.n_heads, self.d_model), 0, -1):
if self.d_model % h == 0:
self.n_heads = h
break
if self.verbose:
print(f"[ITransformerWrapper] Adjusted n_heads to {self.n_heads} so d_model % n_heads == 0.")
self.attn = attn
self.activation = activation
self.output_attention = output_attention
self.distil = distil
self.embed = embed
self.freq = freq
self.features = features
self.moving_avg = moving_avg
self.factor = factor
self.task_name = task_name
self.loss_type = str(loss_type)
self.target_is_logvar = bool(target_is_logvar)
self.nll_eps = float(nll_eps)
self.clamp_logvar_min = float(clamp_logvar_min)
self.clamp_logvar_max = float(clamp_logvar_max)
self.student_df = float(student_df)
self._rebuild()
def _make_config(self):
return {
"task_name": self.task_name,
"seq_len": self.input_len,
"label_len": self.input_len // 2,
"pred_len": self.output_len,
"enc_in": self.input_dim,
"dec_in": self.input_dim,
"c_out": self.output_dim,
"e_layers": self.e_layers,
"d_model": self.d_model,
"n_heads": self.n_heads,
"d_ff": self.d_ff,
"dropout": self.dropout,
"attn": self.attn,
"activation": self.activation,
"output_attention": self.output_attention,
"distil": self.distil,
"embed": self.embed,
"freq": self.freq,
"device": self.device,
"num_workers": 0,
"features": self.features,
"moving_avg": self.moving_avg,
"factor": self.factor,
}
def _rebuild(self):
cfg = self._make_config()
self.model = ITransformerModel(configs=SimpleNamespace(**cfg)).to(self.device)
self.optimizer = torch.optim.Adam(self.model.parameters(), lr=self.lr, weight_decay=self.l2_weight)
self._mse = nn.MSELoss()
def _compute_loss(self, y_hat, y_true):
if self.loss_type == "mse":
return self._mse(y_hat, y_true)
z = torch.clamp(y_hat, self.clamp_logvar_min, self.clamp_logvar_max)
if self.loss_type == "gauss_nll_var":
v = torch.exp(y_true) if self.target_is_logvar else y_true
v = torch.clamp(v, min=self.nll_eps)
loss = v * torch.exp(-z) + z
return loss.mean()
elif self.loss_type == "student_t_nll_var":
v = torch.exp(y_true) if self.target_is_logvar else y_true
v = torch.clamp(v, min=self.nll_eps)
nu = torch.tensor(self.student_df, device=z.device, dtype=z.dtype)
loss = 0.5 * (nu + 1.0) * torch.log1p(v / (nu * torch.exp(z))) + 0.5 * z
return loss.mean()
else:
raise ValueError(f"Unknown loss_type: {self.loss_type}")
def _save_checkpoint(self, epoch):
if self.checkpoint_path:
torch.save({
"epoch": epoch,
"model_state_dict": self.model.state_dict(),
"optimizer_state_dict": self.optimizer.state_dict(),
}, self.checkpoint_path)
if self.verbose:
print(f"Saved checkpoint at epoch {epoch} to: {self.checkpoint_path}")
def _load_checkpoint(self):
if self.checkpoint_path and os.path.isfile(self.checkpoint_path):
ckpt = torch.load(self.checkpoint_path, map_location=self.device)
self.model.load_state_dict(ckpt["model_state_dict"])
self.optimizer.load_state_dict(ckpt["optimizer_state_dict"])
if self.verbose:
print(f"Resumed from checkpoint at epoch {ckpt['epoch']}")
return ckpt["epoch"] + 1
return 0
def fit(self, X_train, y_train, X_val, y_val):
self.model.train()
start_epoch = self._load_checkpoint()
best_val = float("inf")
bad_epochs = 0
for epoch in range(start_epoch, self.epochs):
epoch_loss = 0.0
t0 = perf_counter()
for i in range(0, len(X_train), self.batch_size):
xb = torch.tensor(X_train[i:i+self.batch_size], dtype=torch.float32, device=self.device)
yb = torch.tensor(y_train[i:i+self.batch_size], dtype=torch.float32, device=self.device)
self.optimizer.zero_grad()
x_dec = torch.zeros((xb.size(0), self.output_len, self.output_dim), dtype=xb.dtype, device=self.device)
pred = self.model(xb, None, x_dec, None)
loss = self._compute_loss(pred, yb)
loss.backward()
self.optimizer.step()
epoch_loss += loss.item()
self.model.eval()
with torch.no_grad():
Xv = torch.tensor(X_val, dtype=torch.float32, device=self.device)
yv = torch.tensor(y_val, dtype=torch.float32, device=self.device)
x_dec_v = torch.zeros((Xv.size(0), self.output_len, self.output_dim), dtype=Xv.dtype, device=self.device)
pv = self.model(Xv, None, x_dec_v, None)
val = self._compute_loss(pv, yv).item()
if self.verbose:
print(f"Epoch {epoch+1}/{self.epochs} Train {epoch_loss:.4f} Val {val:.4f} {perf_counter()-t0:.1f}s")
if (best_val - val) > self.min_delta:
best_val = val
bad_epochs = 0
self._save_checkpoint(epoch)
else:
bad_epochs += 1
if (epoch + 1) >= self.min_epochs and bad_epochs >= self.patience:
if self.verbose:
print("Early stopping triggered.")
break
self.model.train()
@torch.no_grad()
def predict(self, X_test):
self.model.eval()
Xt = torch.tensor(X_test, dtype=torch.float32, device=self.device)
x_dec = torch.zeros((Xt.size(0), self.output_len, self.output_dim), dtype=Xt.dtype, device=self.device)
pred = self.model(Xt, None, x_dec, None)
return pred.cpu().numpy()
def get_params(self, deep=True):
return {
"input_len": self.input_len, "output_len": self.output_len,
"input_dim": self.input_dim, "output_dim": self.output_dim, "no_tasks": self.no_tasks,
"device": self.device, "epochs": self.epochs, "batch_size": self.batch_size,
"verbose": self.verbose, "lr": self.lr, "checkpoint_path": self.checkpoint_path,
"dropout": self.dropout, "l2_weight": self.l2_weight, "patience": self.patience,
"min_epochs": self.min_epochs, "min_delta": self.min_delta,
"d_model": self.d_model, "d_ff": self.d_ff, "n_heads": self.n_heads, "e_layers": self.e_layers,
"attn": self.attn, "activation": self.activation, "output_attention": self.output_attention,
"distil": self.distil, "embed": self.embed, "freq": self.freq, "features": self.features,
"moving_avg": self.moving_avg, "factor": self.factor, "task_name": self.task_name,
"hidden_dim": None, "hidden_layers": None,
"loss_type": self.loss_type, "target_is_logvar": self.target_is_logvar,
"nll_eps": self.nll_eps, "clamp_logvar_min": self.clamp_logvar_min,
"clamp_logvar_max": self.clamp_logvar_max, "student_df": self.student_df,
}
def set_params(self, **params):
for k, v in params.items():
setattr(self, k, v)
if "hidden_dim" in params and params.get("hidden_dim") is not None:
if "d_model" not in params: self.d_model = params["hidden_dim"]
if "d_ff" not in params: self.d_ff = max(4 * self.d_model, params["hidden_dim"])
if "hidden_layers" in params and params.get("hidden_layers") is not None:
if "n_heads" not in params: self.n_heads = max(1, params["hidden_layers"])
if "e_layers" not in params: self.e_layers = max(1, params["hidden_layers"])
if self.d_model % self.n_heads != 0:
for h in range(min(self.n_heads, self.d_model), 0, -1):
if self.d_model % h == 0:
self.n_heads = h
break
if self.verbose:
print(f"[ITransformerWrapper] Adjusted n_heads to {self.n_heads} so d_model % n_heads == 0.")
rebuild_keys = {"input_len","output_len","input_dim","output_dim","dropout","d_model","d_ff","n_heads","e_layers","attn","activation","output_attention","distil","embed","freq","features","moving_avg","factor","task_name","device"}
if any(k in rebuild_keys for k in params):
self._rebuild()
return self
Test All task EURUSD with ITransformer without CV¶
In [803]:
import os
save_eur_test_ITRS_model_file_path_8 = os.path.join(root_folder, objects_relative_path, "eur_test_ITRS_model_8.pkl")
print(eur8_X_price.shape[2])
print(eur8_y.shape[2])
t_ITRS8_results_eur, t_ITRS8_nested_results_eur, t_ITRS8_best_model_eur, t_ITRS8_best_params_eur, _ = train_and_evaluate_model(
model_type="ITransformer",
X_price=eur8_X_price,
X_time=eur8_X_time,
y=eur8_y,
no_tasks=1,
use_nested_cv=False,
merge_price_time=False,
normalize_X=True,
normalize_Time=False,
normalize_y=True,
flatten=False,
save_model_path=save_eur_test_ITRS_model_file_path_8,
lr=1e-4,
epochs=50,
batch_size=128,
verbose=True,
time_horizon=1,
d_model=64,
d_ff=256,
n_heads=4,
e_layers=2,
target_mode="log_mse"
)
1 6 [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 3782 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525025 Min value: -0.7148707238463011 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_train_core (log_mse scaled): Shape: (2723, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9963965101732293 Min value: -3.718602223017603 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.408921165171427 Min value: -0.7103978753601863 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_val (log_mse scaled): Shape: (302, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.319059579642504 Min value: -2.975928211620611 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144182 Min value: -0.7173043382357188 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.0 Min value: -1.0 Checking y_test (log_mse scaled): Shape: (757, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3834322546316056 Min value: -5.349969967608034 Epoch 1/50 Train 20.0067 Val 0.6610 0.5s Epoch 2/50 Train 15.8688 Val 0.5731 0.2s Epoch 3/50 Train 14.2884 Val 0.5238 0.2s Epoch 4/50 Train 13.3456 Val 0.4948 0.1s Epoch 5/50 Train 12.6880 Val 0.4775 0.1s Epoch 6/50 Train 12.1958 Val 0.4664 0.1s Epoch 7/50 Train 11.8065 Val 0.4593 0.2s Epoch 8/50 Train 11.4853 Val 0.4548 0.1s Epoch 9/50 Train 11.2120 Val 0.4522 0.2s Epoch 10/50 Train 10.9735 Val 0.4510 0.2s Epoch 11/50 Train 10.7610 Val 0.4509 0.1s Epoch 12/50 Train 10.5687 Val 0.4517 0.1s Epoch 13/50 Train 10.3928 Val 0.4533 0.1s Epoch 14/50 Train 10.2306 Val 0.4554 0.2s Epoch 15/50 Train 10.0803 Val 0.4580 0.1s Epoch 16/50 Train 9.9404 Val 0.4611 0.1s Epoch 17/50 Train 9.8099 Val 0.4644 0.1s Epoch 18/50 Train 9.6878 Val 0.4680 0.1s Epoch 19/50 Train 9.5734 Val 0.4719 0.2s Epoch 20/50 Train 9.4661 Val 0.4758 0.1s Epoch 21/50 Train 9.3651 Val 0.4799 0.2s Epoch 22/50 Train 9.2700 Val 0.4840 0.2s Epoch 23/50 Train 9.1801 Val 0.4882 0.2s Epoch 24/50 Train 9.0952 Val 0.4924 0.1s Epoch 25/50 Train 9.0147 Val 0.4967 0.1s Epoch 26/50 Train 8.9381 Val 0.5009 0.2s Epoch 27/50 Train 8.8653 Val 0.5051 0.2s Epoch 28/50 Train 8.7957 Val 0.5093 0.1s Epoch 29/50 Train 8.7292 Val 0.5134 0.2s Epoch 30/50 Train 8.6654 Val 0.5175 0.1s Epoch 31/50 Train 8.6040 Val 0.5216 0.1s Epoch 32/50 Train 8.5449 Val 0.5256 0.1s Epoch 33/50 Train 8.4878 Val 0.5296 0.1s Epoch 34/50 Train 8.4326 Val 0.5336 0.1s Epoch 35/50 Train 8.3791 Val 0.5374 0.1s Epoch 36/50 Train 8.3271 Val 0.5413 0.1s Epoch 37/50 Train 8.2766 Val 0.5451 0.1s Epoch 38/50 Train 8.2273 Val 0.5488 0.1s Epoch 39/50 Train 8.1792 Val 0.5525 0.1s Epoch 40/50 Train 8.1322 Val 0.5562 0.1s Epoch 41/50 Train 8.0862 Val 0.5598 0.1s Epoch 42/50 Train 8.0412 Val 0.5634 0.1s Epoch 43/50 Train 7.9970 Val 0.5669 0.1s Epoch 44/50 Train 7.9536 Val 0.5705 0.1s Epoch 45/50 Train 7.9110 Val 0.5739 0.1s Epoch 46/50 Train 7.8691 Val 0.5774 0.1s Epoch 47/50 Train 7.8278 Val 0.5808 0.1s Epoch 48/50 Train 7.7872 Val 0.5843 0.1s Epoch 49/50 Train 7.7471 Val 0.5877 0.1s Epoch 50/50 Train 7.7076 Val 0.5911 0.1s Early stopping triggered. Parameters used in the single-fit model: input_len: 60 output_len: 1 input_dim: 1 output_dim: 1 no_tasks: 1 device: mps epochs: 50 batch_size: 128 verbose: True lr: 0.00010000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00000000 patience: 10 min_epochs: 50 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 2 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.11062735 1 day(s) RMSE : 0.24461457 1 day(s) R2 : -0.31095260 1 day(s) Pearson r : 0.54062598 1 day(s) QLIKE : 0.43211135 full horizon MAE : 0.11062735 full horizon RMSE : 0.24461457 full horizon R2 : -0.31095260 full horizon Pearson r : 0.54062598 full horizon QLIKE : 0.43211135 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/eur_test_ITRS_model_8.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00157381, max=2.72273
OHO with parameter search - iTransformer - all task - data 3 - horizon 1¶
In [ ]:
import os
save_test_ITransformer_model_file_path = os.path.join(
root_folder,
objects_relative_path,
"test_ITransformer_model.pkl"
)
param_grid = {
"lr": [5e-3],
#"dropout": [0, 0.1],
#"l2_weight": [5e-5, 5e-4],
#"batch_size": [256, 512],
"d_model": [64],
"d_ff": [256],
"n_heads": [4],
"e_layers": [2],
}
t1d3_OHO_ITransformer_results, t1d3_OHO_ITransformer_nested_results, t1d3_OHO_ITransformer_best_model, t1d3_OHO_ITransformer_best_params, t1d3_OHO_ITransformer_y_data = train_and_evaluate_model(
param_grid=param_grid,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="ITransformer",
# data for each scenario
X_price=eur9_X_price,
X_time=eur9_X_time,
y=eur9_y,
# specify these for each data
no_tasks=eur9_y.shape[2],
flatten=False,
time_horizon=28,
# these have to be adjusted for some models
patience=2,
epochs = 50,
min_epochs=50,
# saving model
save_model_path=save_test_ITransformer_model_file_path,
# these go into the parameter grid
lr=1e-3,
dropout=0,
l2_weight=1e-4,
batch_size=4,
hidden_layers=2,
hidden_dim=32,
# specific to ITransformer:
d_model=64,
d_ff=256,
n_heads=4,
e_layers=2
)
[mode=log_var_ratio] loss_type=mse, target_is_logvar=False, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 3782 Time steps for y: 28 Features for y: 2 Single holdout: one train/test split, param search on a single train/val (early-stop) split. [ITransformerWrapper] Using device: mps Epoch 1/50 Train 1087.3472 Val 21.5468 4.5s Epoch 2/50 Train 839.3814 Val 14.0376 4.5s Epoch 3/50 Train 827.5597 Val 1.2781 4.3s Epoch 4/50 Train 786.9254 Val 1.3413 4.4s Epoch 5/50 Train 823.9570 Val 2.9974 4.5s Epoch 6/50 Train 792.4536 Val 4.5769 4.6s Epoch 7/50 Train 801.2790 Val 93.4962 4.6s Epoch 8/50 Train 785.3818 Val 10.1032 4.8s Epoch 9/50 Train 803.2856 Val 6.6489 4.6s Epoch 10/50 Train 816.0592 Val 26.5699 4.9s Epoch 11/50 Train 816.8220 Val 14.0419 4.4s Epoch 12/50 Train 839.0234 Val 17.0919 4.5s Epoch 13/50 Train 813.6907 Val 10.0767 4.5s Epoch 14/50 Train 783.8769 Val 9.8195 4.7s Epoch 15/50 Train 796.6099 Val 9.9969 4.6s Epoch 16/50 Train 792.6564 Val 6.8186 4.4s Epoch 17/50 Train 800.0370 Val 12.0580 4.4s Epoch 18/50 Train 822.7716 Val 24.0334 4.4s Epoch 19/50 Train 814.3003 Val 25.8565 4.5s Epoch 20/50 Train 853.4293 Val 17.3778 4.4s Epoch 21/50 Train 820.1548 Val 3.1449 4.4s Epoch 22/50 Train 809.1714 Val 58.2582 4.4s Epoch 23/50 Train 812.9411 Val 11.4833 4.4s Epoch 24/50 Train 815.5600 Val 14.2370 4.4s Epoch 25/50 Train 822.2740 Val 28.5312 4.4s Epoch 26/50 Train 1223.1283 Val 19.2804 4.5s Epoch 27/50 Train 845.5810 Val 1.4031 4.4s Epoch 28/50 Train 839.8259 Val 3.0496 4.4s Epoch 29/50 Train 810.6021 Val 2.3458 4.4s Epoch 30/50 Train 815.3922 Val 2.0710 4.5s Epoch 31/50 Train 811.9153 Val 3.2759 4.6s Epoch 32/50 Train 809.6778 Val 2.0042 4.4s Epoch 33/50 Train 820.3989 Val 4.5912 4.6s Epoch 34/50 Train 802.8380 Val 2.6348 4.5s Epoch 35/50 Train 806.8375 Val 4.6025 4.7s Epoch 36/50 Train 993.5795 Val 21.0750 4.4s Epoch 37/50 Train 803.9765 Val 1.4148 5.4s Epoch 38/50 Train 836.5816 Val 11.2053 4.4s Epoch 39/50 Train 825.2896 Val 6.7030 4.4s Epoch 40/50 Train 807.9391 Val 4.2100 4.4s Epoch 41/50 Train 821.0650 Val 40.5931 4.4s Epoch 42/50 Train 823.2032 Val 6.7066 4.4s Epoch 43/50 Train 813.2474 Val 6.5751 4.4s Epoch 44/50 Train 799.8728 Val 24.1139 4.4s Epoch 45/50 Train 862.9803 Val 10.8057 4.4s Epoch 46/50 Train 833.0010 Val 25.8066 4.4s Epoch 47/50 Train 806.2162 Val 4.6622 4.4s Epoch 48/50 Train 799.4888 Val 4.7479 4.4s Epoch 49/50 Train 807.3868 Val 5.6666 4.4s Epoch 50/50 Train 821.4557 Val 7.7336 4.4s Early stopping triggered. Best parameters found in single holdout (grid): d_ff: 256 d_model: 64 e_layers: 2 lr: 0.005 n_heads: 4 Refitting with selected hyperparameters on (train_core + train_val), new early-stop split from that union… [ITransformerWrapper] Using device: mps Epoch 1/50 Train 778.4017 Val 52.3412 4.4s Epoch 2/50 Train 809.2608 Val 32.2755 4.4s Epoch 3/50 Train 809.3679 Val 11.0243 4.4s Epoch 4/50 Train 788.7047 Val 4.0465 4.5s Epoch 5/50 Train 800.6716 Val 10.4544 4.5s Epoch 6/50 Train 818.3347 Val 6.2930 4.4s Epoch 7/50 Train 808.3282 Val 5.6543 4.5s Epoch 8/50 Train 840.8377 Val 18.9487 4.4s Epoch 9/50 Train 807.4513 Val 47.9825 4.5s Epoch 10/50 Train 817.5101 Val 7.9060 4.5s Epoch 11/50 Train 838.2396 Val 14.5890 4.4s Epoch 12/50 Train 831.4466 Val 60.9383 4.3s Epoch 13/50 Train 836.1850 Val 81.6192 4.4s Epoch 14/50 Train 824.2687 Val 2.9463 4.4s Epoch 15/50 Train 824.6539 Val 8.1547 4.4s Epoch 16/50 Train 811.4833 Val 6.9473 4.3s Epoch 17/50 Train 790.3770 Val 30.9246 4.4s Epoch 18/50 Train 840.1710 Val 34.9243 4.4s Epoch 19/50 Train 797.1199 Val 3.2593 4.4s Epoch 20/50 Train 797.9213 Val 8.3437 4.4s Epoch 21/50 Train 824.5640 Val 10.6512 4.4s Epoch 22/50 Train 862.3645 Val 17.7547 4.6s Epoch 23/50 Train 836.8259 Val 84.5915 4.4s Epoch 24/50 Train 840.4047 Val 20.4057 4.3s Epoch 25/50 Train 832.3700 Val 4.0024 4.3s Epoch 26/50 Train 827.1364 Val 13.5759 4.4s Epoch 27/50 Train 813.4445 Val 2.1575 4.3s Epoch 28/50 Train 772.1198 Val 1.4681 4.4s Epoch 29/50 Train 778.8743 Val 8.0640 4.4s Epoch 30/50 Train 838.9962 Val 17.0537 4.3s Epoch 31/50 Train 904.9647 Val 14.0029 4.3s Epoch 32/50 Train 822.8615 Val 2.8273 4.4s Epoch 33/50 Train 829.9406 Val 9.5769 4.3s Epoch 34/50 Train 822.4383 Val 3.2647 4.3s Epoch 35/50 Train 832.1558 Val 3.3220 4.4s Epoch 36/50 Train 828.5571 Val 59.3234 4.3s Epoch 37/50 Train 791.0894 Val 20.7131 4.3s Epoch 38/50 Train 772.4258 Val 4.2178 4.3s Epoch 39/50 Train 813.5990 Val 8.5702 4.3s Epoch 40/50 Train 853.4989 Val 30.4546 4.4s Epoch 41/50 Train 847.3138 Val 17.6805 4.4s Epoch 42/50 Train 826.3270 Val 1.6980 4.3s Epoch 43/50 Train 809.1862 Val 8.2035 4.4s Epoch 44/50 Train 817.0469 Val 65.8593 4.4s Epoch 45/50 Train 826.5142 Val 3.9596 4.4s Epoch 46/50 Train 802.7560 Val 20.2774 4.3s Epoch 47/50 Train 809.0999 Val 4.7626 4.3s Epoch 48/50 Train 796.9779 Val 5.4513 4.4s Epoch 49/50 Train 809.6859 Val 2.9171 4.6s Epoch 50/50 Train 827.8608 Val 15.3343 4.4s Early stopping triggered. Results for the refit model (single holdout): --- Task 1 --- 1 day(s) MAE : 17.54991214 1 day(s) RMSE : 83.33589416 1 day(s) R2 : -152154.10950240 1 day(s) Pearson r : 0.25075678 1 day(s) QLIKE : 1.56570299 3 day(s) MAE : 17.44675449 3 day(s) RMSE : 82.82175523 3 day(s) R2 : -151441.87058045 3 day(s) Pearson r : 0.23234911 3 day(s) QLIKE : 1.57260817 5 day(s) MAE : 17.43576307 5 day(s) RMSE : 82.77497175 5 day(s) R2 : -152989.81145626 5 day(s) Pearson r : 0.20546074 5 day(s) QLIKE : 1.58593820 10 day(s) MAE : 17.16006614 10 day(s) RMSE : 81.40399678 10 day(s) R2 : -149376.31596726 10 day(s) Pearson r : 0.18169619 10 day(s) QLIKE : 1.63377881 20 day(s) MAE : 16.75686248 20 day(s) RMSE : 79.41041311 20 day(s) R2 : -143825.94365717 20 day(s) Pearson r : 0.14143044 20 day(s) QLIKE : 1.65560892 full horizon MAE : 16.70763077 full horizon RMSE : 79.14899050 full horizon R2 : -143655.09126308 full horizon Pearson r : 0.12965748 full horizon QLIKE : 1.68016603 --- Task 2 --- 1 day(s) MAE : 0.23288420 1 day(s) RMSE : 0.71200473 1 day(s) R2 : -70.02431082 1 day(s) Pearson r : 0.41471879 1 day(s) QLIKE : 0.40409938 3 day(s) MAE : 0.23349916 3 day(s) RMSE : 0.71192313 3 day(s) R2 : -70.19015558 3 day(s) Pearson r : 0.41606060 3 day(s) QLIKE : 0.40501343 5 day(s) MAE : 0.23378195 5 day(s) RMSE : 0.71198999 5 day(s) R2 : -70.04047294 5 day(s) Pearson r : 0.41474383 5 day(s) QLIKE : 0.40456905 10 day(s) MAE : 0.23424545 10 day(s) RMSE : 0.71219872 10 day(s) R2 : -69.78576030 10 day(s) Pearson r : 0.41107686 10 day(s) QLIKE : 0.40628523 20 day(s) MAE : 0.23370814 20 day(s) RMSE : 0.71166203 20 day(s) R2 : -70.83710231 20 day(s) Pearson r : 0.42000434 20 day(s) QLIKE : 0.40320620 full horizon MAE : 0.23277048 full horizon RMSE : 0.71083669 full horizon R2 : -71.24617445 full horizon Pearson r : 0.43110690 full horizon QLIKE : 0.40069991 Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00754228, max=917.637 Best single-holdout (refit) model saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/test_ITransformer_model.pkl
Run All¶
Call drafts¶
Do not run them, it will take forever.
In [ ]:
import os
time_horizon=60,
save_MLP = os.path.join(
root_folder,
objects_relative_path,
"MLP_model.pkl"
)
param_grid_MLP = {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [128, 512],
"hidden_layers": [2, 4],
"hidden_dim": [32, 64]
}
res, nes_res, best_m, best_par, y_data = train_and_evaluate_model(
param_grid=param_grid_MLP,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Simple_MLP",
# data for each scenario
X_price=eur4_X_price,
X_time=eur4_X_time,
y=eur4_y,
# specify these for each data
no_tasks=1,
flatten=True,
time_horizon=time_horizon,
# these have to be adjusted for some models
patience=10,
epochs=50,
min_epochs=30,
# saving model
save_model_path=save_MLP,
# these go into the parameter grid
lr=1e-5,
dropout=0,
l2_weight=1e-4,
batch_size=64,
hidden_layers=3,
hidden_dim=256
)
save_LSTM = os.path.join(
root_folder,
objects_relative_path,
"LSTM_model.pkl"
)
param_grid_LSTM = {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [16, 32],
"hidden_layers": [2, 4],
"hidden_dim": [32, 64]
}
res, nes_res, best_m, best_par, y_data = train_and_evaluate_model(
param_grid=param_grid_LSTM,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Simple_LSTM",
# data for each scenario
X_price=eur4_X_price,
X_time=eur4_X_time,
y=eur4_y,
# specify these for each data
no_tasks=1,
flatten=False,
time_horizon=time_horizon,
# these have to be adjusted for some models
patience=10,
epochs=50,
min_epochs=30,
# saving model
save_model_path=save_LSTM,
# these go into the parameter grid
lr=1e-5,
dropout=0,
l2_weight=1e-4,
batch_size=64,
hidden_layers=2,
hidden_dim=16
)
save_KAN = os.path.join(
root_folder,
objects_relative_path,
"KAN_model.pkl"
)
param_grid_KAN = {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [256, 512],
"hidden_layers": [1, 2],
"hidden_dim": [16, 32],
"knots": [5, 8],
"spline_power": [3, 5]
}
res, nes_res, best_m, best_par, y_data = train_and_evaluate_model(
param_grid=param_grid_KAN,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Simple_KAN",
# data for each scenario
X_price=eur4_X_price,
X_time=eur4_X_time,
y=eur4_y,
# specify these for each data
no_tasks=1,
flatten=True,
time_horizon=time_horizon,
# these have to be adjusted for some models
patience=10,
epochs=50,
min_epochs=30,
# saving model
save_model_path=save_KAN,
# these go into the parameter grid
lr=1e-5,
dropout=0,
l2_weight=1e-4,
batch_size=64,
hidden_layers=2,
hidden_dim=64,
# specific to KAN:
knots=12,
spline_power=7
)
save_Custom_KAN = os.path.join(
root_folder,
objects_relative_path,
"Custom_KAN_model.pkl"
)
param_grid_Custom_KAN = {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [256, 512],
"hidden_layers": [1, 2],
"hidden_dim": [16, 32],
"knots": [5, 8],
"spline_power": [3, 5]
}
res, nes_res, best_m, best_par, y_data = train_and_evaluate_model(
param_grid=param_grid_Custom_KAN,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Custom_KAN",
# data for each scenario
X_price=eur4_X_price,
X_time=eur4_X_time,
y=eur4_y,
# specify these for each data
no_tasks=1,
flatten=True,
time_horizon=time_horizon,
# these have to be adjusted for some models
patience=10,
epochs=50,
min_epochs=30,
# saving model
save_model_path=save_Custom_KAN,
# these go into the parameter grid
lr=1e-5,
dropout=0,
l2_weight=1e-4,
batch_size=64,
hidden_layers=2,
hidden_dim=64,
# specific to KAN:
knots=12,
spline_power=7,
# additional parameters for Custom KAN
warmup_aux_epochs=15,
joint_epochs=15
)
save_LSTM_KAN = os.path.join(
root_folder,
objects_relative_path,
"LSTM_KAN_model.pkl"
)
param_grid_LSTM_KAN = {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [16, 32],
"hidden_layers": [2, 4],
"hidden_dim": [32, 64],
"knots": [5, 8],
"spline_power": [3, 5]
}
res, nes_res, best_m, best_par, y_data = train_and_evaluate_model(
param_grid=param_grid_LSTM_KAN,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="LSTM_KAN",
# data for each scenario
X_price=eur4_X_price,
X_time=eur4_X_time,
y=eur4_y,
# specify these for each data
no_tasks=1,
flatten=False,
time_horizon=time_horizon,
# these have to be adjusted for some models
patience=10,
epochs=50,
min_epochs=30,
# saving model
save_model_path=save_LSTM_KAN,
# these go into the parameter grid
lr=5e-3,
dropout=0,
l2_weight=1e-5,
batch_size=32,
hidden_layers=2,
hidden_dim=64,
# specific to KAN:
knots=8,
spline_power=3
)
save_Custom_LSTM_KAN = os.path.join(
root_folder,
objects_relative_path,
"Custom_LSTM_KAN_model.pkl"
)
param_grid_Custom_LSTM_KAN = {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [16, 32],
"hidden_layers": [2, 4],
"hidden_dim": [32, 64],
"knots": [5, 8],
"spline_power": [3, 5]
}
res, nes_res, best_m, best_par, y_data = train_and_evaluate_model(
param_grid=param_grid_Custom_LSTM_KAN,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="Custom_LSTM_KAN",
# data for each scenario
X_price=eur4_X_price,
X_time=eur4_X_time,
y=eur4_y,
# specify these for each data
no_tasks=1,
flatten=False,
time_horizon=time_horizon,
# these have to be adjusted for some models
patience=10,
epochs=50,
min_epochs=30,
# saving model
save_model_path=save_Custom_LSTM_KAN,
# these go into the parameter grid
lr=5e-3,
dropout=0,
l2_weight=1e-5,
batch_size=32,
hidden_layers=2,
hidden_dim=64,
# specific to KAN:
knots=8,
spline_power=3
)
save_TimesNet = os.path.join(
root_folder,
objects_relative_path,
"TimesNet_model.pkl"
)
param_grid_TimesNet = {
"lr": [5e-4, 5e-3],
"l2_weight": [5e-5, 5e-4],
"batch_size": [256, 512],
"d_model": [64, 128],
"d_ff": [128, 256],
"n_heads": [4, 6],
"e_layers": [2, 4],
}
res, nes_res, best_m, best_par, y_data = train_and_evaluate_model(
param_grid=param_grid_TimesNet,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="TimesNet",
# data for each scenario
X_price=eur4_X_price,
X_time=eur4_X_time,
y=eur4_y,
# specify these for each data
no_tasks=eur4_y.shape[2],
flatten=False,
time_horizon=28,
# these have to be adjusted for some models
patience=2,
epochs = 1,
min_epochs=50,
# saving model
save_model_path=save_TimesNet,
# these go into the parameter grid
lr=1e-3,
dropout=0,
l2_weight=1e-5,
batch_size=4,
# specific to TimeNet
d_model=64,
d_ff=256,
n_heads=4,
e_layers=2
)
save_ITransformer = os.path.join(
root_folder,
objects_relative_path,
"ITransformer_model.pkl"
)
param_grid_ITransformer = {
"lr": [5e-4, 5e-3],
"l2_weight": [5e-5, 5e-4],
"batch_size": [256, 512],
"d_model": [64, 128],
"d_ff": [128, 256],
"n_heads": [4, 6],
"e_layers": [2, 4],
}
res, nes_res, best_m, best_par, y_data = train_and_evaluate_model(
param_grid=param_grid_ITransformer,
verbose=True,
use_nested_cv=False,
single_holdout=True,
merge_price_time=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
# some models might not need this
min_delta=1e-4,
# model name for each
model_type="ITransformer",
# data for each scenario
X_price=eur4_X_price,
X_time=eur4_X_time,
y=eur4_y,
# specify these for each data
no_tasks=eur4_y.shape[2],
flatten=False,
time_horizon=28,
# these have to be adjusted for some models
patience=2,
epochs = 1,
min_epochs=50,
# saving model
save_model_path=save_ITransformer,
# these go into the parameter grid
lr=1e-3,
dropout=0,
l2_weight=1e-5,
batch_size=4,
# specific to TimeNet
d_model=64,
d_ff=256,
n_heads=4,
e_layers=2
)
Data load example¶
In [362]:
load_data_object_8_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_8.pkl")
with open(load_data_object_8_file_path, "rb") as f:
structured_data_dict_8 = pickle.load(f)
print("Data dictionary 8 loaded successfully.")
eur8_X_price = structured_data_dict_8["EURUSD"].get("X_other")
eur8_X_time = structured_data_dict_8["EURUSD"].get("X_time")
eur8_y = structured_data_dict_8["EURUSD"].get("y")
eur8_y_columns = structured_data_dict_8["EURUSD"].get("y_columns")
eur8_X_columns = structured_data_dict_8["EURUSD"].get("X_other_columns")
eur8_X_price.shape
Data dictionary 8 loaded successfully.
Out[362]:
(3782, 60, 1)
Dictionaries¶
In [326]:
tickers = ["AAPL", "MSFT", "GE", "BAC", "C", "BTCUSDT", "EURUSD", "GOLD", "SP500"]
horizons = [60, 20, 1]
model_list = [
"Simple_MLP",
"Simple_LSTM",
"Simple_KAN",
"LSTM_KAN",
"TimesNet",
"ITransformer",
]
param_grids = {
"Simple_MLP": {
"lr": [5e-3],
"dropout": [0],
"l2_weight": [1e-5],
"batch_size": [512],
"hidden_layers": [3],
"hidden_dim": [64],
},
"Simple_LSTM": {
"lr": [5e-3],
"dropout": [0],
"l2_weight": [1e-5],
"batch_size": [16],
"hidden_layers": [4],
"hidden_dim": [64],
},
"Simple_KAN": {
"lr": [5e-3],
"dropout": [0],
"l2_weight": [1e-5],
"batch_size": [512],
"hidden_layers": [4],
"hidden_dim": [32],
"knots": [8],
"spline_power": [5],
},
"LSTM_KAN": {
"lr": [5e-4],
"dropout": [0],
"l2_weight": [1e-5],
"batch_size": [16],
"hidden_layers": [2],
"hidden_dim": [64],
"knots": [8],
"spline_power": [5],
},
"TimesNet": {
"lr": [5e-3],
"l2_weight": [5e-5],
"batch_size": [512],
"d_model": [128],
"d_ff": [256],
"n_heads": [4],
"e_layers": [4],
},
"ITransformer": {
"lr": [5e-3],
"l2_weight": [5e-5],
"batch_size": [512],
"d_model": [128],
"d_ff": [256],
"n_heads": [4],
"e_layers": [4],
},
}
Complete dictionaries¶
In [327]:
complete_models = model_list = [
"Simple_MLP",
"Simple_LSTM",
"Simple_KAN",
"Custom_KAN",
"LSTM_KAN",
"Custom_LSTM_KAN",
"TimesNet",
"ITransformer",
]
complete_param_grids = {
"Simple_MLP": {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [128, 512],
"hidden_layers": [2, 4],
"hidden_dim": [32, 64],
},
"Simple_LSTM": {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [16, 32],
"hidden_layers": [2, 4],
"hidden_dim": [32, 64],
},
"Simple_KAN": {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [256, 512],
"hidden_layers": [1, 2],
"hidden_dim": [16, 32],
"knots": [5, 8],
"spline_power": [3, 5],
},
"Custom_KAN": {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [256, 512],
"hidden_layers": [1, 2],
"hidden_dim": [16, 32],
"knots": [5, 8],
"spline_power": [3, 5],
},
"LSTM_KAN": {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [16, 32],
"hidden_layers": [2, 4],
"hidden_dim": [32, 64],
"knots": [5, 8],
"spline_power": [3, 5],
},
"Custom_LSTM_KAN": {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [16, 32],
"hidden_layers": [2, 4],
"hidden_dim": [32, 64],
"knots": [5, 8],
"spline_power": [3, 5],
},
"TimesNet": {
"lr": [5e-4, 5e-3],
"l2_weight": [5e-5, 5e-4],
"batch_size": [256, 512],
"d_model": [64, 128],
"d_ff": [128, 256],
"n_heads": [4, 6],
"e_layers": [2, 4],
},
"ITransformer": {
"lr": [5e-4, 5e-3],
"l2_weight": [5e-5, 5e-4],
"batch_size": [256, 512],
"d_model": [64, 128],
"d_ff": [128, 256],
"n_heads": [4, 6],
"e_layers": [2, 4],
},
}
Left out dictionaries¶
In [328]:
model_list_left_out = [
"Custom_KAN",
"Custom_LSTM_KAN"
]
param_grids_lest_out = {
"Custom_KAN": {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [256, 512],
"hidden_layers": [1, 2],
"hidden_dim": [16, 32],
"knots": [5, 8],
"spline_power": [3, 5],
},
"Custom_LSTM_KAN": {
"lr": [5e-4, 5e-3],
"dropout": [0, 0.1],
"l2_weight": [1e-5, 1e-4],
"batch_size": [16, 32],
"hidden_layers": [2, 4],
"hidden_dim": [32, 64],
"knots": [5, 8],
"spline_power": [3, 5],
}
}
Routine to run all models¶
In [133]:
import os
from collections import defaultdict
from sklearn.model_selection import ParameterGrid
import inspect
tickers = ["AAPL", "BAC", "BTCUSDT", "EURUSD", "GOLD", "SP500"]
horizons = [20, 1, 3, 5, 10]
model_list = [
"Simple_MLP", "Simple_LSTM", "Simple_KAN",
"LSTM_KAN", "TimesNet", "ITransformer"
]
FIXED_PARAMS = {
"Simple_MLP": dict(
lr=5e-3, dropout=0.0, l2_weight=1e-5, batch_size=64, hidden_layers=3, hidden_dim=512,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
"Simple_LSTM": dict(
lr=5e-3, dropout=0.0, l2_weight=1e-5, batch_size=16, hidden_layers=4, hidden_dim=128,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
"Simple_KAN": dict(
lr=5e-3, dropout=0.0, l2_weight=1e-5, batch_size=128, hidden_layers=3, hidden_dim=128, knots=8, spline_power=5,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
"LSTM_KAN": dict(
lr=5e-4, dropout=0.0, l2_weight=1e-5, batch_size=16, hidden_layers=2, hidden_dim=128, knots=8, spline_power=5,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
"TimesNet": dict(
lr=5e-3, l2_weight=5e-5, batch_size=512, d_model=128, d_ff=256, n_heads=4, e_layers=4, dropout=0.0,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
"ITransformer": dict(
lr=5e-3, l2_weight=5e-5, batch_size=512, d_model=128, d_ff=256, n_heads=4, e_layers=4, dropout=0.0,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
}
MODEL_IO = {
"Simple_MLP": dict(merge_price_time=False, flatten=True),
"Simple_KAN": dict(merge_price_time=False, flatten=True),
"Simple_LSTM": dict(merge_price_time=False, flatten=False),
"LSTM_KAN": dict(merge_price_time=False, flatten=False),
"TimesNet": dict(merge_price_time=False, flatten=False),
"ITransformer": dict(merge_price_time=False, flatten=False),
}
COMMON = dict(
use_nested_cv=False,
single_holdout=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
verbose=True,
)
MULTI_TASK_MODELS = {"TimesNet", "ITransformer"}
BASE_SAVE_DIR = os.path.join(root_folder, objects_relative_path, "runs")
results_store = defaultdict(lambda: defaultdict(dict))
def filter_kwargs_for_callable(fn, d):
sig = inspect.signature(fn)
return {k: v for k, v in d.items() if k in sig.parameters}
def run_all_models_for_all(
*,
tickers,
horizons,
model_list,
X_price_map,
X_time_map,
y_map,
base_save_dir,
fixed_params,
common_params,
model_io,
multitask_models,
param_grids=None,
grid_tickers=None,
grid_horizons=None,
results_store=None,
overwrite=True,
run_grid_with_single_holdout=None,
):
if results_store is None:
results_store = defaultdict(lambda: defaultdict(dict))
param_grids = param_grids or {}
os.makedirs(base_save_dir, exist_ok=True)
verbose = bool(common_params.get("verbose", False))
force_grid = bool(run_grid_with_single_holdout)
for ticker in tickers:
if ticker not in X_price_map or ticker not in y_map:
if verbose:
print(f"[skip] Missing data for ticker: {ticker}")
continue
Xp = X_price_map[ticker]
Xt = X_time_map.get(ticker, None)
Y = y_map[ticker]
for horizon in horizons:
for model_name in model_list:
if model_name not in model_io:
if verbose: print(f"[skip] MODEL_IO missing for model: {model_name}")
continue
if model_name not in fixed_params:
if verbose: print(f"[skip] FIXED_PARAMS missing for model: {model_name}")
continue
io = model_io[model_name]
fixed = dict(fixed_params[model_name])
ntasks = (Y.shape[2] if model_name in multitask_models else 1)
t_dict = results_store.setdefault(ticker, {})
h_dict = t_dict.setdefault(horizon, {})
if (not overwrite) and (model_name in h_dict):
if verbose:
print(f"[skip] {ticker} | H={horizon} | {model_name} (exists, overwrite=False)")
continue
if force_grid:
single_holdout = True
grid = param_grids.get(model_name, None)
else:
single_holdout = False
grid = None
save_path = os.path.join(base_save_dir, f"{ticker}", f"{model_name}_H{horizon}.pkl")
os.makedirs(os.path.dirname(save_path), exist_ok=True)
if verbose:
mode_note = " (grid + single-holdout)" if single_holdout else " (simple fit)"
print(f"\n=== {ticker} | H={horizon} | {model_name}{mode_note} | no_tasks={ntasks} ===")
merged = dict(common_params)
merged.update(fixed)
for k in (
"single_holdout", "param_grid", "use_nested_cv",
"no_tasks", "time_horizon", "merge_price_time", "flatten"
):
merged.pop(k, None)
if force_grid:
merged["use_nested_cv"] = False
merged = filter_kwargs_for_callable(train_and_evaluate_model, merged)
results, nested, best_model, used_params, y_data = train_and_evaluate_model(
model_type=model_name,
X_price=Xp,
X_time=Xt,
y=Y,
no_tasks=ntasks,
merge_price_time=io["merge_price_time"],
flatten=io["flatten"],
time_horizon=horizon,
save_model_path=save_path,
single_holdout=single_holdout,
param_grid=grid,
**merged
)
h_dict[model_name] = {
"results": results,
"model": best_model,
"used_params": used_params,
"y_data": y_data,
"save_path": save_path,
"grid_used": single_holdout,
}
return results_store
load_data_object_8_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_8.pkl")
with open(load_data_object_8_file_path, "rb") as f:
structured_data_dict_8 = pickle.load(f)
print("Data dictionary 8 loaded successfully.")
X_price_map, X_time_map, y_map = {}, {}, {}
missing = []
for t in tickers:
if t not in structured_data_dict_8:
missing.append(t)
continue
entry = structured_data_dict_8[t]
Xp = entry.get("X_other", None)
Xt = entry.get("X_time", None)
Y = entry.get("y", None)
if Xp is None or Y is None:
print(f"[WARN] {t}: missing {'X_other' if Xp is None else ''}{' and ' if (Xp is None and Y is None) else ''}{'y' if Y is None else ''} → skipping.")
continue
Xp = np.asarray(Xp, dtype=float)
Xt = None if Xt is None else np.asarray(Xt, dtype=float)
Y = np.asarray(Y, dtype=float)
if Xp.ndim != 3 or Y.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_other={Xp.shape}, y={Y.shape} → skipping.")
continue
if Xt is not None and Xt.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_time={Xt.shape} → setting to None.")
Xt = None
X_price_map[t] = Xp
X_time_map[t] = Xt
y_map[t] = Y
if missing:
print(f"[INFO] Missing tickers in data dict (skipped): {missing}")
if "MSFT" in y_map:
print("MSFT shapes:",
"X_other", X_price_map["MSFT"].shape,
"X_time", None if X_time_map["MSFT"] is None else X_time_map["MSFT"].shape,
"y", y_map["MSFT"].shape)
Data dictionary 8 loaded successfully.
In [358]:
X_price_map["EURUSD"].shape
Out[358]:
(3782, 60, 1)
Helper to merge GARCH results with ML ones¶
In [134]:
import os
import pickle
import numpy as np
def run_garch_over_dict(
structured_data_dict,
tickers,
horizons,
results_dict,
*,
x_price_key="X_other",
y_key="y",
anchor_step=55,
anchor_feature_idx=0,
y_feature_idx=0,
split_ratio=0.8,
roll_window=250,
mean_mode="Zero",
candidates=None,
sim_paths=2000,
metric_horizons=(1, 3, 5, 10, 20, -1),
verbose=True,
save_dir=None,
):
def _get_x_price(d):
x = d.get(x_price_key)
if x is None:
x = d.get("X_price")
return x
if save_dir:
os.makedirs(save_dir, exist_ok=True)
for ticker in tickers:
if ticker not in structured_data_dict:
if verbose:
print(f"[GARCH] Skipping {ticker}: not in structured_data_dict.")
continue
bundle = structured_data_dict[ticker]
X_price = _get_x_price(bundle)
y_all = bundle.get(y_key)
if X_price is None or y_all is None:
if verbose:
print(f"[GARCH] Skipping {ticker}: missing X or y (keys tried: {x_price_key}/X_price and {y_key}).")
continue
if X_price.ndim != 3:
raise ValueError(f"[GARCH] {ticker}: X_price must be 3D (N, T, F). Got {X_price.shape}.")
if y_all.ndim != 3:
raise ValueError(f"[GARCH] {ticker}: y must be 3D (N, H, C). Got {y_all.shape}.")
if anchor_step >= X_price.shape[1] or anchor_feature_idx >= X_price.shape[2]:
raise IndexError(f"[GARCH] {ticker}: anchor_step/feature out of bounds for X_price shape {X_price.shape}.")
if y_feature_idx >= y_all.shape[2]:
raise IndexError(f"[GARCH] {ticker}: y_feature_idx out of bounds for y shape {y_all.shape}.")
X_anchors = X_price[:, anchor_step, anchor_feature_idx].astype(float)
Y_fwd = y_all[:, :, y_feature_idx].astype(float)
results_dict.setdefault(ticker, {})
max_h_avail = Y_fwd.shape[1]
for H in horizons:
if H > max_h_avail:
if verbose:
print(f"[GARCH] {ticker}: skip H={H} (only {max_h_avail} steps available).")
continue
if verbose:
print(f"\n[GARCH] {ticker} — H={H} (step={anchor_step}, x_feat={anchor_feature_idx}, y_feat={y_feature_idx})")
metrics, best_params, _, panel_df, y_pair = evaluate_vol_on_anchors_rolling_split(
X_anchors=X_anchors,
Y_fwd=Y_fwd,
horizon=H,
split_ratio=split_ratio,
roll_window=roll_window,
mean_mode=mean_mode,
candidates=candidates,
sim_paths=sim_paths,
horizons=metric_horizons,
verbose=verbose,
)
results_dict[ticker].setdefault(H, {})
results_dict[ticker][H]["GARCH"] = {
"results": metrics,
"best_params": best_params,
"model": None,
"y_data": y_pair,
"panel": panel_df,
"params": {
"split_ratio": split_ratio,
"roll_window": roll_window,
"mean_mode": mean_mode,
"candidates": candidates,
"sim_paths": sim_paths,
},
}
if save_dir:
out_path = os.path.join(save_dir, f"GARCH_{ticker}_H{H}.pkl")
to_save = {
"metrics": metrics,
"panel_head": panel_df.head(10),
"y_data_shape": y_pair.shape,
"params": {
"split_ratio": split_ratio,
"roll_window": roll_window,
"mean_mode": mean_mode,
"candidates": candidates,
"sim_paths": sim_paths,
},
}
try:
with open(out_path, "wb") as f:
pickle.dump(to_save, f)
if verbose:
print(f"[GARCH] Saved summary: {out_path}")
except Exception as e:
if verbose:
print(f"[GARCH] Warning: could not pickle summary for {ticker}, H={H}: {e}")
return results_dict
Call to run ML models¶
In [ ]:
BASE_SAVE_DIR = os.path.join(root_folder, objects_relative_path, "runs_timeseries")
os.makedirs(BASE_SAVE_DIR, exist_ok=True)
results_store = run_all_models_for_all(
tickers=tickers,
horizons=horizons,
model_list=model_list,
X_price_map=X_price_map,
X_time_map=X_time_map,
y_map=y_map,
base_save_dir=BASE_SAVE_DIR,
fixed_params=FIXED_PARAMS,
common_params=COMMON,
model_io=MODEL_IO,
multitask_models=MULTI_TASK_MODELS,
param_grids=complete_param_grids,
grid_tickers=tickers,
grid_horizons=horizons,
results_store=None,
overwrite=True,
)
print("\nAll runs completed. Example access:")
ex_ticker, ex_H, ex_model = "MSFT", 60, "Simple_MLP"
if ex_ticker in results_store and ex_H in results_store[ex_ticker] and ex_model in results_store[ex_ticker][ex_H]:
bundle = results_store[ex_ticker][ex_H][ex_model]
print(f"- {ex_ticker} H={ex_H} {ex_model} metrics keys:", list(bundle["results"].keys())[:5], "...")
print(f"- saved model path: {bundle['save_path']}")
Call to run GARCH extension¶
In [ ]:
tickers = ["AAPL", "BAC", "BTCUSDT", "EURUSD", "GOLD", "SP500"]
horizons = [20, 1, 3, 5, 10]
all_results = run_garch_over_dict(
structured_data_dict=structured_data_dict_8,
tickers=["EURUSD","GOLD"],
horizons=[1,5,20],
results_dict={},
anchor_step=20,
anchor_feature_idx=0,
y_feature_idx=0,
split_ratio=0.8,
roll_window=250,
mean_mode="Zero",
candidates=[("GARCH", 1, 1, "t")],
sim_paths=0,
metric_horizons=(1,5,20,-1),
verbose=True,
save_dir=None
)
[GARCH] AAPL — horizon=20 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2018, n_fit=1614, horizon=20
[fit] Best by BIC: ('GARCH', 2, 2, 't'), BIC=-6221.349
[best config] ('GARCH', 2, 2, 't')
[best params (train fit)]
omega: 0.005131
alpha[1]: 0.000000
alpha[2]: 0.000932
beta[1]: 0.000003
beta[2]: 0.101618
nu: 2.366449
[metrics]
1 day(s)
MAE : 1247.361545
RMSE : 1247.391571
R2 : -20830.404952
Pearson r : -0.057371
QLIKE : 0.535415
3 day(s)
MAE : 1247.598175
RMSE : 1247.629979
R2 : -19695.647025
Pearson r : -0.010520
QLIKE : 0.553156
5 day(s)
MAE : 1247.856658
RMSE : 1247.888798
R2 : -19486.389292
Pearson r : -0.004070
QLIKE : 0.556909
10 day(s)
MAE : 1248.069276
RMSE : 1248.101633
R2 : -19332.684614
Pearson r : -0.000541
QLIKE : 0.559523
20 day(s)
MAE : 1248.151050
RMSE : 1248.183505
R2 : -19252.970840
Pearson r : 0.001326
QLIKE : 0.559622
full horizon
MAE : 1248.151050
RMSE : 1248.183505
R2 : -19252.970840
Pearson r : 0.001326
QLIKE : 0.559622
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_AAPL_H20.pkl
[GARCH] AAPL — horizon=1 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2018, n_fit=1614, horizon=1
[fit] Best by BIC: ('GARCH', 2, 2, 't'), BIC=-6221.349
[best config] ('GARCH', 2, 2, 't')
[best params (train fit)]
omega: 0.005131
alpha[1]: 0.000000
alpha[2]: 0.000932
beta[1]: 0.000003
beta[2]: 0.101618
nu: 2.366449
[metrics]
1 day(s)
MAE : 1247.361545
RMSE : 1247.391571
R2 : -20830.404952
Pearson r : -0.057371
QLIKE : 0.535415
full horizon
MAE : 1247.361545
RMSE : 1247.391571
R2 : -20830.404952
Pearson r : -0.057371
QLIKE : 0.535415
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_AAPL_H1.pkl
[GARCH] AAPL — horizon=3 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2018, n_fit=1614, horizon=3
[fit] Best by BIC: ('GARCH', 2, 2, 't'), BIC=-6221.349
[best config] ('GARCH', 2, 2, 't')
[best params (train fit)]
omega: 0.005131
alpha[1]: 0.000000
alpha[2]: 0.000932
beta[1]: 0.000003
beta[2]: 0.101618
nu: 2.366449
[metrics]
1 day(s)
MAE : 1247.361545
RMSE : 1247.391571
R2 : -20830.404952
Pearson r : -0.057371
QLIKE : 0.535415
3 day(s)
MAE : 1247.598175
RMSE : 1247.629979
R2 : -19695.647025
Pearson r : -0.010520
QLIKE : 0.553156
full horizon
MAE : 1247.598175
RMSE : 1247.629979
R2 : -19695.647025
Pearson r : -0.010520
QLIKE : 0.553156
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_AAPL_H3.pkl
[GARCH] AAPL — horizon=5 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2018, n_fit=1614, horizon=5
/opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 4. The message is: Inequality constraints incompatible See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 8. The message is: Positive directional derivative for linesearch See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 4. The message is: Inequality constraints incompatible See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 4. The message is: Inequality constraints incompatible See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 8. The message is: Positive directional derivative for linesearch See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 4. The message is: Inequality constraints incompatible See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 4. The message is: Inequality constraints incompatible See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 8. The message is: Positive directional derivative for linesearch See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 4. The message is: Inequality constraints incompatible See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 4. The message is: Inequality constraints incompatible See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 8. The message is: Positive directional derivative for linesearch See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 4. The message is: Inequality constraints incompatible See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 4. The message is: Inequality constraints incompatible See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 8. The message is: Positive directional derivative for linesearch See scipy.optimize.fmin_slsqp for code meaning. warnings.warn( /opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/arch/univariate/base.py:768: ConvergenceWarning: The optimizer returned code 4. The message is: Inequality constraints incompatible See scipy.optimize.fmin_slsqp for code meaning. warnings.warn(
[fit] Best by BIC: ('GARCH', 2, 2, 't'), BIC=-6221.349
[best config] ('GARCH', 2, 2, 't')
[best params (train fit)]
omega: 0.005131
alpha[1]: 0.000000
alpha[2]: 0.000932
beta[1]: 0.000003
beta[2]: 0.101618
nu: 2.366449
[metrics]
1 day(s)
MAE : 1247.361545
RMSE : 1247.391571
R2 : -20830.404952
Pearson r : -0.057371
QLIKE : 0.535415
3 day(s)
MAE : 1247.598175
RMSE : 1247.629979
R2 : -19695.647025
Pearson r : -0.010520
QLIKE : 0.553156
5 day(s)
MAE : 1247.856658
RMSE : 1247.888798
R2 : -19486.389292
Pearson r : -0.004070
QLIKE : 0.556909
full horizon
MAE : 1247.856658
RMSE : 1247.888798
R2 : -19486.389292
Pearson r : -0.004070
QLIKE : 0.556909
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_AAPL_H5.pkl
[GARCH] AAPL — horizon=10 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2018, n_fit=1614, horizon=10
[fit] Best by BIC: ('GARCH', 2, 2, 't'), BIC=-6221.349
[best config] ('GARCH', 2, 2, 't')
[best params (train fit)]
omega: 0.005131
alpha[1]: 0.000000
alpha[2]: 0.000932
beta[1]: 0.000003
beta[2]: 0.101618
nu: 2.366449
[metrics]
1 day(s)
MAE : 1247.361545
RMSE : 1247.391571
R2 : -20830.404952
Pearson r : -0.057371
QLIKE : 0.535415
3 day(s)
MAE : 1247.598175
RMSE : 1247.629979
R2 : -19695.647025
Pearson r : -0.010520
QLIKE : 0.553156
5 day(s)
MAE : 1247.856658
RMSE : 1247.888798
R2 : -19486.389292
Pearson r : -0.004070
QLIKE : 0.556909
10 day(s)
MAE : 1248.069276
RMSE : 1248.101633
R2 : -19332.684614
Pearson r : -0.000541
QLIKE : 0.559523
full horizon
MAE : 1248.069276
RMSE : 1248.101633
R2 : -19332.684614
Pearson r : -0.000541
QLIKE : 0.559523
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_AAPL_H10.pkl
[GARCH] BAC — horizon=20 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2018, n_fit=1614, horizon=20
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=971.679
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.029588
alpha[1]: 0.421756
beta[1]: 0.376107
nu: 7.692989
[metrics]
1 day(s)
MAE : 18.782275
RMSE : 71.493605
R2 : -83.822500
Pearson r : -0.016550
QLIKE : 0.585551
3 day(s)
MAE : 18.238195
RMSE : 59.861951
R2 : -58.429355
Pearson r : -0.021610
QLIKE : 0.553894
5 day(s)
MAE : 17.798800
RMSE : 51.761570
R2 : -43.420143
Pearson r : -0.021995
QLIKE : 0.528120
10 day(s)
MAE : 17.123131
RMSE : 40.124812
R2 : -25.690081
Pearson r : -0.019958
QLIKE : 0.496919
20 day(s)
MAE : 16.538826
RMSE : 30.848084
R2 : -14.780593
Pearson r : -0.016033
QLIKE : 0.466328
full horizon
MAE : 16.538826
RMSE : 30.848084
R2 : -14.780593
Pearson r : -0.016033
QLIKE : 0.466328
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_BAC_H20.pkl
[GARCH] BAC — horizon=1 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2018, n_fit=1614, horizon=1
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=971.679
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.029588
alpha[1]: 0.421756
beta[1]: 0.376107
nu: 7.692989
[metrics]
1 day(s)
MAE : 18.782275
RMSE : 71.493605
R2 : -83.822500
Pearson r : -0.016550
QLIKE : 0.585551
full horizon
MAE : 18.782275
RMSE : 71.493605
R2 : -83.822500
Pearson r : -0.016550
QLIKE : 0.585551
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_BAC_H1.pkl
[GARCH] BAC — horizon=3 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2018, n_fit=1614, horizon=3
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=971.679
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.029588
alpha[1]: 0.421756
beta[1]: 0.376107
nu: 7.692989
[metrics]
1 day(s)
MAE : 18.782275
RMSE : 71.493605
R2 : -83.822500
Pearson r : -0.016550
QLIKE : 0.585551
3 day(s)
MAE : 18.238195
RMSE : 59.861951
R2 : -58.429355
Pearson r : -0.021610
QLIKE : 0.553894
full horizon
MAE : 18.238195
RMSE : 59.861951
R2 : -58.429355
Pearson r : -0.021610
QLIKE : 0.553894
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_BAC_H3.pkl
[GARCH] BAC — horizon=5 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2018, n_fit=1614, horizon=5
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=971.679
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.029588
alpha[1]: 0.421756
beta[1]: 0.376107
nu: 7.692989
[metrics]
1 day(s)
MAE : 18.782275
RMSE : 71.493605
R2 : -83.822500
Pearson r : -0.016550
QLIKE : 0.585551
3 day(s)
MAE : 18.238195
RMSE : 59.861951
R2 : -58.429355
Pearson r : -0.021610
QLIKE : 0.553894
5 day(s)
MAE : 17.798800
RMSE : 51.761570
R2 : -43.420143
Pearson r : -0.021995
QLIKE : 0.528120
full horizon
MAE : 17.798800
RMSE : 51.761570
R2 : -43.420143
Pearson r : -0.021995
QLIKE : 0.528120
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_BAC_H5.pkl
[GARCH] BAC — horizon=10 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2018, n_fit=1614, horizon=10
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=971.679
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.029588
alpha[1]: 0.421756
beta[1]: 0.376107
nu: 7.692989
[metrics]
1 day(s)
MAE : 18.782275
RMSE : 71.493605
R2 : -83.822500
Pearson r : -0.016550
QLIKE : 0.585551
3 day(s)
MAE : 18.238195
RMSE : 59.861951
R2 : -58.429355
Pearson r : -0.021610
QLIKE : 0.553894
5 day(s)
MAE : 17.798800
RMSE : 51.761570
R2 : -43.420143
Pearson r : -0.021995
QLIKE : 0.528120
10 day(s)
MAE : 17.123131
RMSE : 40.124812
R2 : -25.690081
Pearson r : -0.019958
QLIKE : 0.496919
full horizon
MAE : 17.123131
RMSE : 40.124812
R2 : -25.690081
Pearson r : -0.019958
QLIKE : 0.496919
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_BAC_H10.pkl
[GARCH] BTCUSDT — horizon=20 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2399, n_fit=1919, horizon=20
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=1478.049
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.059681
alpha[1]: 0.956642
beta[1]: 0.014088
nu: 4.471211
[metrics]
1 day(s)
MAE : 207.204437
RMSE : 635.349959
R2 : -4782.854861
Pearson r : -0.029780
QLIKE : 0.488054
3 day(s)
MAE : 285.222552
RMSE : 652.221901
R2 : -5039.603758
Pearson r : -0.000142
QLIKE : 0.497030
5 day(s)
MAE : 360.229661
RMSE : 679.931411
R2 : -5478.717553
Pearson r : -0.000500
QLIKE : 0.513093
10 day(s)
MAE : 535.437861
RMSE : 779.241332
R2 : -7194.606480
Pearson r : -0.001405
QLIKE : 0.553222
20 day(s)
MAE : 839.134871
RMSE : 1022.317360
R2 : -12388.343878
Pearson r : -0.001163
QLIKE : 0.601529
full horizon
MAE : 839.134871
RMSE : 1022.317360
R2 : -12388.343878
Pearson r : -0.001163
QLIKE : 0.601529
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_BTCUSDT_H20.pkl
[GARCH] BTCUSDT — horizon=1 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2399, n_fit=1919, horizon=1
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=1478.049
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.059681
alpha[1]: 0.956642
beta[1]: 0.014088
nu: 4.471211
[metrics]
1 day(s)
MAE : 207.204437
RMSE : 635.349959
R2 : -4782.854861
Pearson r : -0.029780
QLIKE : 0.488054
full horizon
MAE : 207.204437
RMSE : 635.349959
R2 : -4782.854861
Pearson r : -0.029780
QLIKE : 0.488054
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_BTCUSDT_H1.pkl
[GARCH] BTCUSDT — horizon=3 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2399, n_fit=1919, horizon=3
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=1478.049
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.059681
alpha[1]: 0.956642
beta[1]: 0.014088
nu: 4.471211
[metrics]
1 day(s)
MAE : 207.204437
RMSE : 635.349959
R2 : -4782.854861
Pearson r : -0.029780
QLIKE : 0.488054
3 day(s)
MAE : 285.222552
RMSE : 652.221901
R2 : -5039.603758
Pearson r : -0.000142
QLIKE : 0.497030
full horizon
MAE : 285.222552
RMSE : 652.221901
R2 : -5039.603758
Pearson r : -0.000142
QLIKE : 0.497030
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_BTCUSDT_H3.pkl
[GARCH] BTCUSDT — horizon=5 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2399, n_fit=1919, horizon=5
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=1478.049
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.059681
alpha[1]: 0.956642
beta[1]: 0.014088
nu: 4.471211
[metrics]
1 day(s)
MAE : 207.204437
RMSE : 635.349959
R2 : -4782.854861
Pearson r : -0.029780
QLIKE : 0.488054
3 day(s)
MAE : 285.222552
RMSE : 652.221901
R2 : -5039.603758
Pearson r : -0.000142
QLIKE : 0.497030
5 day(s)
MAE : 360.229661
RMSE : 679.931411
R2 : -5478.717553
Pearson r : -0.000500
QLIKE : 0.513093
full horizon
MAE : 360.229661
RMSE : 679.931411
R2 : -5478.717553
Pearson r : -0.000500
QLIKE : 0.513093
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_BTCUSDT_H5.pkl
[GARCH] BTCUSDT — horizon=10 (anchors: step=55, feat=0; y_feat=0)
[eval] B=2399, n_fit=1919, horizon=10
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=1478.049
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.059681
alpha[1]: 0.956642
beta[1]: 0.014088
nu: 4.471211
[metrics]
1 day(s)
MAE : 207.204437
RMSE : 635.349959
R2 : -4782.854861
Pearson r : -0.029780
QLIKE : 0.488054
3 day(s)
MAE : 285.222552
RMSE : 652.221901
R2 : -5039.603758
Pearson r : -0.000142
QLIKE : 0.497030
5 day(s)
MAE : 360.229661
RMSE : 679.931411
R2 : -5478.717553
Pearson r : -0.000500
QLIKE : 0.513093
10 day(s)
MAE : 535.437861
RMSE : 779.241332
R2 : -7194.606480
Pearson r : -0.001405
QLIKE : 0.553222
full horizon
MAE : 535.437861
RMSE : 779.241332
R2 : -7194.606480
Pearson r : -0.001405
QLIKE : 0.553222
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_BTCUSDT_H10.pkl
[GARCH] EURUSD — horizon=20 (anchors: step=55, feat=0; y_feat=0)
[eval] B=3782, n_fit=3025, horizon=20
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=5848.783
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.066681
alpha[1]: 0.131620
beta[1]: 0.724793
nu: 4.538773
[metrics]
1 day(s)
MAE : 0.161044
RMSE : 0.257576
R2 : -0.453556
Pearson r : 0.184611
QLIKE : 0.580218
3 day(s)
MAE : 0.159703
RMSE : 0.256700
R2 : -0.454827
Pearson r : 0.169722
QLIKE : 0.578770
5 day(s)
MAE : 0.158073
RMSE : 0.255028
R2 : -0.452251
Pearson r : 0.169864
QLIKE : 0.575421
10 day(s)
MAE : 0.157020
RMSE : 0.254732
R2 : -0.462719
Pearson r : 0.139592
QLIKE : 0.575508
20 day(s)
MAE : 0.156029
RMSE : 0.254118
R2 : -0.472847
Pearson r : 0.108493
QLIKE : 0.570917
full horizon
MAE : 0.156029
RMSE : 0.254118
R2 : -0.472847
Pearson r : 0.108493
QLIKE : 0.570917
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_EURUSD_H20.pkl
[GARCH] EURUSD — horizon=1 (anchors: step=55, feat=0; y_feat=0)
[eval] B=3782, n_fit=3025, horizon=1
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=5848.783
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.066681
alpha[1]: 0.131620
beta[1]: 0.724793
nu: 4.538773
[metrics]
1 day(s)
MAE : 0.161044
RMSE : 0.257576
R2 : -0.453556
Pearson r : 0.184611
QLIKE : 0.580218
full horizon
MAE : 0.161044
RMSE : 0.257576
R2 : -0.453556
Pearson r : 0.184611
QLIKE : 0.580218
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_EURUSD_H1.pkl
[GARCH] EURUSD — horizon=3 (anchors: step=55, feat=0; y_feat=0)
[eval] B=3782, n_fit=3025, horizon=3
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=5848.783
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.066681
alpha[1]: 0.131620
beta[1]: 0.724793
nu: 4.538773
[metrics]
1 day(s)
MAE : 0.161044
RMSE : 0.257576
R2 : -0.453556
Pearson r : 0.184611
QLIKE : 0.580218
3 day(s)
MAE : 0.159703
RMSE : 0.256700
R2 : -0.454827
Pearson r : 0.169722
QLIKE : 0.578770
full horizon
MAE : 0.159703
RMSE : 0.256700
R2 : -0.454827
Pearson r : 0.169722
QLIKE : 0.578770
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_EURUSD_H3.pkl
[GARCH] EURUSD — horizon=5 (anchors: step=55, feat=0; y_feat=0)
[eval] B=3782, n_fit=3025, horizon=5
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=5848.783
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.066681
alpha[1]: 0.131620
beta[1]: 0.724793
nu: 4.538773
[metrics]
1 day(s)
MAE : 0.161044
RMSE : 0.257576
R2 : -0.453556
Pearson r : 0.184611
QLIKE : 0.580218
3 day(s)
MAE : 0.159703
RMSE : 0.256700
R2 : -0.454827
Pearson r : 0.169722
QLIKE : 0.578770
5 day(s)
MAE : 0.158073
RMSE : 0.255028
R2 : -0.452251
Pearson r : 0.169864
QLIKE : 0.575421
full horizon
MAE : 0.158073
RMSE : 0.255028
R2 : -0.452251
Pearson r : 0.169864
QLIKE : 0.575421
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_EURUSD_H5.pkl
[GARCH] EURUSD — horizon=10 (anchors: step=55, feat=0; y_feat=0)
[eval] B=3782, n_fit=3025, horizon=10
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=5848.783
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.066681
alpha[1]: 0.131620
beta[1]: 0.724793
nu: 4.538773
[metrics]
1 day(s)
MAE : 0.161044
RMSE : 0.257576
R2 : -0.453556
Pearson r : 0.184611
QLIKE : 0.580218
3 day(s)
MAE : 0.159703
RMSE : 0.256700
R2 : -0.454827
Pearson r : 0.169722
QLIKE : 0.578770
5 day(s)
MAE : 0.158073
RMSE : 0.255028
R2 : -0.452251
Pearson r : 0.169864
QLIKE : 0.575421
10 day(s)
MAE : 0.157020
RMSE : 0.254732
R2 : -0.462719
Pearson r : 0.139592
QLIKE : 0.575508
full horizon
MAE : 0.157020
RMSE : 0.254732
R2 : -0.462719
Pearson r : 0.139592
QLIKE : 0.575508
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_EURUSD_H10.pkl
[GARCH] GOLD — horizon=20 (anchors: step=55, feat=0; y_feat=0)
[eval] B=5534, n_fit=4427, horizon=20
[fit] Best by BIC: ('GARCH', 2, 2, 't'), BIC=6662.170
[best config] ('GARCH', 2, 2, 't')
[best params (train fit)]
omega: 0.042837
alpha[1]: 0.265957
alpha[2]: 0.000000
beta[1]: 0.157845
beta[2]: 0.434977
nu: 6.781479
[metrics]
1 day(s)
MAE : 0.452322
RMSE : 0.871243
R2 : -0.767577
Pearson r : 0.180690
QLIKE : 0.548914
3 day(s)
MAE : 0.441101
RMSE : 0.797136
R2 : -0.478434
Pearson r : 0.188963
QLIKE : 0.541996
5 day(s)
MAE : 0.433882
RMSE : 0.764818
R2 : -0.357113
Pearson r : 0.190094
QLIKE : 0.536240
10 day(s)
MAE : 0.431766
RMSE : 0.723196
R2 : -0.199885
Pearson r : 0.187273
QLIKE : 0.535947
20 day(s)
MAE : 0.445144
RMSE : 0.703653
R2 : -0.119146
Pearson r : 0.149233
QLIKE : 0.540202
full horizon
MAE : 0.445144
RMSE : 0.703653
R2 : -0.119146
Pearson r : 0.149233
QLIKE : 0.540202
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_GOLD_H20.pkl
[GARCH] GOLD — horizon=1 (anchors: step=55, feat=0; y_feat=0)
[eval] B=5534, n_fit=4427, horizon=1
[fit] Best by BIC: ('GARCH', 2, 2, 't'), BIC=6662.170
[best config] ('GARCH', 2, 2, 't')
[best params (train fit)]
omega: 0.042837
alpha[1]: 0.265957
alpha[2]: 0.000000
beta[1]: 0.157845
beta[2]: 0.434977
nu: 6.781479
[metrics]
1 day(s)
MAE : 0.452322
RMSE : 0.871243
R2 : -0.767577
Pearson r : 0.180690
QLIKE : 0.548914
full horizon
MAE : 0.452322
RMSE : 0.871243
R2 : -0.767577
Pearson r : 0.180690
QLIKE : 0.548914
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_GOLD_H1.pkl
[GARCH] GOLD — horizon=3 (anchors: step=55, feat=0; y_feat=0)
[eval] B=5534, n_fit=4427, horizon=3
[fit] Best by BIC: ('GARCH', 2, 2, 't'), BIC=6662.170
[best config] ('GARCH', 2, 2, 't')
[best params (train fit)]
omega: 0.042837
alpha[1]: 0.265957
alpha[2]: 0.000000
beta[1]: 0.157845
beta[2]: 0.434977
nu: 6.781479
[metrics]
1 day(s)
MAE : 0.452322
RMSE : 0.871243
R2 : -0.767577
Pearson r : 0.180690
QLIKE : 0.548914
3 day(s)
MAE : 0.441101
RMSE : 0.797136
R2 : -0.478434
Pearson r : 0.188963
QLIKE : 0.541996
full horizon
MAE : 0.441101
RMSE : 0.797136
R2 : -0.478434
Pearson r : 0.188963
QLIKE : 0.541996
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_GOLD_H3.pkl
[GARCH] GOLD — horizon=5 (anchors: step=55, feat=0; y_feat=0)
[eval] B=5534, n_fit=4427, horizon=5
[fit] Best by BIC: ('GARCH', 2, 2, 't'), BIC=6662.170
[best config] ('GARCH', 2, 2, 't')
[best params (train fit)]
omega: 0.042837
alpha[1]: 0.265957
alpha[2]: 0.000000
beta[1]: 0.157845
beta[2]: 0.434977
nu: 6.781479
[metrics]
1 day(s)
MAE : 0.452322
RMSE : 0.871243
R2 : -0.767577
Pearson r : 0.180690
QLIKE : 0.548914
3 day(s)
MAE : 0.441101
RMSE : 0.797136
R2 : -0.478434
Pearson r : 0.188963
QLIKE : 0.541996
5 day(s)
MAE : 0.433882
RMSE : 0.764818
R2 : -0.357113
Pearson r : 0.190094
QLIKE : 0.536240
full horizon
MAE : 0.433882
RMSE : 0.764818
R2 : -0.357113
Pearson r : 0.190094
QLIKE : 0.536240
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_GOLD_H5.pkl
[GARCH] GOLD — horizon=10 (anchors: step=55, feat=0; y_feat=0)
[eval] B=5534, n_fit=4427, horizon=10
[fit] Best by BIC: ('GARCH', 2, 2, 't'), BIC=6662.170
[best config] ('GARCH', 2, 2, 't')
[best params (train fit)]
omega: 0.042837
alpha[1]: 0.265957
alpha[2]: 0.000000
beta[1]: 0.157845
beta[2]: 0.434977
nu: 6.781479
[metrics]
1 day(s)
MAE : 0.452322
RMSE : 0.871243
R2 : -0.767577
Pearson r : 0.180690
QLIKE : 0.548914
3 day(s)
MAE : 0.441101
RMSE : 0.797136
R2 : -0.478434
Pearson r : 0.188963
QLIKE : 0.541996
5 day(s)
MAE : 0.433882
RMSE : 0.764818
R2 : -0.357113
Pearson r : 0.190094
QLIKE : 0.536240
10 day(s)
MAE : 0.431766
RMSE : 0.723196
R2 : -0.199885
Pearson r : 0.187273
QLIKE : 0.535947
full horizon
MAE : 0.431766
RMSE : 0.723196
R2 : -0.199885
Pearson r : 0.187273
QLIKE : 0.535947
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_GOLD_H10.pkl
[GARCH] SP500 — horizon=20 (anchors: step=55, feat=0; y_feat=0)
[eval] B=3466, n_fit=2772, horizon=20
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=1457.339
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.026327
alpha[1]: 0.498665
beta[1]: 0.342041
nu: 7.394411
[metrics]
1 day(s)
MAE : 6.334555
RMSE : 67.344524
R2 : -564.611368
Pearson r : 0.075889
QLIKE : 1.078707
3 day(s)
MAE : 5.636203
RMSE : 57.755112
R2 : -414.980930
Pearson r : 0.075584
QLIKE : 1.065490
5 day(s)
MAE : 5.090601
RMSE : 50.512924
R2 : -317.179877
Pearson r : 0.064283
QLIKE : 1.074019
10 day(s)
MAE : 4.106294
RMSE : 38.795260
R2 : -186.657621
Pearson r : 0.051327
QLIKE : 1.074098
20 day(s)
MAE : 3.102543
RMSE : 27.942745
R2 : -96.340929
Pearson r : 0.036919
QLIKE : 1.055650
full horizon
MAE : 3.102543
RMSE : 27.942745
R2 : -96.340929
Pearson r : 0.036919
QLIKE : 1.055650
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_SP500_H20.pkl
[GARCH] SP500 — horizon=1 (anchors: step=55, feat=0; y_feat=0)
[eval] B=3466, n_fit=2772, horizon=1
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=1457.339
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.026327
alpha[1]: 0.498665
beta[1]: 0.342041
nu: 7.394411
[metrics]
1 day(s)
MAE : 6.334555
RMSE : 67.344524
R2 : -564.611368
Pearson r : 0.075889
QLIKE : 1.078707
full horizon
MAE : 6.334555
RMSE : 67.344524
R2 : -564.611368
Pearson r : 0.075889
QLIKE : 1.078707
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_SP500_H1.pkl
[GARCH] SP500 — horizon=3 (anchors: step=55, feat=0; y_feat=0)
[eval] B=3466, n_fit=2772, horizon=3
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=1457.339
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.026327
alpha[1]: 0.498665
beta[1]: 0.342041
nu: 7.394411
[metrics]
1 day(s)
MAE : 6.334555
RMSE : 67.344524
R2 : -564.611368
Pearson r : 0.075889
QLIKE : 1.078707
3 day(s)
MAE : 5.636203
RMSE : 57.755112
R2 : -414.980930
Pearson r : 0.075584
QLIKE : 1.065490
full horizon
MAE : 5.636203
RMSE : 57.755112
R2 : -414.980930
Pearson r : 0.075584
QLIKE : 1.065490
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_SP500_H3.pkl
[GARCH] SP500 — horizon=5 (anchors: step=55, feat=0; y_feat=0)
[eval] B=3466, n_fit=2772, horizon=5
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=1457.339
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.026327
alpha[1]: 0.498665
beta[1]: 0.342041
nu: 7.394411
[metrics]
1 day(s)
MAE : 6.334555
RMSE : 67.344524
R2 : -564.611368
Pearson r : 0.075889
QLIKE : 1.078707
3 day(s)
MAE : 5.636203
RMSE : 57.755112
R2 : -414.980930
Pearson r : 0.075584
QLIKE : 1.065490
5 day(s)
MAE : 5.090601
RMSE : 50.512924
R2 : -317.179877
Pearson r : 0.064283
QLIKE : 1.074019
full horizon
MAE : 5.090601
RMSE : 50.512924
R2 : -317.179877
Pearson r : 0.064283
QLIKE : 1.074019
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_SP500_H5.pkl
[GARCH] SP500 — horizon=10 (anchors: step=55, feat=0; y_feat=0)
[eval] B=3466, n_fit=2772, horizon=10
[fit] Best by BIC: ('GARCH', 1, 1, 't'), BIC=1457.339
[best config] ('GARCH', 1, 1, 't')
[best params (train fit)]
omega: 0.026327
alpha[1]: 0.498665
beta[1]: 0.342041
nu: 7.394411
[metrics]
1 day(s)
MAE : 6.334555
RMSE : 67.344524
R2 : -564.611368
Pearson r : 0.075889
QLIKE : 1.078707
3 day(s)
MAE : 5.636203
RMSE : 57.755112
R2 : -414.980930
Pearson r : 0.075584
QLIKE : 1.065490
5 day(s)
MAE : 5.090601
RMSE : 50.512924
R2 : -317.179877
Pearson r : 0.064283
QLIKE : 1.074019
10 day(s)
MAE : 4.106294
RMSE : 38.795260
R2 : -186.657621
Pearson r : 0.051327
QLIKE : 1.074098
full horizon
MAE : 4.106294
RMSE : 38.795260
R2 : -186.657621
Pearson r : 0.051327
QLIKE : 1.074098
[GARCH] Saved: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/garch_models/GARCH_SP500_H10.pkl
Test call¶
In [135]:
import os, pickle, gzip, tempfile
#tickers = ["GOLD", "BTCUSDT", "SP500"]
tickers = ["EURUSD", "GOLD", "SP500"]
horizons = [1, 20]
model_list = [
"Simple_MLP",
]
FIXED_PARAMS = {
"Simple_MLP": dict(
lr=1e-3, dropout=0.0, l2_weight=1e-4, batch_size=64, hidden_layers=2, hidden_dim=64,
epochs=50, patience=10, min_epochs=10, min_delta=1e-4, target_mode="log_mse"
),
"Simple_LSTM": dict(
lr=5e-3, dropout=0.0, l2_weight=1e-5, batch_size=16, hidden_layers=4, hidden_dim=128,
epochs=50, patience=5, min_epochs=20, min_delta=1e-4
),
"Simple_KAN": dict(
lr=5e-4, dropout=0.0, l2_weight=1e-5, batch_size=32, hidden_layers=3, hidden_dim=32, knots=8, spline_power=5,
epochs=50, patience=5, min_epochs=20, min_delta=1e-4
),
"LSTM_KAN": dict(
lr=5e-4, dropout=0.0, l2_weight=1e-5, batch_size=16, hidden_layers=2, hidden_dim=128, knots=8, spline_power=5,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
"TimesNet": dict(
lr=5e-3, l2_weight=5e-5, batch_size=512, d_model=128, d_ff=256, n_heads=4, e_layers=4, dropout=0.0,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
"ITransformer": dict(
lr=5e-3, l2_weight=5e-5, batch_size=512, d_model=128, d_ff=256, n_heads=4, e_layers=4, dropout=0.0,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
}
COMMON = dict(
use_nested_cv=False,
single_holdout=True,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
verbose=True,
)
# test_param_grids = {
# "Simple_MLP": {
# "lr": [5e-3],
# "batch_size": [64, 512],
# "hidden_layers": [2, 4],
# "hidden_dim": [64, 512],
# "dropout": [0.0],
# "l2_weight": [1e-5],
# }
# }
BASE_SAVE_DIR = os.path.join(root_folder, objects_relative_path, "runs_timeseries_test")
os.makedirs(BASE_SAVE_DIR, exist_ok=True)
print(X_price_map["EURUSD"].shape)
results_store_test = run_all_models_for_all(
tickers=tickers,
horizons=horizons,
model_list=model_list,
X_price_map=X_price_map,
X_time_map=X_time_map,
y_map=y_map,
base_save_dir=BASE_SAVE_DIR,
fixed_params=FIXED_PARAMS,
common_params=COMMON,
model_io=MODEL_IO,
multitask_models=MULTI_TASK_MODELS,
param_grids=None, #test_param_grids,
grid_tickers=None,
grid_horizons=None,
results_store=None,
overwrite=True
)
all_results_test = run_garch_over_dict(
structured_data_dict=structured_data_dict_8,
tickers=["EURUSD","GOLD"],
horizons=[1,5,20],
results_dict={},
anchor_step=20,
anchor_feature_idx=0,
y_feature_idx=0,
split_ratio=0.8,
roll_window=250,
mean_mode="Zero",
candidates=[("GARCH", 1, 1, "t")],
sim_paths=0,
metric_horizons=(1,5,20,-1),
verbose=True,
save_dir=None
)
ex_ticker, ex_H, ex_model = "GOLD",20, "Simple_MLP"
if ex_ticker in all_results_test and ex_H in all_results_test[ex_ticker] and ex_model in all_results_test[ex_ticker][ex_H]:
bundle = all_results_test[ex_ticker][ex_H][ex_model]
print(f"- {ex_ticker} H={ex_H} {ex_model} metrics keys:",
list(bundle["results"].keys())[:5], "...")
print(f"- saved model path: {bundle.get('save_path')}")
SAVE_PATH = os.path.join(root_folder, objects_relative_path, "all_results_test.pkl.gz")
os.makedirs(os.path.dirname(SAVE_PATH), exist_ok=True)
def strip_models(d):
out = {}
for tkr, byH in d.items():
out[tkr] = {}
for H, byM in byH.items():
out[tkr][H] = {}
for mname, payload in byM.items():
slim = dict(payload)
slim.pop("model", None)
out[tkr][H][mname] = slim
return out
to_save_test = strip_models(all_results_test)
with tempfile.NamedTemporaryFile(dir=os.path.dirname(SAVE_PATH), delete=False) as tmp:
with gzip.GzipFile(fileobj=tmp, mode="wb") as f:
pickle.dump(to_save_test, f, protocol=pickle.HIGHEST_PROTOCOL)
tmp_name = tmp.name
os.replace(tmp_name, SAVE_PATH)
print(f"Saved merged results to: {SAVE_PATH}")
(3782, 60, 1) === EURUSD | H=1 | Simple_MLP (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525025 Min value: -0.7148707238463011 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9963965101732293 Min value: -3.718602223017603 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.408921165171427 Min value: -0.7103978753601863 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.319059579642504 Min value: -2.975928211620611 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144182 Min value: -0.7173043382357188 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3834322546316056 Min value: -5.349969967608034 Epoch 1: Train mse = 0.6608 | Val mse = 0.3995 Epoch 2: Train mse = 0.4878 | Val mse = 0.3860 Epoch 3: Train mse = 0.3254 | Val mse = 0.3415 Epoch 4: Train mse = 0.3088 | Val mse = 0.3321 Epoch 5: Train mse = 0.2757 | Val mse = 0.3277 Epoch 6: Train mse = 0.2905 | Val mse = 0.3525 Epoch 7: Train mse = 0.2569 | Val mse = 0.3329 Epoch 8: Train mse = 0.2488 | Val mse = 0.3487 Epoch 9: Train mse = 0.2260 | Val mse = 0.3284 Epoch 10: Train mse = 0.2067 | Val mse = 0.3476 Epoch 11: Train mse = 0.1894 | Val mse = 0.3323 Epoch 12: Train mse = 0.1771 | Val mse = 0.3377 Epoch 13: Train mse = 0.1674 | Val mse = 0.3442 Epoch 14: Train mse = 0.1617 | Val mse = 0.3686 Epoch 15: Train mse = 0.1514 | Val mse = 0.4189 Early stopping triggered at epoch 15. Parameters used in the single-fit model: input_dim: 60 output_dim: 1 dropout: 0.00000000 lr: 0.00100000 epochs: 50 batch_size: 64 device: mps verbose: True hidden_layers: 2 no_tasks: 1 l2_weight: 0.00010000 patience: 10 min_epochs: 10 min_delta: 0.00010000 hidden_dim: 64 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.08919762 1 day(s) RMSE : 0.17621370 1 day(s) R2 : 0.31969810 1 day(s) Pearson r : 0.58196664 1 day(s) QLIKE : 0.41610350 full horizon MAE : 0.08919762 full horizon RMSE : 0.17621370 full horizon R2 : 0.31969810 full horizon Pearson r : 0.58196664 full horizon QLIKE : 0.41610350 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/runs_timeseries_test/EURUSD/Simple_MLP_H1.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00600063, max=0.943153 === EURUSD | H=20 | Simple_MLP (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525025 Min value: -0.7148707238463011 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.998957169490066 Min value: -3.7200073694899434 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.408921165171427 Min value: -0.7103978753601863 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3212202110774225 Min value: -2.9768947428361368 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144182 Min value: -0.7173043382357188 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737162 Min value: -5.3523385822659755 Epoch 1: Train mse = 0.8544 | Val mse = 0.7737 Epoch 2: Train mse = 0.5614 | Val mse = 0.5585 Epoch 3: Train mse = 0.4420 | Val mse = 0.4830 Epoch 4: Train mse = 0.3990 | Val mse = 0.4512 Epoch 5: Train mse = 0.3803 | Val mse = 0.4250 Epoch 6: Train mse = 0.3656 | Val mse = 0.4103 Epoch 7: Train mse = 0.3517 | Val mse = 0.3997 Epoch 8: Train mse = 0.3406 | Val mse = 0.3939 Epoch 9: Train mse = 0.3318 | Val mse = 0.3903 Epoch 10: Train mse = 0.3240 | Val mse = 0.3884 Epoch 11: Train mse = 0.3172 | Val mse = 0.3873 Epoch 12: Train mse = 0.3113 | Val mse = 0.3868 Epoch 13: Train mse = 0.3060 | Val mse = 0.3868 Epoch 14: Train mse = 0.3012 | Val mse = 0.3873 Epoch 15: Train mse = 0.2970 | Val mse = 0.3882 Epoch 16: Train mse = 0.2931 | Val mse = 0.3893 Epoch 17: Train mse = 0.2896 | Val mse = 0.3908 Epoch 18: Train mse = 0.2863 | Val mse = 0.3923 Epoch 19: Train mse = 0.2837 | Val mse = 0.3946 Epoch 20: Train mse = 0.2805 | Val mse = 0.3961 Epoch 21: Train mse = 0.2807 | Val mse = 0.4004 Epoch 22: Train mse = 0.2789 | Val mse = 0.4010 Early stopping triggered at epoch 22. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 dropout: 0.00000000 lr: 0.00100000 epochs: 50 batch_size: 64 device: mps verbose: True hidden_layers: 2 no_tasks: 1 l2_weight: 0.00010000 patience: 10 min_epochs: 10 min_delta: 0.00010000 hidden_dim: 64 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.08452395 1 day(s) RMSE : 0.17544610 1 day(s) R2 : 0.32561206 1 day(s) Pearson r : 0.60415090 1 day(s) QLIKE : 0.37362880 3 day(s) MAE : 0.08768875 3 day(s) RMSE : 0.17907247 3 day(s) R2 : 0.29202553 3 day(s) Pearson r : 0.56785289 3 day(s) QLIKE : 0.41967778 5 day(s) MAE : 0.08924005 5 day(s) RMSE : 0.18413082 5 day(s) R2 : 0.24295755 5 day(s) Pearson r : 0.52254482 5 day(s) QLIKE : 0.42620857 10 day(s) MAE : 0.09066161 10 day(s) RMSE : 0.18931449 10 day(s) R2 : 0.19209390 10 day(s) Pearson r : 0.47360505 10 day(s) QLIKE : 0.44704286 20 day(s) MAE : 0.09247805 20 day(s) RMSE : 0.19310444 20 day(s) R2 : 0.14950877 20 day(s) Pearson r : 0.43119811 20 day(s) QLIKE : 0.47442451 full horizon MAE : 0.09247805 full horizon RMSE : 0.19310444 full horizon R2 : 0.14950877 full horizon Pearson r : 0.43119811 full horizon QLIKE : 0.47442451 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/runs_timeseries_test/EURUSD/Simple_MLP_H20.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00463019, max=0.80387 === GOLD | H=1 | Simple_MLP (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 5534 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 17.63876776611196 Min value: -0.5790835075307484 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.28323490000094 Min value: -5.250298730157779 Checking X_price_val: Shape: (443, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722398346 Min value: -0.5755730380699846 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.7902797110229713 Min value: -2.119473437475616 Checking X_price_test: Shape: (1107, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698947 Min value: -0.5768892216495461 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5405778347636028 Min value: -2.3606736150182797 Epoch 1: Train mse = 0.7753 | Val mse = 0.3612 Epoch 2: Train mse = 0.4954 | Val mse = 0.3113 Epoch 3: Train mse = 0.3452 | Val mse = 0.2997 Epoch 4: Train mse = 0.3138 | Val mse = 0.2999 Epoch 5: Train mse = 0.2968 | Val mse = 0.3057 Epoch 6: Train mse = 0.2620 | Val mse = 0.3054 Epoch 7: Train mse = 0.2454 | Val mse = 0.3038 Epoch 8: Train mse = 0.2316 | Val mse = 0.3043 Epoch 9: Train mse = 0.2144 | Val mse = 0.3039 Epoch 10: Train mse = 0.1991 | Val mse = 0.3069 Epoch 11: Train mse = 0.1886 | Val mse = 0.3074 Epoch 12: Train mse = 0.1728 | Val mse = 0.3034 Epoch 13: Train mse = 0.1679 | Val mse = 0.3063 Early stopping triggered at epoch 13. Parameters used in the single-fit model: input_dim: 60 output_dim: 1 dropout: 0.00000000 lr: 0.00100000 epochs: 50 batch_size: 64 device: mps verbose: True hidden_layers: 2 no_tasks: 1 l2_weight: 0.00010000 patience: 10 min_epochs: 10 min_delta: 0.00010000 hidden_dim: 64 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.32007091 1 day(s) RMSE : 0.56865279 1 day(s) R2 : 0.24700192 1 day(s) Pearson r : 0.51610008 1 day(s) QLIKE : 0.57212185 full horizon MAE : 0.32007091 full horizon RMSE : 0.56865279 full horizon R2 : 0.24700192 full horizon Pearson r : 0.51610008 full horizon QLIKE : 0.57212185 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/runs_timeseries_test/GOLD/Simple_MLP_H1.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.0063539, max=2.17653 === GOLD | H=20 | Simple_MLP (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 5534 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 17.63876776611196 Min value: -0.5790835075307484 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.2870228653696634 Min value: -5.257736685381005 Checking X_price_val: Shape: (443, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722398346 Min value: -0.5755730380699846 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.793333110638287 Min value: -2.1222460659281195 Checking X_price_test: Shape: (1107, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698947 Min value: -0.5768892216495461 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5432591469134391 Min value: -2.3638056623268993 Epoch 1: Train mse = 0.9324 | Val mse = 0.4719 Epoch 2: Train mse = 0.7355 | Val mse = 0.4365 Epoch 3: Train mse = 0.6123 | Val mse = 0.4017 Epoch 4: Train mse = 0.5212 | Val mse = 0.4023 Epoch 5: Train mse = 0.4588 | Val mse = 0.3947 Epoch 6: Train mse = 0.4225 | Val mse = 0.3857 Epoch 7: Train mse = 0.3955 | Val mse = 0.3774 Epoch 8: Train mse = 0.3748 | Val mse = 0.3702 Epoch 9: Train mse = 0.3596 | Val mse = 0.3621 Epoch 10: Train mse = 0.3481 | Val mse = 0.3546 Epoch 11: Train mse = 0.3377 | Val mse = 0.3478 Epoch 12: Train mse = 0.3279 | Val mse = 0.3429 Epoch 13: Train mse = 0.3195 | Val mse = 0.3398 Epoch 14: Train mse = 0.3130 | Val mse = 0.3379 Epoch 15: Train mse = 0.3097 | Val mse = 0.3374 Epoch 16: Train mse = 0.3118 | Val mse = 0.3382 Epoch 17: Train mse = 0.3121 | Val mse = 0.3339 Epoch 18: Train mse = 0.3010 | Val mse = 0.3260 Epoch 19: Train mse = 0.2863 | Val mse = 0.3208 Epoch 20: Train mse = 0.2768 | Val mse = 0.3168 Epoch 21: Train mse = 0.2737 | Val mse = 0.3142 Epoch 22: Train mse = 0.2794 | Val mse = 0.3142 Epoch 23: Train mse = 0.2816 | Val mse = 0.3143 Epoch 24: Train mse = 0.2785 | Val mse = 0.3178 Epoch 25: Train mse = 0.2766 | Val mse = 0.3228 Epoch 26: Train mse = 0.2768 | Val mse = 0.3258 Epoch 27: Train mse = 0.2798 | Val mse = 0.3276 Epoch 28: Train mse = 0.2740 | Val mse = 0.3172 Epoch 29: Train mse = 0.2615 | Val mse = 0.3152 Epoch 30: Train mse = 0.2590 | Val mse = 0.3141 Epoch 31: Train mse = 0.2529 | Val mse = 0.3084 Epoch 32: Train mse = 0.2418 | Val mse = 0.3083 Epoch 33: Train mse = 0.2375 | Val mse = 0.3087 Epoch 34: Train mse = 0.2378 | Val mse = 0.3106 Epoch 35: Train mse = 0.2464 | Val mse = 0.3147 Epoch 36: Train mse = 0.2494 | Val mse = 0.3108 Epoch 37: Train mse = 0.2409 | Val mse = 0.3149 Epoch 38: Train mse = 0.2433 | Val mse = 0.3209 Epoch 39: Train mse = 0.2540 | Val mse = 0.3230 Epoch 40: Train mse = 0.2555 | Val mse = 0.3234 Epoch 41: Train mse = 0.2498 | Val mse = 0.3214 Epoch 42: Train mse = 0.2428 | Val mse = 0.3199 Early stopping triggered at epoch 42. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 dropout: 0.00000000 lr: 0.00100000 epochs: 50 batch_size: 64 device: mps verbose: True hidden_layers: 2 no_tasks: 1 l2_weight: 0.00010000 patience: 10 min_epochs: 10 min_delta: 0.00010000 hidden_dim: 64 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.29238660 1 day(s) RMSE : 0.55577848 1 day(s) R2 : 0.28071174 1 day(s) Pearson r : 0.57670710 1 day(s) QLIKE : 0.47645527 3 day(s) MAE : 0.30724076 3 day(s) RMSE : 0.58533270 3 day(s) R2 : 0.20284588 3 day(s) Pearson r : 0.52899745 3 day(s) QLIKE : 0.50072996 5 day(s) MAE : 0.31478991 5 day(s) RMSE : 0.60132274 5 day(s) R2 : 0.16109141 5 day(s) Pearson r : 0.49529540 5 day(s) QLIKE : 0.51605551 10 day(s) MAE : 0.32736396 10 day(s) RMSE : 0.62665027 10 day(s) R2 : 0.09909648 10 day(s) Pearson r : 0.44347614 10 day(s) QLIKE : 0.54242719 20 day(s) MAE : 0.34726557 20 day(s) RMSE : 0.65913494 20 day(s) R2 : 0.01798373 20 day(s) Pearson r : 0.37493238 20 day(s) QLIKE : 0.58685642 full horizon MAE : 0.34726557 full horizon RMSE : 0.65913494 full horizon R2 : 0.01798373 full horizon Pearson r : 0.37493238 full horizon QLIKE : 0.58685642 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/runs_timeseries_test/GOLD/Simple_MLP_H20.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.00879674, max=5.31712 === SP500 | H=1 | Simple_MLP (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3466 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790344 Min value: -0.29186160079947243 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.598103080699805 Min value: -18.186962127218408 Checking X_price_val: Shape: (277, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.189039765537116 Min value: -0.288808876061582 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3850009093345803 Min value: -2.4181466917899535 Checking X_price_test: Shape: (694, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.65876861006644 Min value: -0.2909517549064528 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.3795163284544953 Min value: -3.2441546953028633 Epoch 1: Train mse = 0.8891 | Val mse = 0.7812 Epoch 2: Train mse = 0.7106 | Val mse = 0.6560 Epoch 3: Train mse = 0.6653 | Val mse = 0.5375 Epoch 4: Train mse = 0.5831 | Val mse = 0.5014 Epoch 5: Train mse = 0.5618 | Val mse = 0.5017 Epoch 6: Train mse = 0.5297 | Val mse = 0.5077 Epoch 7: Train mse = 0.4951 | Val mse = 0.5260 Epoch 8: Train mse = 0.4742 | Val mse = 0.5107 Epoch 9: Train mse = 0.4468 | Val mse = 0.5205 Epoch 10: Train mse = 0.4446 | Val mse = 0.4900 Epoch 11: Train mse = 0.4120 | Val mse = 0.5517 Epoch 12: Train mse = 0.4325 | Val mse = 0.4902 Epoch 13: Train mse = 0.3870 | Val mse = 0.5490 Epoch 14: Train mse = 0.4261 | Val mse = 0.5298 Epoch 15: Train mse = 0.3708 | Val mse = 0.5616 Epoch 16: Train mse = 0.3896 | Val mse = 0.6111 Epoch 17: Train mse = 0.3445 | Val mse = 0.5591 Epoch 18: Train mse = 0.3347 | Val mse = 0.5615 Epoch 19: Train mse = 0.3224 | Val mse = 0.5864 Epoch 20: Train mse = 0.3223 | Val mse = 0.5622 Early stopping triggered at epoch 20. Parameters used in the single-fit model: input_dim: 60 output_dim: 1 dropout: 0.00000000 lr: 0.00100000 epochs: 50 batch_size: 64 device: mps verbose: True hidden_layers: 2 no_tasks: 1 l2_weight: 0.00010000 patience: 10 min_epochs: 10 min_delta: 0.00010000 hidden_dim: 64 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.55122681 1 day(s) RMSE : 2.30113331 1 day(s) R2 : 0.33961513 1 day(s) Pearson r : 0.60973808 1 day(s) QLIKE : 0.54568460 full horizon MAE : 0.55122681 full horizon RMSE : 2.30113331 full horizon R2 : 0.33961513 full horizon Pearson r : 0.60973808 full horizon QLIKE : 0.54568460 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/runs_timeseries_test/SP500/Simple_MLP_H1.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.00721551, max=12.551 === SP500 | H=20 | Simple_MLP (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3466 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790344 Min value: -0.29186160079947243 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.5963112311058607 Min value: -18.187442810157716 Checking X_price_val: Shape: (277, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.189039765537116 Min value: -0.288808876061582 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3832820721048678 Min value: -2.446834413184202 Checking X_price_test: Shape: (694, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.65876861006644 Min value: -0.2909517549064528 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.377737634830384 Min value: -3.2455347334033506 Epoch 1: Train mse = 0.9955 | Val mse = 0.9399 Epoch 2: Train mse = 0.8933 | Val mse = 0.8310 Epoch 3: Train mse = 0.8626 | Val mse = 0.7989 Epoch 4: Train mse = 0.8334 | Val mse = 0.7690 Epoch 5: Train mse = 0.8152 | Val mse = 0.7538 Epoch 6: Train mse = 0.7933 | Val mse = 0.7265 Epoch 7: Train mse = 0.7745 | Val mse = 0.7115 Epoch 8: Train mse = 0.7622 | Val mse = 0.6946 Epoch 9: Train mse = 0.7523 | Val mse = 0.6820 Epoch 10: Train mse = 0.7447 | Val mse = 0.6646 Epoch 11: Train mse = 0.7356 | Val mse = 0.6545 Epoch 12: Train mse = 0.7188 | Val mse = 0.6498 Epoch 13: Train mse = 0.7116 | Val mse = 0.6617 Epoch 14: Train mse = 0.6998 | Val mse = 0.6395 Epoch 15: Train mse = 0.6941 | Val mse = 0.6405 Epoch 16: Train mse = 0.6888 | Val mse = 0.6261 Epoch 17: Train mse = 0.6771 | Val mse = 0.6283 Epoch 18: Train mse = 0.6704 | Val mse = 0.6283 Epoch 19: Train mse = 0.6643 | Val mse = 0.6302 Epoch 20: Train mse = 0.6549 | Val mse = 0.6262 Epoch 21: Train mse = 0.6513 | Val mse = 0.6340 Epoch 22: Train mse = 0.6407 | Val mse = 0.6303 Epoch 23: Train mse = 0.6361 | Val mse = 0.6316 Epoch 24: Train mse = 0.6252 | Val mse = 0.6351 Epoch 25: Train mse = 0.6220 | Val mse = 0.6315 Epoch 26: Train mse = 0.6080 | Val mse = 0.6322 Early stopping triggered at epoch 26. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 dropout: 0.00000000 lr: 0.00100000 epochs: 50 batch_size: 64 device: mps verbose: True hidden_layers: 2 no_tasks: 1 l2_weight: 0.00010000 patience: 10 min_epochs: 10 min_delta: 0.00010000 hidden_dim: 64 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.55215352 1 day(s) RMSE : 2.48234127 1 day(s) R2 : 0.23151299 1 day(s) Pearson r : 0.53407678 1 day(s) QLIKE : 0.47378945 3 day(s) MAE : 0.57915858 3 day(s) RMSE : 2.55154316 3 day(s) R2 : 0.18810775 3 day(s) Pearson r : 0.47458197 3 day(s) QLIKE : 0.54040077 5 day(s) MAE : 0.60104043 5 day(s) RMSE : 2.63407078 5 day(s) R2 : 0.13478794 5 day(s) Pearson r : 0.39365122 5 day(s) QLIKE : 0.62935035 10 day(s) MAE : 0.62264108 10 day(s) RMSE : 2.73910211 10 day(s) R2 : 0.06453979 10 day(s) Pearson r : 0.27627694 10 day(s) QLIKE : 0.77801216 20 day(s) MAE : 0.64834771 20 day(s) RMSE : 2.79859961 20 day(s) R2 : 0.02357500 20 day(s) Pearson r : 0.19407947 20 day(s) QLIKE : 0.85499450 full horizon MAE : 0.64834771 full horizon RMSE : 2.79859961 full horizon R2 : 0.02357500 full horizon Pearson r : 0.19407947 full horizon QLIKE : 0.85499450 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/runs_timeseries_test/SP500/Simple_MLP_H20.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.0238434, max=7.35137 [GARCH] EURUSD — H=1 (step=20, x_feat=0, y_feat=0) [vol rolling] B=3782, split=0.8 -> n_train=3025, t0=3024, anchors=758, H=1, roll_window=250 anchor 3024/3781: sd=0.301101, h1_pred=0.205496 anchor 3175/3781: sd=0.328803, h1_pred=0.0848216 anchor 3326/3781: sd=0.149937, h1_pred=0.0124112 anchor 3477/3781: sd=0.10242, h1_pred=0.0106819 anchor 3628/3781: sd=0.0917164, h1_pred=0.00642232 anchor 3779/3781: sd=0.197916, h1_pred=0.0239315 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.161562 RMSE : 0.258397 R2 : -0.459119 Pearson r : 0.216443 QLIKE : 0.793928 full horizon MAE : 0.161562 RMSE : 0.258397 R2 : -0.459119 Pearson r : 0.216443 QLIKE : 0.793928 [GARCH] EURUSD — H=5 (step=20, x_feat=0, y_feat=0) [vol rolling] B=3782, split=0.8 -> n_train=3025, t0=3024, anchors=758, H=5, roll_window=250 anchor 3024/3781: sd=0.301101, h1_pred=0.205496 anchor 3175/3781: sd=0.328803, h1_pred=0.0848216 anchor 3326/3781: sd=0.149937, h1_pred=0.0124112 anchor 3477/3781: sd=0.10242, h1_pred=0.0106819 anchor 3628/3781: sd=0.0917164, h1_pred=0.00642232 anchor 3779/3781: sd=0.197916, h1_pred=0.0239315 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.161562 RMSE : 0.258397 R2 : -0.459119 Pearson r : 0.216443 QLIKE : 0.793928 5 day(s) MAE : 0.160722 RMSE : 0.256832 R2 : -0.462196 Pearson r : 0.208236 QLIKE : 0.795203 full horizon MAE : 0.160722 RMSE : 0.256832 R2 : -0.462196 Pearson r : 0.208236 QLIKE : 0.795203 [GARCH] EURUSD — H=20 (step=20, x_feat=0, y_feat=0) [vol rolling] B=3782, split=0.8 -> n_train=3025, t0=3024, anchors=758, H=20, roll_window=250 anchor 3024/3781: sd=0.301101, h1_pred=0.205496 anchor 3175/3781: sd=0.328803, h1_pred=0.0848216 anchor 3326/3781: sd=0.149937, h1_pred=0.0124112 anchor 3477/3781: sd=0.10242, h1_pred=0.0106819 anchor 3628/3781: sd=0.0917164, h1_pred=0.00642232 anchor 3779/3781: sd=0.197916, h1_pred=0.0239315 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.161562 RMSE : 0.258397 R2 : -0.459119 Pearson r : 0.216443 QLIKE : 0.793928 5 day(s) MAE : 0.160722 RMSE : 0.256832 R2 : -0.462196 Pearson r : 0.208236 QLIKE : 0.795203 20 day(s) MAE : 0.159353 RMSE : 0.253765 R2 : -0.464724 Pearson r : 0.219113 QLIKE : 0.797077 full horizon MAE : 0.159353 RMSE : 0.253765 R2 : -0.464724 Pearson r : 0.219113 QLIKE : 0.797077 [GARCH] GOLD — H=1 (step=20, x_feat=0, y_feat=0) [vol rolling] B=5534, split=0.8 -> n_train=4427, t0=4426, anchors=1108, H=1, roll_window=250 anchor 4426/5533: sd=0.927787, h1_pred=0.473358 anchor 4647/5533: sd=1.03081, h1_pred=0.318591 anchor 4868/5533: sd=0.461766, h1_pred=0.175207 anchor 5089/5533: sd=0.431765, h1_pred=0.149619 anchor 5310/5533: sd=0.536677, h1_pred=0.178005 anchor 5531/5533: sd=0.450478, h1_pred=0.203049 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.507631 RMSE : 0.900324 R2 : -0.889048 Pearson r : 0.012688 QLIKE : 0.650927 full horizon MAE : 0.507631 RMSE : 0.900324 R2 : -0.889048 Pearson r : 0.012688 QLIKE : 0.650927 [GARCH] GOLD — H=5 (step=20, x_feat=0, y_feat=0) [vol rolling] B=5534, split=0.8 -> n_train=4427, t0=4426, anchors=1108, H=5, roll_window=250 anchor 4426/5533: sd=0.927787, h1_pred=0.473358 anchor 4647/5533: sd=1.03081, h1_pred=0.318591 anchor 4868/5533: sd=0.461766, h1_pred=0.175207 anchor 5089/5533: sd=0.431765, h1_pred=0.149619 anchor 5310/5533: sd=0.536677, h1_pred=0.178005 anchor 5531/5533: sd=0.450478, h1_pred=0.203049 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.507631 RMSE : 0.900324 R2 : -0.889048 Pearson r : 0.012688 QLIKE : 0.650927 5 day(s) MAE : 0.485309 RMSE : 0.807028 R2 : -0.512028 Pearson r : -0.008773 QLIKE : 0.642400 full horizon MAE : 0.485309 RMSE : 0.807028 R2 : -0.512028 Pearson r : -0.008773 QLIKE : 0.642400 [GARCH] GOLD — H=20 (step=20, x_feat=0, y_feat=0) [vol rolling] B=5534, split=0.8 -> n_train=4427, t0=4426, anchors=1108, H=20, roll_window=250 anchor 4426/5533: sd=0.927787, h1_pred=0.473358 anchor 4647/5533: sd=1.03081, h1_pred=0.318591 anchor 4868/5533: sd=0.461766, h1_pred=0.175207 anchor 5089/5533: sd=0.431765, h1_pred=0.149619 anchor 5310/5533: sd=0.536677, h1_pred=0.178005 anchor 5531/5533: sd=0.450478, h1_pred=0.203049 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.507631 RMSE : 0.900324 R2 : -0.889048 Pearson r : 0.012688 QLIKE : 0.650927 5 day(s) MAE : 0.485309 RMSE : 0.807028 R2 : -0.512028 Pearson r : -0.008773 QLIKE : 0.642400 20 day(s) MAE : 0.486546 RMSE : 0.796783 R2 : -0.435984 Pearson r : -0.038474 QLIKE : 0.640279 full horizon MAE : 0.486546 RMSE : 0.796783 R2 : -0.435984 Pearson r : -0.038474 QLIKE : 0.640279 Saved merged results to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/all_results_test.pkl.gz
Printing a data exaple¶
In [720]:
ex_ticker, ex_H, ex_model = "EURUSD",20, "Simple_MLP"
extras = all_results_test[ex_ticker][ex_H][ex_model]
print(extras["y_data"].shape)
print(np.nanmax(extras["y_data"][:, :, 1]))
(757, 20, 2) 0.8038699626922607
Load saved dictionary¶
In [721]:
import pickle, gzip
SAVE_PATH = os.path.join(root_folder, objects_relative_path, "all_results.pkl.gz")
with gzip.open(SAVE_PATH, "rb") as f:
all_results = pickle.load(f)
print("Loaded. Example tickers:", list(all_results.keys())[:5])
Loaded. Example tickers: ['AAPL', 'MSFT', 'GOLD', 'BTCUSDT', 'SP500']
Routine to print results¶
In [62]:
import numpy as np
import pandas as pd
def build_metric_frames(
results_store,
task_name=None,
outer_horizon="full",
metrics_order=None,
round_decimals=6,
pretty_print=True,
):
frames = {}
default_metrics_order = [
"MAE (log-var)", "RMSE (log-var)", "R2 (log-var)",
"MAE (var)", "RMSE (var)", "R2 (var)", "QLIKE (var)"
]
if metrics_order is None:
metrics_order = default_metrics_order
def _pick_task(results_blob):
if isinstance(results_blob, dict) and all(isinstance(v, dict) for v in results_blob.values()):
if task_name and task_name in results_blob:
return task_name, results_blob[task_name]
if "Task 1" in results_blob:
return "Task 1", results_blob["Task 1"]
k0 = sorted(results_blob.keys())[0]
return k0, results_blob[k0]
return task_name or "Task 1", results_blob
def _extract_full_metrics(metrics_dict):
out = {}
for k, v in metrics_dict.items():
if isinstance(k, str) and k.startswith("full horizon "):
measure = k[len("full horizon "):].strip()
out[measure] = float(v)
return out
for ticker, hdict in results_store.items():
frames[ticker] = {}
model_names = set()
for h in hdict:
model_names.update(hdict[h].keys())
for model in sorted(model_names):
if outer_horizon == "full":
outer_cols = sorted([h for h in hdict.keys() if model in hdict[h]])
else:
if outer_horizon in hdict and model in hdict[outer_horizon]:
outer_cols = [outer_horizon]
else:
continue
all_measures = set()
per_col_measures = {}
for h in outer_cols:
bundle = hdict[h][model]
results_blob = bundle.get("results", {})
_, metrics_for_task = _pick_task(results_blob)
full_only = _extract_full_metrics(metrics_for_task)
per_col_measures[h] = full_only
all_measures.update(full_only.keys())
row_order = [m for m in metrics_order if m in all_measures] + \
[m for m in sorted(all_measures) if m not in metrics_order]
data = {
f"H{h}": [per_col_measures.get(h, {}).get(m, np.nan) for m in row_order]
for h in outer_cols
}
df = pd.DataFrame(data, index=row_order)
frames[ticker][model] = df
if pretty_print:
col_note = (f"all outer horizons {outer_cols}" if outer_horizon == "full"
else f"H={outer_horizon}")
print(f"\n=== {ticker} | {model} | using {col_note} | task={_pick_task(results_blob)[0]} | full-only ===")
with pd.option_context("display.max_rows", None, "display.max_columns", None):
print(df.round(round_decimals).to_string())
return frames
import os
import re
import numpy as np
import pandas as pd
# assumes build_metric_frames(...) is already defined in scope
def _frames_to_long(frames_dict):
"""
Convert nested frames {ticker: {model: DataFrame}} into a single long-form DataFrame:
columns = [ticker, model, metric, horizon, value]
"""
rows = []
for ticker, models in frames_dict.items():
for model, df in models.items():
if not isinstance(df, pd.DataFrame) or df.empty:
continue
for horizon_col in df.columns:
# Expect columns like "H1", "H5", "H20"
m = re.match(r"^H(\d+)$", str(horizon_col))
horizon = int(m.group(1)) if m else horizon_col
for metric_name, val in df[horizon_col].items():
rows.append({
"ticker": ticker,
"model": model,
"metric": metric_name,
"horizon": horizon,
"value": float(val) if pd.notna(val) else np.nan,
})
if rows:
return pd.DataFrame(rows, columns=["ticker", "model", "metric", "horizon", "value"])
return pd.DataFrame(columns=["ticker", "model", "metric", "horizon", "value"])
def export_metrics(
results_store,
out_path,
*,
task_name=None,
outer_horizon="full",
metrics_order=None,
round_decimals=6,
pretty_print=False,
):
frames = build_metric_frames(
results_store,
task_name=task_name,
outer_horizon=outer_horizon,
metrics_order=metrics_order,
round_decimals=round_decimals,
pretty_print=pretty_print,
)
out_path = os.path.abspath(out_path)
root, ext = os.path.splitext(out_path)
def _ensure_dir(d):
os.makedirs(d, exist_ok=True)
return d
if ext.lower() == ".csv":
long_df = _frames_to_long(frames)
long_df.to_csv(out_path, index=False)
return {"mode": "single_csv", "path": out_path, "rows": len(long_df)}
if ext.lower() == ".txt":
lines = []
for ticker, models in frames.items():
for model, df in models.items():
lines.append(f"=== {ticker} | {model} ===")
if isinstance(df, pd.DataFrame) and not df.empty:
lines.append(df.round(round_decimals).to_string())
else:
lines.append("(no data)")
lines.append("") # blank line
with open(out_path, "w", encoding="utf-8") as f:
f.write("\n".join(lines))
return {"mode": "text", "path": out_path, "sections": sum(len(v) for v in frames.values())}
out_dir = _ensure_dir(out_path)
count = 0
for ticker, models in frames.items():
for model, df in models.items():
safe_t = re.sub(r"[^A-Za-z0-9._-]+", "_", str(ticker))
safe_m = re.sub(r"[^A-Za-z0-9._-]+", "_", str(model))
fp = os.path.join(out_dir, f"{safe_t}__{safe_m}.csv")
(df if isinstance(df, pd.DataFrame) else pd.DataFrame()).to_csv(fp)
count += 1
combined_fp = os.path.join(out_dir, "combined_metrics.csv")
long_df = _frames_to_long(frames)
long_df.to_csv(combined_fp, index=False)
return {"mode": "folder", "dir": out_dir, "files_written": count + 1, "combined_path": combined_fp}
Actual print¶
In [723]:
frames = build_metric_frames(results_store_test, outer_horizon="full", pretty_print=True)
=== EURUSD | GARCH | using all outer horizons [1, 20] | task=Task 1 | full-only ===
H1 H20
MAE 0.161310 0.159137
Pearson r 0.209875 0.215759
QLIKE 0.794253 0.796778
R2 -0.461172 -0.466228
RMSE 0.258249 0.253547
=== EURUSD | Simple_MLP | using all outer horizons [1, 20] | task=Task 1 | full-only ===
H1 H20
MAE 0.089198 0.092478
Pearson r 0.581967 0.431198
QLIKE 0.416104 0.474425
R2 0.319698 0.149509
RMSE 0.176214 0.193104
=== GOLD | GARCH | using all outer horizons [1, 20] | task=Task 1 | full-only ===
H1 H20
MAE 0.508063 0.486758
Pearson r 0.012804 -0.038307
QLIKE 0.651075 0.640301
R2 -0.889246 -0.436083
RMSE 0.900730 0.797085
=== GOLD | Simple_MLP | using all outer horizons [1, 20] | task=Task 1 | full-only ===
H1 H20
MAE 0.320071 0.347266
Pearson r 0.516100 0.374932
QLIKE 0.572122 0.586856
R2 0.247002 0.017984
RMSE 0.568653 0.659135
=== SP500 | GARCH | using all outer horizons [1, 20] | task=Task 1 | full-only ===
H1 H20
MAE 0.943624 0.960721
Pearson r -0.028312 -0.034362
QLIKE 1.434360 1.382856
R2 -0.137066 -0.120473
RMSE 3.019507 2.997934
=== SP500 | Simple_MLP | using all outer horizons [1, 20] | task=Task 1 | full-only ===
H1 H20
MAE 0.551227 0.648348
Pearson r 0.609738 0.194079
QLIKE 0.545685 0.854994
R2 0.339615 0.023575
RMSE 2.301133 2.798600
Plot helper¶
In [20]:
import os
import re
import numpy as np
import matplotlib.pyplot as plt
def _parse_horizon_columns(cols):
numeric = {}
has_full = False
for c in cols:
if isinstance(c, (int, np.integer)):
numeric[int(c)] = c
elif isinstance(c, str):
if c.lower() == "full":
has_full = True
else:
m = re.match(r"^[Hh](\d+)$", c)
if m:
numeric[int(m.group(1))] = c
return sorted(numeric.keys()), has_full, numeric
def plot_metric_vs_horizon(
frames,
tickers=None,
models=None,
metrics=None,
include_full=True,
save_dir=None,
show=True,
fig_size=(7, 4),
dpi=150
):
if metrics is None:
mset = set()
for t, by_model in frames.items():
for _, df in by_model.items():
mset |= set(df.index)
metrics = sorted(mset)
tickers = tickers or list(frames.keys())
if save_dir:
os.makedirs(save_dir, exist_ok=True)
saved_paths = []
for t in tickers:
if t not in frames:
continue
model_names = models or list(frames[t].keys())
all_h = set()
has_full_any = False
per_model_maps = {}
for m in model_names:
if m not in frames[t]:
continue
df = frames[t][m]
Hs, has_full, colmap = _parse_horizon_columns(df.columns)
per_model_maps[m] = (Hs, has_full, colmap)
all_h |= set(Hs)
has_full_any |= has_full
if not all_h and not has_full_any:
continue
xs_all = sorted(all_h)
x_full = xs_all[-1] + 1 if xs_all else 1
for metric in metrics:
plt.figure(figsize=fig_size)
plotted_any = False
if xs_all:
for m in model_names:
if m not in frames[t]:
continue
df = frames[t][m]
if metric not in df.index:
continue
Hs, has_full, colmap = per_model_maps[m]
ys = []
mask = []
for h in xs_all:
if h in colmap:
val = df.at[metric, colmap[h]] if colmap[h] in df.columns else np.nan
else:
val = np.nan
ys.append(val)
mask.append(np.isfinite(val))
ys = np.array(ys, dtype=float)
mask = np.array(mask, dtype=bool)
if mask.any():
plt.plot(np.array(xs_all)[mask], ys[mask], marker="o", label=m)
plotted_any = True
if include_full and has_full_any:
for m in model_names:
if m not in frames[t]:
continue
df = frames[t][m]
if metric in df.index and any(str(c).lower() == "full" for c in df.columns):
y_full = df.at[metric, "full"]
if np.isfinite(y_full):
plt.scatter([x_full], [y_full], marker="x", label=f"{m} (full)")
plotted_any = True
plt.xlabel("Horizon")
xticks = xs_all + ([x_full] if include_full and has_full_any else [])
xticklabels = [str(h) for h in xs_all] + (["full"] if include_full and has_full_any else [])
plt.xticks(xticks, xticklabels)
elif include_full and has_full_any:
x_pos = []
y_vals = []
labels = []
for i, m in enumerate(model_names):
if m in frames[t] and metric in frames[t][m].index and "full" in frames[t][m].columns:
y = frames[t][m].at[metric, "full"]
if np.isfinite(y):
x_pos.append(i)
y_vals.append(y)
labels.append(m)
if y_vals:
plt.scatter(x_pos, y_vals, marker="o")
plt.xticks(x_pos, labels, rotation=0)
plt.xlabel("Model")
plotted_any = True
if not plotted_any:
plt.close()
continue
plt.title(f"{t} — {metric}")
plt.ylabel(metric)
plt.grid(True, alpha=0.3)
plt.legend(loc="best")
plt.tight_layout()
if save_dir:
safe_metric = re.sub(r"[^A-Za-z0-9._-]+", "_", metric)
fname = f"{t}__{safe_metric}.png"
path = os.path.join(save_dir, fname)
plt.savefig(path, dpi=dpi)
saved_paths.append(path)
if show:
plt.show()
else:
plt.close()
return saved_paths
Actual plot¶
In [725]:
saved = plot_metric_vs_horizon(
frames,
tickers=None,
models=None,
metrics=None,
include_full=True,
save_dir="plots/metrics",
show=True
)
print("Saved", len(saved), "charts to plots/metrics")
Saved 15 charts to plots/metrics
Plot of y and predicted y helpers¶
In [21]:
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
def _get_hdict(store, ticker, horizon=1):
byH = store.get(ticker, {})
if horizon in byH: return byH[horizon]
k1 = str(horizon)
if k1 in byH: return byH[k1]
k2 = f"H{horizon}"
if k2 in byH: return byH[k2]
return {}
def _find_y_blob(payload):
if isinstance(payload, dict) and "y_data" in payload:
return payload["y_data"]
return None
def _pick_full_step_y(y_blob):
if y_blob is None:
return None, None
a = np.asarray(y_blob)
if a.ndim != 3:
if a.ndim == 2:
if a.shape[1] >= 2: return a[:, 0], a[:, 1]
if a.shape[1] == 1: return None, a[:, 0]
elif a.ndim == 1:
return None, a
return None, None
B, T, F = a.shape
last = a[:, T-1, :]
if F >= 2:
return last[:, 0], last[:, 1]
elif F == 1:
return None, last[:, 0]
return None, None
def plot_h1_full_from_results(
results_store,
tickers=None,
models=None,
save_dir=None,
show=True,
fig_size=(9, 4),
dpi=150,
verbose=True
):
all_tickers = [t for t in results_store if _get_hdict(results_store, t, 1)]
tickers = all_tickers if tickers is None else [t for t in tickers if t in all_tickers]
if save_dir:
os.makedirs(save_dir, exist_ok=True)
saved = []
for t in tickers:
hdict = _get_hdict(results_store, t, 1)
if not hdict:
if verbose: print(f("[skip] {t}: no models at H=1"))
continue
model_names = list(hdict.keys()) if models is None else [m for m in models if m in hdict]
y_true_ref = None
preds = {}
lens = []
for m in model_names:
y_blob = _find_y_blob(hdict[m])
yt, yp = _pick_full_step_y(y_blob)
if yt is not None:
y_true_ref = yt
lens.append(len(yt))
break
missing = []
for m in model_names:
y_blob = _find_y_blob(hdict[m])
yt, yp = _pick_full_step_y(y_blob)
if yp is not None and yp.size > 0:
preds[m] = yp
lens.append(len(yp))
else:
missing.append(m)
if verbose:
shape = None
try: shape = np.asarray(y_blob).shape
except: pass
print(f" [{t} H=1] {m}: blob_shape={shape}, "
f"yt={'ok' if yt is not None else 'none'}, "
f"yp={'ok' if yp is not None else 'none'}")
if verbose:
msg = f"[{t} H=1] actual=" + ("YES" if y_true_ref is not None else "NO")
if missing: msg += " | no_pred=" + ",".join(missing)
print(msg)
if not preds and y_true_ref is None:
if verbose: print(f"[skip] {t}: nothing to plot at H=1")
continue
L = min(lens) if lens else None
if not L or L <= 0:
if verbose: print(f"[skip] {t}: invalid lengths")
continue
series = {}
if y_true_ref is not None and len(y_true_ref) >= L:
series["Actual"] = y_true_ref[-L:]
for m, yp in preds.items():
if len(yp) >= L:
series[f"{m}_pred"] = yp[-L:]
df_head = pd.DataFrame(series)
head_rows = min(50, len(df_head))
print(f"\n[{t} H=1] Aligned series head (first {head_rows} of {len(df_head)} rows):")
with pd.option_context("display.max_rows", 50, "display.width", 120):
print(df_head.head(50).to_string(index=False, float_format=lambda v: f"{v:.6g}"))
x = np.arange(L)
plt.figure(figsize=fig_size)
if "Actual" in series:
plt.plot(x, series["Actual"], label="Actual (full)", linewidth=2)
for m in model_names:
key = f"{m}_pred"
if key in series:
plt.plot(x, series[key], '--', label=f"{m} pred (full)")
plt.title(f"{t} — Horizon=1 (last step from y_data[..., 1])")
plt.xlabel("Test index"); plt.ylabel("Value")
plt.grid(True, alpha=0.3); plt.legend(loc="best"); plt.tight_layout()
if save_dir:
out = os.path.join(save_dir, f"{t}_H1_full_actual_vs_models.png")
plt.savefig(out, dpi=dpi)
saved.append(out)
if show: plt.show()
else: plt.close()
return saved
Actual y plots¶
In [727]:
_ = plot_h1_full_from_results(
results_store_test,
tickers=None,
models=None,
save_dir="plots/h1_full",
show=True,
verbose=True
)
[EURUSD H=1] Simple_MLP: blob_shape=(757, 1, 2), yt=ok, yp=ok
[EURUSD H=1] GARCH: blob_shape=(757, 1, 2), yt=ok, yp=ok
[EURUSD H=1] actual=YES
[EURUSD H=1] Aligned series head (first 50 of 757 rows):
Actual Simple_MLP_pred GARCH_pred
0.739859 0.456605 0.194402
0.743822 0.439917 0.182659
1.02363 0.293959 0.163768
0.0199331 0.0373925 0.147496
0.52303 0.51497 0.130205
0.355217 0.449064 0.115887
0.417086 0.470106 0.155131
0.266624 0.591673 0.138263
0.312901 0.575305 0.131967
0.00443731 0.0209627 0.119333
0.261753 0.306895 0.108023
0.788937 0.360136 0.102587
0.598342 0.553496 0.0934497
0.723261 0.476556 0.0969616
0.341589 0.207176 0.0889363
0.0137579 0.0302896 0.0826681
0.29405 0.230426 0.108359
0.513047 0.461021 0.0988499
0.252839 0.565897 0.100185
0.305402 0.458735 0.094313
0.226156 0.24895 0.089753
0.00963577 0.0291474 0.0848477
0.0784947 0.346752 0.082161
0.199036 0.360084 0.200495
0.171632 0.273153 0.178106
0.201223 0.400656 0.171009
0.353565 0.134514 0.155538
0.0670925 0.0161179 0.215442
0.2403 0.253816 0.199108
0.443555 0.464334 0.174538
0.418858 0.370152 0.154062
0.378402 0.32185 0.15851
0.644091 0.355531 0.139604
0.0152947 0.03466 0.128518
0.272258 0.223135 0.114498
0.23458 0.187277 0.104534
0.249941 0.402456 0.100107
0.705648 0.390593 0.104415
0.293732 0.279373 0.100611
0.00988965 0.0137109 0.0980077
0.164063 0.225401 0.0984769
0.414405 0.268784 0.100162
0.477121 0.314562 0.115875
0.264156 0.335598 0.126115
0.210154 0.376182 0.116968
0.0136265 0.0204071 0.108811
0.265973 0.252806 0.101425
0.234507 0.390409 0.0990592
0.284101 0.3602 0.0973021
0.226239 0.179596 0.100023
[GOLD H=1] Simple_MLP: blob_shape=(1107, 1, 2), yt=ok, yp=ok [GOLD H=1] GARCH: blob_shape=(1107, 1, 2), yt=ok, yp=ok [GOLD H=1] actual=YES [GOLD H=1] Aligned series head (first 50 of 1107 rows): Actual Simple_MLP_pred GARCH_pred 0.983207 0.340429 0.404734 0.0725466 0.475195 0.41204 0.365228 0.153136 0.386377 0.675363 0.445471 0.388799 1.19577 0.713868 0.379988 0.396614 1.24502 0.475619 0.569428 0.501321 0.422518 0.0394832 0.532566 0.327936 0.540289 0.543877 0.339051 0.645375 0.715095 0.345618 0.596069 0.919522 0.319951 0.496734 0.646822 0.430255 0.986594 0.271303 0.39405 0.0170706 0.0102254 0.317561 0.388559 0.629708 0.318934 0.616087 0.655644 0.332173 0.603893 0.763518 0.327931 0.739671 0.583307 0.429513 0.832053 0.566336 0.361713 0.0110286 0.0107584 0.31836 0.506412 0.487149 0.343133 0.392005 0.611626 0.317323 0.80787 0.858652 0.317796 0.535457 0.559314 0.432284 0.837414 0.462319 0.329636 0.0220968 0.0102691 0.341653 0.498523 0.593219 0.378154 0.472977 0.521982 0.333948 1.61389 0.763689 0.327132 0.641823 0.627251 0.43009 0.600193 0.816263 0.335509 0.0226182 0.0113437 0.337952 0.411008 0.631846 0.321095 0.743739 0.569564 0.319928 0.369762 0.762199 0.319878 0.57285 0.516212 0.437624 1.20635 0.31281 0.318503 0.0659951 0.00812005 0.33278 1.33651 0.657572 0.321075 0.970258 0.81574 0.336696 0.553171 0.990921 0.331243 0.193903 0.347003 0.415988 1.26963 0.742318 0.340967 0.222961 0.0108863 0.297523 0.483934 0.723005 0.344531 1.3403 0.842585 0.318788 1.06602 1.08537 0.297852 0.692459 0.207267 0.401388 0.757949 0.685486 0.307926 0.0295473 0.0266834 0.300452
[SP500 H=1] Simple_MLP: blob_shape=(694, 1, 2), yt=ok, yp=ok [SP500 H=1] GARCH: blob_shape=(694, 1, 2), yt=ok, yp=ok [SP500 H=1] actual=YES [SP500 H=1] Aligned series head (first 50 of 694 rows): Actual Simple_MLP_pred GARCH_pred 1.0351 1.02346 2.38582 1.03491 1.90407 2.27062 0.818223 1.22392 2.22203 0.0217497 0.095521 2.64515 0.502587 0.521083 2.22968 2.13502 1.15246 2.12537 0.903485 1.68215 2.31461 0.898743 0.924198 2.09158 0.904151 1.07291 2.48451 0.0405714 0.0543168 2.08508 0.113815 0.41974 2.00842 0.60793 0.676438 1.99926 0.92855 0.553493 2.0014 0.794209 0.416095 2.60646 0.910428 0.233005 2.21111 0.0104435 0.0328714 2.03453 0.610551 0.589967 2.30905 0.532918 0.802979 2.05008 0.832645 0.551246 2.22178 0.902649 0.608186 2.66556 0.540703 0.609466 2.51951 0.0320152 0.0198967 2.27669 0.44739 0.526564 2.08586 1.17379 0.444389 2.13541 0.784725 0.616858 2.18155 1.19858 0.948593 2.68093 3.1834 0.348121 2.14288 1.95774 0.112661 2.12631 5.83359 2.93549 2.15689 1.91438 5.05136 2.04765 2.82295 4.47707 2.26379 1.97836 4.49097 2.64127 1.67848 4.21192 2.20808 0.231827 1.26015 2.17517 1.99306 2.11611 2.04889 0.595525 2.37936 2.02309 2.0958 1.78721 1.96395 1.44715 2.04939 2.59256 1.8779 1.87158 2.23315 0.0836623 0.228839 1.99344 0.723351 1.52607 2.11009 0.463055 1.67432 2.11381 0.331959 1.61614 2.17244 0.355102 0.664165 2.6587 0.359515 0.510746 2.3202 0.0698865 0.17361 1.95308 0.418851 0.598284 2.09169 0.479569 0.728678 1.78404 0.468942 0.659265 1.75529 0.278331 0.823747 1.79928
Extract best parameters¶
In [136]:
from collections import Counter
def extract_best_params_global(results_store_subset, model_list):
best = {}
for model in model_list:
seen = []
for tkr, byH in results_store_subset.items():
for H, byM in byH.items():
if model in byM and "used_params" in byM[model]:
# freeze dict to hashable for counting
tup = tuple(sorted(byM[model]["used_params"].items()))
seen.append(tup)
if seen:
winner, _ = Counter(seen).most_common(1)[0]
best[model] = dict(winner)
return best
def extract_best_params_by_horizon(results_store_subset, model_list):
out = {}
for model in model_list:
perH = {}
# collect by horizon
temp = {}
for tkr, byH in results_store_subset.items():
for H, byM in byH.items():
if model in byM and "used_params" in byM[model]:
tup = tuple(sorted(byM[model]["used_params"].items()))
temp.setdefault(H, []).append(tup)
for H, tuples in temp.items():
if tuples:
winner, _ = Counter(tuples).most_common(1)[0]
perH[H] = dict(winner)
if perH:
out[model] = perH
return out
def merge_fixed(FIXED_PARAMS, tuned_global):
merged = {m: dict(FIXED_PARAMS[m]) for m in FIXED_PARAMS}
for m, p in tuned_global.items():
merged.setdefault(m, {})
merged[m].update(p)
return merged
def collect_final_params(results_store, *, fallback_to_model=True):
params_by_model = {}
for ticker, byH in results_store.items():
for horizon, byM in byH.items():
for model_name, payload in byM.items():
params = payload.get("used_params")
if (not params) and fallback_to_model:
mdl = payload.get("model")
if hasattr(mdl, "get_params"):
try:
params = mdl.get_params()
except Exception:
params = None
if not params:
continue
params_by_model.setdefault(model_name, {}) \
.setdefault(ticker, {})[horizon] = dict(params)
return params_by_model
def print_final_params(params_by_model, *, sort_models=True, sort_tickers=True, sort_horizons=True):
model_keys = sorted(params_by_model) if sort_models else params_by_model.keys()
for model in model_keys:
print(f"\n=== {model} ===")
by_ticker = params_by_model[model]
ticker_keys = sorted(by_ticker) if sort_tickers else by_ticker.keys()
for tkr in ticker_keys:
by_h = by_ticker[tkr]
horizon_keys = sorted(by_h) if sort_horizons else by_h.keys()
for H in horizon_keys:
print(f"{tkr} | H={H}:")
for k, v in sorted(by_h[H].items()):
print(f" {k}: {v}")
def params_to_dataframe(params_by_model):
import pandas as pd
rows = []
for model, by_t in params_by_model.items():
for tkr, by_h in by_t.items():
for H, params in by_h.items():
row = {"model": model, "ticker": tkr, "horizon": H}
row.update(params)
rows.append(row)
return pd.DataFrame(rows)
def filter_tuned_params(tuned: dict) -> dict:
blacklist = {
"no_tasks", "input_dim", "output_dim", "time_horizon",
"merge_price_time", "flatten", "use_nested_cv", "single_holdout",
"device", "verbose"
}
out = {}
for model, params in tuned.items():
out[model] = {k: v for k, v in params.items() if k not in blacklist}
return out
Results on 1 task models¶
In [137]:
load_data_object_8_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_8.pkl")
with open(load_data_object_8_file_path, "rb") as f:
structured_data_dict_8 = pickle.load(f)
print("Data dictionary 8 loaded successfully.")
tickers = ["AAPL", "MSFT", "GE", "BAC", "C", "BTCUSDT", "EURUSD", "GOLD", "SP500"]
X_price_map, X_time_map, y_map = {}, {}, {}
missing = []
for t in tickers:
if t not in structured_data_dict_8:
missing.append(t)
continue
entry = structured_data_dict_8[t]
Xp = entry.get("X_other", None)
Xt = entry.get("X_time", None)
Y = entry.get("y", None)
if Xp is None or Y is None:
print(f"[WARN] {t}: missing {'X_other' if Xp is None else ''}{' and ' if (Xp is None and Y is None) else ''}{'y' if Y is None else ''} → skipping.")
continue
Xp = np.asarray(Xp, dtype=float)
Xt = None if Xt is None else np.asarray(Xt, dtype=float)
Y = np.asarray(Y, dtype=float)
if Xp.ndim != 3 or Y.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_other={Xp.shape}, y={Y.shape} → skipping.")
continue
if Xt is not None and Xt.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_time={Xt.shape} → setting to None.")
Xt = None
X_price_map[t] = Xp
X_time_map[t] = Xt
y_map[t] = Y
if missing:
print(f"[INFO] Missing tickers in data dict (skipped): {missing}")
Data dictionary 8 loaded successfully.
In [ ]:
#models_to_tune = ["Simple_MLP", "Simple_KAN", "Simple_LSTM"]
models_to_tune = ["Simple_KAN"]
tune_tickers = ["EURUSD"]
tune_horizons = [20]
param_grid_tune = {
"Simple_MLP": {
"lr": [5e-4, 5e-3],
"batch_size": [128, 512],
"hidden_layers": [2, 4],
"hidden_dim": [32, 64],
},
"Simple_KAN": {
"lr": [5e-4, 5e-3],
"batch_size": [128, 512],
"knots": [10, 20],
"spline_power": [5, 7],
"hidden_dim": [32, 64],
"hidden_layers": [2, 4],
},
"Simple_LSTM": {
"lr": [5e-4, 5e-3],
"batch_size": [16, 32],
},
"ITransformer": {
"lr": [1e-6, 1e-5, 1e-4, 1e-3],
"batch_size": [64, 256],
"d_model": [64, 128],
"d_ff": [128, 256],
"n_heads": [4, 6],
"e_layers": [2, 4],
},
}
FIXED_PARAMS = {
"Simple_MLP": dict(
lr=5e-4, dropout=0.0, l2_weight=1e-5, batch_size=128, hidden_layers=3, hidden_dim=128,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
"Simple_LSTM": dict(
lr=5e-4, dropout=0.0, l2_weight=1e-5, batch_size=16, hidden_layers=3, hidden_dim=32,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
"Simple_KAN": dict(
lr=5e-4, dropout=0.0, l2_weight=1e-5, batch_size=128, hidden_layers=3, hidden_dim=128, knots=8, spline_power=5,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
"ITransformer": dict(
lr=1e-4, l2_weight=1e-5, batch_size=256, d_model=128, d_ff=256, n_heads=4, e_layers=2, dropout=0.0,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
}
MODEL_IO = {
"Simple_MLP": dict(merge_price_time=False, flatten=True),
"Simple_KAN": dict(merge_price_time=False, flatten=True),
"Simple_LSTM": dict(merge_price_time=False, flatten=False),
"ITransformer": dict(merge_price_time=False, flatten=False),
}
COMMON = dict(
use_nested_cv=False,
single_holdout=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
verbose=True,
target_mode="log_mse"
)
MULTI_TASK_MODELS = set()
final_results_store = {}
TUNE_BASE_SAVE_DIR = os.path.join(root_folder, objects_relative_path, "tune_saved_object")
os.makedirs(TUNE_BASE_SAVE_DIR, exist_ok=True)
tune_store = run_all_models_for_all(
tickers=tune_tickers,
horizons=tune_horizons,
model_list=models_to_tune,
X_price_map=X_price_map,
X_time_map=X_time_map,
y_map=y_map,
base_save_dir=TUNE_BASE_SAVE_DIR,
fixed_params=FIXED_PARAMS,
common_params=COMMON,
model_io=MODEL_IO,
multitask_models=MULTI_TASK_MODELS,
param_grids=param_grid_tune,
results_store=final_results_store,
overwrite=True,
run_grid_with_single_holdout=True
)
tuned_global = extract_best_params_global(tune_store, models_to_tune)
tuned_global_filtered = filter_tuned_params(tuned_global)
FIXED_PARAMS_MERGED = merge_fixed(FIXED_PARAMS, tuned_global_filtered)
final_tickers = ["AAPL", "MSFT", "GE", "BAC", "C", "BTCUSDT", "EURUSD", "GOLD", "SP500"]
#final_models = ["Simple_MLP", "Simple_KAN", "Simple_LSTM", "ITransformer"]
final_models = ["Simple_KAN"]
final_horizons = [1, 5, 10, 20]
FINAL_BASE_SAVE_DIR = os.path.join(root_folder, objects_relative_path, "final_results_saved_object")
os.makedirs(FINAL_BASE_SAVE_DIR, exist_ok=True)
_ = run_all_models_for_all(
tickers=final_tickers,
horizons=final_horizons,
model_list=final_models,
X_price_map=X_price_map,
X_time_map=X_time_map,
y_map=y_map,
base_save_dir=FINAL_BASE_SAVE_DIR,
fixed_params=FIXED_PARAMS_MERGED,
common_params=COMMON,
model_io=MODEL_IO,
multitask_models=MULTI_TASK_MODELS,
param_grids=None,
results_store=final_results_store,
overwrite=True
)
=== EURUSD | H=20 | Simple_KAN (grid + single-holdout) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 1 Single holdout: one train/test split, param search on a single train/val (early-stop) split. Using device: mps Epoch 1: Train mse = 0.9958 | Val mse = 1.1893 Epoch 2: Train mse = 0.9590 | Val mse = 1.0539 Epoch 3: Train mse = 0.9110 | Val mse = 0.9542 Epoch 4: Train mse = 0.8786 | Val mse = 0.9305 Epoch 5: Train mse = 0.8502 | Val mse = 0.9000 Epoch 6: Train mse = 0.8155 | Val mse = 0.8489 Epoch 7: Train mse = 0.7548 | Val mse = 0.7776 Epoch 8: Train mse = 0.6818 | Val mse = 0.7141 Epoch 9: Train mse = 0.6113 | Val mse = 0.6568 Epoch 10: Train mse = 0.5528 | Val mse = 0.6156 Epoch 11: Train mse = 0.5004 | Val mse = 0.5631 Epoch 12: Train mse = 0.4478 | Val mse = 0.5243 Epoch 13: Train mse = 0.4025 | Val mse = 0.4747 Epoch 14: Train mse = 0.3654 | Val mse = 0.4442 Epoch 15: Train mse = 0.3423 | Val mse = 0.4199 Epoch 16: Train mse = 0.3201 | Val mse = 0.4030 Epoch 17: Train mse = 0.3057 | Val mse = 0.3910 Epoch 18: Train mse = 0.2950 | Val mse = 0.3834 Epoch 19: Train mse = 0.2879 | Val mse = 0.3710 Epoch 20: Train mse = 0.2804 | Val mse = 0.3630 Epoch 21: Train mse = 0.2757 | Val mse = 0.3590 Epoch 22: Train mse = 0.2727 | Val mse = 0.3554 Epoch 23: Train mse = 0.2683 | Val mse = 0.3491 Epoch 24: Train mse = 0.2624 | Val mse = 0.3549 Epoch 25: Train mse = 0.2603 | Val mse = 0.3518 Epoch 26: Train mse = 0.2540 | Val mse = 0.3510 Epoch 27: Train mse = 0.2502 | Val mse = 0.3509 Epoch 28: Train mse = 0.2480 | Val mse = 0.3494 Epoch 29: Train mse = 0.2465 | Val mse = 0.3497 Epoch 30: Train mse = 0.2416 | Val mse = 0.3523 Epoch 31: Train mse = 0.2382 | Val mse = 0.3516 Epoch 32: Train mse = 0.2366 | Val mse = 0.3497 Epoch 33: Train mse = 0.2323 | Val mse = 0.3493 Early stopping triggered at epoch 33. Using device: mps Epoch 1: Train mse = 0.9960 | Val mse = 1.1902 Epoch 2: Train mse = 0.9599 | Val mse = 1.0556 Epoch 3: Train mse = 0.9119 | Val mse = 0.9553 Epoch 4: Train mse = 0.8798 | Val mse = 0.9309 Epoch 5: Train mse = 0.8526 | Val mse = 0.9012 Epoch 6: Train mse = 0.8219 | Val mse = 0.8532 Epoch 7: Train mse = 0.7654 | Val mse = 0.7817 Epoch 8: Train mse = 0.6922 | Val mse = 0.7163 Epoch 9: Train mse = 0.6200 | Val mse = 0.6583 Epoch 10: Train mse = 0.5610 | Val mse = 0.6182 Epoch 11: Train mse = 0.5092 | Val mse = 0.5649 Epoch 12: Train mse = 0.4566 | Val mse = 0.5254 Epoch 13: Train mse = 0.4104 | Val mse = 0.4735 Epoch 14: Train mse = 0.3717 | Val mse = 0.4418 Epoch 15: Train mse = 0.3473 | Val mse = 0.4164 Epoch 16: Train mse = 0.3240 | Val mse = 0.3988 Epoch 17: Train mse = 0.3091 | Val mse = 0.3860 Epoch 18: Train mse = 0.2982 | Val mse = 0.3785 Epoch 19: Train mse = 0.2912 | Val mse = 0.3653 Epoch 20: Train mse = 0.2839 | Val mse = 0.3580 Epoch 21: Train mse = 0.2794 | Val mse = 0.3531 Epoch 22: Train mse = 0.2765 | Val mse = 0.3494 Epoch 23: Train mse = 0.2723 | Val mse = 0.3431 Epoch 24: Train mse = 0.2665 | Val mse = 0.3487 Epoch 25: Train mse = 0.2647 | Val mse = 0.3453 Epoch 26: Train mse = 0.2587 | Val mse = 0.3444 Epoch 27: Train mse = 0.2550 | Val mse = 0.3443 Epoch 28: Train mse = 0.2533 | Val mse = 0.3430 Epoch 29: Train mse = 0.2520 | Val mse = 0.3416 Epoch 30: Train mse = 0.2470 | Val mse = 0.3441 Epoch 31: Train mse = 0.2438 | Val mse = 0.3439 Epoch 32: Train mse = 0.2426 | Val mse = 0.3412 Epoch 33: Train mse = 0.2383 | Val mse = 0.3406 Epoch 34: Train mse = 0.2357 | Val mse = 0.3414 Epoch 35: Train mse = 0.2334 | Val mse = 0.3457 Epoch 36: Train mse = 0.2312 | Val mse = 0.3470 Epoch 37: Train mse = 0.2302 | Val mse = 0.3443 Epoch 38: Train mse = 0.2262 | Val mse = 0.3493 Epoch 39: Train mse = 0.2255 | Val mse = 0.3498 Epoch 40: Train mse = 0.2233 | Val mse = 0.3549 Epoch 41: Train mse = 0.2217 | Val mse = 0.3543 Epoch 42: Train mse = 0.2181 | Val mse = 0.3586 Epoch 43: Train mse = 0.2158 | Val mse = 0.3586 Early stopping triggered at epoch 43. Using device: mps Epoch 1: Train mse = 0.8984 | Val mse = 0.8299 Epoch 2: Train mse = 0.6341 | Val mse = 0.5931 Epoch 3: Train mse = 0.3898 | Val mse = 0.3901 Epoch 4: Train mse = 0.3132 | Val mse = 0.3456 Epoch 5: Train mse = 0.2846 | Val mse = 0.3434 Epoch 6: Train mse = 0.2624 | Val mse = 0.3446 Epoch 7: Train mse = 0.2496 | Val mse = 0.3591 Epoch 8: Train mse = 0.2371 | Val mse = 0.3527 Epoch 9: Train mse = 0.2284 | Val mse = 0.3724 Epoch 10: Train mse = 0.2179 | Val mse = 0.3744 Epoch 11: Train mse = 0.2120 | Val mse = 0.3737 Epoch 12: Train mse = 0.2061 | Val mse = 0.3731 Epoch 13: Train mse = 0.2010 | Val mse = 0.3754 Epoch 14: Train mse = 0.1946 | Val mse = 0.3725 Epoch 15: Train mse = 0.1928 | Val mse = 0.3833 Epoch 16: Train mse = 0.1894 | Val mse = 0.3873 Epoch 17: Train mse = 0.1824 | Val mse = 0.3862 Epoch 18: Train mse = 0.1743 | Val mse = 0.3978 Epoch 19: Train mse = 0.1693 | Val mse = 0.3986 Epoch 20: Train mse = 0.1647 | Val mse = 0.4133 Epoch 21: Train mse = 0.1643 | Val mse = 0.4058 Epoch 22: Train mse = 0.1628 | Val mse = 0.4133 Epoch 23: Train mse = 0.1589 | Val mse = 0.4081 Epoch 24: Train mse = 0.1586 | Val mse = 0.4052 Epoch 25: Train mse = 0.1548 | Val mse = 0.4178 Epoch 26: Train mse = 0.1526 | Val mse = 0.4215 Epoch 27: Train mse = 0.1538 | Val mse = 0.4277 Epoch 28: Train mse = 0.1535 | Val mse = 0.4104 Epoch 29: Train mse = 0.1523 | Val mse = 0.4152 Epoch 30: Train mse = 0.1481 | Val mse = 0.4124 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9010 | Val mse = 0.8331 Epoch 2: Train mse = 0.6454 | Val mse = 0.6030 Epoch 3: Train mse = 0.3981 | Val mse = 0.3894 Epoch 4: Train mse = 0.3156 | Val mse = 0.3413 Epoch 5: Train mse = 0.2844 | Val mse = 0.3396 Epoch 6: Train mse = 0.2635 | Val mse = 0.3389 Epoch 7: Train mse = 0.2518 | Val mse = 0.3530 Epoch 8: Train mse = 0.2397 | Val mse = 0.3462 Epoch 9: Train mse = 0.2320 | Val mse = 0.3576 Epoch 10: Train mse = 0.2235 | Val mse = 0.3705 Epoch 11: Train mse = 0.2187 | Val mse = 0.3713 Epoch 12: Train mse = 0.2111 | Val mse = 0.3660 Epoch 13: Train mse = 0.2015 | Val mse = 0.3666 Epoch 14: Train mse = 0.1938 | Val mse = 0.3804 Epoch 15: Train mse = 0.1891 | Val mse = 0.3795 Epoch 16: Train mse = 0.1861 | Val mse = 0.3873 Epoch 17: Train mse = 0.1818 | Val mse = 0.3925 Epoch 18: Train mse = 0.1760 | Val mse = 0.3946 Epoch 19: Train mse = 0.1728 | Val mse = 0.3973 Epoch 20: Train mse = 0.1693 | Val mse = 0.3957 Epoch 21: Train mse = 0.1689 | Val mse = 0.4028 Epoch 22: Train mse = 0.1652 | Val mse = 0.4144 Epoch 23: Train mse = 0.1636 | Val mse = 0.4066 Epoch 24: Train mse = 0.1651 | Val mse = 0.4014 Epoch 25: Train mse = 0.1624 | Val mse = 0.3967 Epoch 26: Train mse = 0.1572 | Val mse = 0.4103 Epoch 27: Train mse = 0.1509 | Val mse = 0.4184 Epoch 28: Train mse = 0.1504 | Val mse = 0.4138 Epoch 29: Train mse = 0.1502 | Val mse = 0.4036 Epoch 30: Train mse = 0.1464 | Val mse = 0.4195 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9931 | Val mse = 1.1859 Epoch 2: Train mse = 0.9542 | Val mse = 1.0484 Epoch 3: Train mse = 0.9099 | Val mse = 0.9627 Epoch 4: Train mse = 0.8770 | Val mse = 0.9211 Epoch 5: Train mse = 0.8316 | Val mse = 0.8733 Epoch 6: Train mse = 0.7821 | Val mse = 0.8249 Epoch 7: Train mse = 0.7292 | Val mse = 0.7807 Epoch 8: Train mse = 0.6763 | Val mse = 0.7408 Epoch 9: Train mse = 0.6151 | Val mse = 0.6847 Epoch 10: Train mse = 0.5534 | Val mse = 0.6306 Epoch 11: Train mse = 0.4958 | Val mse = 0.5810 Epoch 12: Train mse = 0.4363 | Val mse = 0.5417 Epoch 13: Train mse = 0.3890 | Val mse = 0.5000 Epoch 14: Train mse = 0.3511 | Val mse = 0.4698 Epoch 15: Train mse = 0.3251 | Val mse = 0.4469 Epoch 16: Train mse = 0.3064 | Val mse = 0.4233 Epoch 17: Train mse = 0.2956 | Val mse = 0.4130 Epoch 18: Train mse = 0.2807 | Val mse = 0.4053 Epoch 19: Train mse = 0.2737 | Val mse = 0.3990 Epoch 20: Train mse = 0.2609 | Val mse = 0.3964 Epoch 21: Train mse = 0.2572 | Val mse = 0.3911 Epoch 22: Train mse = 0.2506 | Val mse = 0.3908 Epoch 23: Train mse = 0.2412 | Val mse = 0.3873 Epoch 24: Train mse = 0.2331 | Val mse = 0.3856 Epoch 25: Train mse = 0.2266 | Val mse = 0.3862 Epoch 26: Train mse = 0.2204 | Val mse = 0.3865 Epoch 27: Train mse = 0.2148 | Val mse = 0.3874 Epoch 28: Train mse = 0.2104 | Val mse = 0.3877 Epoch 29: Train mse = 0.2079 | Val mse = 0.3872 Epoch 30: Train mse = 0.2027 | Val mse = 0.3906 Epoch 31: Train mse = 0.1974 | Val mse = 0.3889 Epoch 32: Train mse = 0.1950 | Val mse = 0.3950 Epoch 33: Train mse = 0.1892 | Val mse = 0.3958 Epoch 34: Train mse = 0.1869 | Val mse = 0.3958 Early stopping triggered at epoch 34. Using device: mps Epoch 1: Train mse = 0.9934 | Val mse = 1.1869 Epoch 2: Train mse = 0.9547 | Val mse = 1.0483 Epoch 3: Train mse = 0.9100 | Val mse = 0.9627 Epoch 4: Train mse = 0.8774 | Val mse = 0.9207 Epoch 5: Train mse = 0.8320 | Val mse = 0.8719 Epoch 6: Train mse = 0.7814 | Val mse = 0.8215 Epoch 7: Train mse = 0.7292 | Val mse = 0.7779 Epoch 8: Train mse = 0.6791 | Val mse = 0.7406 Epoch 9: Train mse = 0.6194 | Val mse = 0.6830 Epoch 10: Train mse = 0.5578 | Val mse = 0.6271 Epoch 11: Train mse = 0.4994 | Val mse = 0.5761 Epoch 12: Train mse = 0.4385 | Val mse = 0.5343 Epoch 13: Train mse = 0.3899 | Val mse = 0.4901 Epoch 14: Train mse = 0.3511 | Val mse = 0.4595 Epoch 15: Train mse = 0.3251 | Val mse = 0.4372 Epoch 16: Train mse = 0.3068 | Val mse = 0.4130 Epoch 17: Train mse = 0.2967 | Val mse = 0.4046 Epoch 18: Train mse = 0.2828 | Val mse = 0.3949 Epoch 19: Train mse = 0.2762 | Val mse = 0.3888 Epoch 20: Train mse = 0.2636 | Val mse = 0.3855 Epoch 21: Train mse = 0.2607 | Val mse = 0.3796 Epoch 22: Train mse = 0.2547 | Val mse = 0.3797 Epoch 23: Train mse = 0.2455 | Val mse = 0.3758 Epoch 24: Train mse = 0.2379 | Val mse = 0.3762 Epoch 25: Train mse = 0.2322 | Val mse = 0.3752 Epoch 26: Train mse = 0.2265 | Val mse = 0.3746 Epoch 27: Train mse = 0.2209 | Val mse = 0.3751 Epoch 28: Train mse = 0.2167 | Val mse = 0.3746 Epoch 29: Train mse = 0.2144 | Val mse = 0.3774 Epoch 30: Train mse = 0.2094 | Val mse = 0.3800 Epoch 31: Train mse = 0.2041 | Val mse = 0.3766 Epoch 32: Train mse = 0.2017 | Val mse = 0.3842 Epoch 33: Train mse = 0.1959 | Val mse = 0.3857 Epoch 34: Train mse = 0.1937 | Val mse = 0.3872 Epoch 35: Train mse = 0.1909 | Val mse = 0.3878 Epoch 36: Train mse = 0.1877 | Val mse = 0.3900 Early stopping triggered at epoch 36. Using device: mps Epoch 1: Train mse = 0.8868 | Val mse = 0.8391 Epoch 2: Train mse = 0.6267 | Val mse = 0.5518 Epoch 3: Train mse = 0.3814 | Val mse = 0.4114 Epoch 4: Train mse = 0.3181 | Val mse = 0.3646 Epoch 5: Train mse = 0.2840 | Val mse = 0.3562 Epoch 6: Train mse = 0.2603 | Val mse = 0.3670 Epoch 7: Train mse = 0.2429 | Val mse = 0.3506 Epoch 8: Train mse = 0.2292 | Val mse = 0.3480 Epoch 9: Train mse = 0.2210 | Val mse = 0.3593 Epoch 10: Train mse = 0.2130 | Val mse = 0.3521 Epoch 11: Train mse = 0.2053 | Val mse = 0.3639 Epoch 12: Train mse = 0.2015 | Val mse = 0.3662 Epoch 13: Train mse = 0.1969 | Val mse = 0.3581 Epoch 14: Train mse = 0.1938 | Val mse = 0.3647 Epoch 15: Train mse = 0.1875 | Val mse = 0.3648 Epoch 16: Train mse = 0.1804 | Val mse = 0.3731 Epoch 17: Train mse = 0.1780 | Val mse = 0.3826 Epoch 18: Train mse = 0.1739 | Val mse = 0.3787 Epoch 19: Train mse = 0.1711 | Val mse = 0.3823 Epoch 20: Train mse = 0.1688 | Val mse = 0.4020 Epoch 21: Train mse = 0.1740 | Val mse = 0.3811 Epoch 22: Train mse = 0.1715 | Val mse = 0.3820 Epoch 23: Train mse = 0.1697 | Val mse = 0.3975 Epoch 24: Train mse = 0.1716 | Val mse = 0.3882 Epoch 25: Train mse = 0.1702 | Val mse = 0.3864 Epoch 26: Train mse = 0.1689 | Val mse = 0.3860 Epoch 27: Train mse = 0.1625 | Val mse = 0.3906 Epoch 28: Train mse = 0.1584 | Val mse = 0.3880 Epoch 29: Train mse = 0.1583 | Val mse = 0.3899 Epoch 30: Train mse = 0.1530 | Val mse = 0.4033 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.8869 | Val mse = 0.8434 Epoch 2: Train mse = 0.6325 | Val mse = 0.5579 Epoch 3: Train mse = 0.3821 | Val mse = 0.4066 Epoch 4: Train mse = 0.3182 | Val mse = 0.3795 Epoch 5: Train mse = 0.2857 | Val mse = 0.3632 Epoch 6: Train mse = 0.2597 | Val mse = 0.3845 Epoch 7: Train mse = 0.2469 | Val mse = 0.3601 Epoch 8: Train mse = 0.2332 | Val mse = 0.3594 Epoch 9: Train mse = 0.2220 | Val mse = 0.3668 Epoch 10: Train mse = 0.2121 | Val mse = 0.3701 Epoch 11: Train mse = 0.2060 | Val mse = 0.3760 Epoch 12: Train mse = 0.2054 | Val mse = 0.3699 Epoch 13: Train mse = 0.1971 | Val mse = 0.3644 Epoch 14: Train mse = 0.1883 | Val mse = 0.3754 Epoch 15: Train mse = 0.1878 | Val mse = 0.3848 Epoch 16: Train mse = 0.1859 | Val mse = 0.3874 Epoch 17: Train mse = 0.1812 | Val mse = 0.3851 Epoch 18: Train mse = 0.1775 | Val mse = 0.3789 Epoch 19: Train mse = 0.1719 | Val mse = 0.3876 Epoch 20: Train mse = 0.1639 | Val mse = 0.3965 Epoch 21: Train mse = 0.1653 | Val mse = 0.3934 Epoch 22: Train mse = 0.1654 | Val mse = 0.3953 Epoch 23: Train mse = 0.1650 | Val mse = 0.3978 Epoch 24: Train mse = 0.1653 | Val mse = 0.4043 Epoch 25: Train mse = 0.1619 | Val mse = 0.4011 Epoch 26: Train mse = 0.1657 | Val mse = 0.4161 Epoch 27: Train mse = 0.1676 | Val mse = 0.4009 Epoch 28: Train mse = 0.1621 | Val mse = 0.4112 Epoch 29: Train mse = 0.1574 | Val mse = 0.3991 Epoch 30: Train mse = 0.1487 | Val mse = 0.3989 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9946 | Val mse = 1.2229 Epoch 2: Train mse = 0.9788 | Val mse = 1.1110 Epoch 3: Train mse = 0.9143 | Val mse = 0.9544 Epoch 4: Train mse = 0.8862 | Val mse = 0.9475 Epoch 5: Train mse = 0.8676 | Val mse = 0.9267 Epoch 6: Train mse = 0.8559 | Val mse = 0.9193 Epoch 7: Train mse = 0.8518 | Val mse = 0.9156 Epoch 8: Train mse = 0.8439 | Val mse = 0.9110 Epoch 9: Train mse = 0.8359 | Val mse = 0.8992 Epoch 10: Train mse = 0.8079 | Val mse = 0.8624 Epoch 11: Train mse = 0.7591 | Val mse = 0.8061 Epoch 12: Train mse = 0.7116 | Val mse = 0.7933 Epoch 13: Train mse = 0.6747 | Val mse = 0.7842 Epoch 14: Train mse = 0.6373 | Val mse = 0.7630 Epoch 15: Train mse = 0.6001 | Val mse = 0.7420 Epoch 16: Train mse = 0.5624 | Val mse = 0.7097 Epoch 17: Train mse = 0.5199 | Val mse = 0.6787 Epoch 18: Train mse = 0.4804 | Val mse = 0.6446 Epoch 19: Train mse = 0.4422 | Val mse = 0.6181 Epoch 20: Train mse = 0.4130 | Val mse = 0.5934 Epoch 21: Train mse = 0.3852 | Val mse = 0.5718 Epoch 22: Train mse = 0.3643 | Val mse = 0.5633 Epoch 23: Train mse = 0.3454 | Val mse = 0.5471 Epoch 24: Train mse = 0.3354 | Val mse = 0.5427 Epoch 25: Train mse = 0.3197 | Val mse = 0.5316 Epoch 26: Train mse = 0.3122 | Val mse = 0.5213 Epoch 27: Train mse = 0.3035 | Val mse = 0.5167 Epoch 28: Train mse = 0.2951 | Val mse = 0.5050 Epoch 29: Train mse = 0.2912 | Val mse = 0.4906 Epoch 30: Train mse = 0.2851 | Val mse = 0.4939 Epoch 31: Train mse = 0.2741 | Val mse = 0.4768 Epoch 32: Train mse = 0.2680 | Val mse = 0.4793 Epoch 33: Train mse = 0.2621 | Val mse = 0.4697 Epoch 34: Train mse = 0.2578 | Val mse = 0.4758 Epoch 35: Train mse = 0.2569 | Val mse = 0.4711 Epoch 36: Train mse = 0.2512 | Val mse = 0.4614 Epoch 37: Train mse = 0.2491 | Val mse = 0.4678 Epoch 38: Train mse = 0.2432 | Val mse = 0.4623 Epoch 39: Train mse = 0.2407 | Val mse = 0.4604 Epoch 40: Train mse = 0.2380 | Val mse = 0.4610 Epoch 41: Train mse = 0.2371 | Val mse = 0.4679 Epoch 42: Train mse = 0.2334 | Val mse = 0.4603 Epoch 43: Train mse = 0.2288 | Val mse = 0.4591 Epoch 44: Train mse = 0.2280 | Val mse = 0.4613 Epoch 45: Train mse = 0.2249 | Val mse = 0.4663 Epoch 46: Train mse = 0.2209 | Val mse = 0.4673 Epoch 47: Train mse = 0.2190 | Val mse = 0.4649 Epoch 48: Train mse = 0.2186 | Val mse = 0.4729 Epoch 49: Train mse = 0.2163 | Val mse = 0.4662 Epoch 50: Train mse = 0.2132 | Val mse = 0.4706 Using device: mps Epoch 1: Train mse = 0.9947 | Val mse = 1.2233 Epoch 2: Train mse = 0.9801 | Val mse = 1.1163 Epoch 3: Train mse = 0.9155 | Val mse = 0.9531 Epoch 4: Train mse = 0.8864 | Val mse = 0.9480 Epoch 5: Train mse = 0.8687 | Val mse = 0.9279 Epoch 6: Train mse = 0.8571 | Val mse = 0.9210 Epoch 7: Train mse = 0.8533 | Val mse = 0.9168 Epoch 8: Train mse = 0.8466 | Val mse = 0.9130 Epoch 9: Train mse = 0.8420 | Val mse = 0.9039 Epoch 10: Train mse = 0.8230 | Val mse = 0.8798 Epoch 11: Train mse = 0.7885 | Val mse = 0.8321 Epoch 12: Train mse = 0.7334 | Val mse = 0.7933 Epoch 13: Train mse = 0.6938 | Val mse = 0.7843 Epoch 14: Train mse = 0.6560 | Val mse = 0.7632 Epoch 15: Train mse = 0.6171 | Val mse = 0.7402 Epoch 16: Train mse = 0.5773 | Val mse = 0.7009 Epoch 17: Train mse = 0.5327 | Val mse = 0.6658 Epoch 18: Train mse = 0.4938 | Val mse = 0.6304 Epoch 19: Train mse = 0.4558 | Val mse = 0.6010 Epoch 20: Train mse = 0.4292 | Val mse = 0.5791 Epoch 21: Train mse = 0.3999 | Val mse = 0.5590 Epoch 22: Train mse = 0.3739 | Val mse = 0.5467 Epoch 23: Train mse = 0.3507 | Val mse = 0.5260 Epoch 24: Train mse = 0.3388 | Val mse = 0.5156 Epoch 25: Train mse = 0.3229 | Val mse = 0.4958 Epoch 26: Train mse = 0.3135 | Val mse = 0.4811 Epoch 27: Train mse = 0.3040 | Val mse = 0.4746 Epoch 28: Train mse = 0.2952 | Val mse = 0.4566 Epoch 29: Train mse = 0.2911 | Val mse = 0.4477 Epoch 30: Train mse = 0.2843 | Val mse = 0.4434 Epoch 31: Train mse = 0.2752 | Val mse = 0.4360 Epoch 32: Train mse = 0.2694 | Val mse = 0.4226 Epoch 33: Train mse = 0.2637 | Val mse = 0.4225 Epoch 34: Train mse = 0.2599 | Val mse = 0.4200 Epoch 35: Train mse = 0.2587 | Val mse = 0.4103 Epoch 36: Train mse = 0.2536 | Val mse = 0.4091 Epoch 37: Train mse = 0.2515 | Val mse = 0.4065 Epoch 38: Train mse = 0.2461 | Val mse = 0.4025 Epoch 39: Train mse = 0.2444 | Val mse = 0.3979 Epoch 40: Train mse = 0.2413 | Val mse = 0.3981 Epoch 41: Train mse = 0.2404 | Val mse = 0.3958 Epoch 42: Train mse = 0.2362 | Val mse = 0.3963 Epoch 43: Train mse = 0.2332 | Val mse = 0.3953 Epoch 44: Train mse = 0.2321 | Val mse = 0.3991 Epoch 45: Train mse = 0.2291 | Val mse = 0.3943 Epoch 46: Train mse = 0.2263 | Val mse = 0.3992 Epoch 47: Train mse = 0.2238 | Val mse = 0.3946 Epoch 48: Train mse = 0.2231 | Val mse = 0.4007 Epoch 49: Train mse = 0.2221 | Val mse = 0.3988 Epoch 50: Train mse = 0.2192 | Val mse = 0.3980 Using device: mps Epoch 1: Train mse = 0.9218 | Val mse = 0.9658 Epoch 2: Train mse = 0.8047 | Val mse = 0.7846 Epoch 3: Train mse = 0.6772 | Val mse = 0.6857 Epoch 4: Train mse = 0.5140 | Val mse = 0.5154 Epoch 5: Train mse = 0.3839 | Val mse = 0.4387 Epoch 6: Train mse = 0.3369 | Val mse = 0.3856 Epoch 7: Train mse = 0.3033 | Val mse = 0.3750 Epoch 8: Train mse = 0.2886 | Val mse = 0.3416 Epoch 9: Train mse = 0.2712 | Val mse = 0.3525 Epoch 10: Train mse = 0.2657 | Val mse = 0.3346 Epoch 11: Train mse = 0.2556 | Val mse = 0.3333 Epoch 12: Train mse = 0.2455 | Val mse = 0.3334 Epoch 13: Train mse = 0.2396 | Val mse = 0.3411 Epoch 14: Train mse = 0.2451 | Val mse = 0.3644 Epoch 15: Train mse = 0.2392 | Val mse = 0.3408 Epoch 16: Train mse = 0.2260 | Val mse = 0.3421 Epoch 17: Train mse = 0.2163 | Val mse = 0.3433 Epoch 18: Train mse = 0.2094 | Val mse = 0.3461 Epoch 19: Train mse = 0.2047 | Val mse = 0.3628 Epoch 20: Train mse = 0.2015 | Val mse = 0.3581 Epoch 21: Train mse = 0.1969 | Val mse = 0.3618 Epoch 22: Train mse = 0.1923 | Val mse = 0.3625 Epoch 23: Train mse = 0.1900 | Val mse = 0.3674 Epoch 24: Train mse = 0.1914 | Val mse = 0.3679 Epoch 25: Train mse = 0.1910 | Val mse = 0.3539 Epoch 26: Train mse = 0.1935 | Val mse = 0.3591 Epoch 27: Train mse = 0.1910 | Val mse = 0.3535 Epoch 28: Train mse = 0.1966 | Val mse = 0.3834 Epoch 29: Train mse = 0.1985 | Val mse = 0.3738 Epoch 30: Train mse = 0.1946 | Val mse = 0.3909 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9226 | Val mse = 0.9688 Epoch 2: Train mse = 0.8126 | Val mse = 0.7823 Epoch 3: Train mse = 0.6883 | Val mse = 0.6967 Epoch 4: Train mse = 0.5311 | Val mse = 0.5214 Epoch 5: Train mse = 0.3951 | Val mse = 0.4270 Epoch 6: Train mse = 0.3351 | Val mse = 0.3984 Epoch 7: Train mse = 0.3125 | Val mse = 0.3603 Epoch 8: Train mse = 0.2943 | Val mse = 0.3501 Epoch 9: Train mse = 0.2833 | Val mse = 0.3512 Epoch 10: Train mse = 0.2708 | Val mse = 0.3319 Epoch 11: Train mse = 0.2573 | Val mse = 0.3258 Epoch 12: Train mse = 0.2460 | Val mse = 0.3318 Epoch 13: Train mse = 0.2382 | Val mse = 0.3312 Epoch 14: Train mse = 0.2343 | Val mse = 0.3462 Epoch 15: Train mse = 0.2328 | Val mse = 0.3381 Epoch 16: Train mse = 0.2279 | Val mse = 0.3486 Epoch 17: Train mse = 0.2216 | Val mse = 0.3360 Epoch 18: Train mse = 0.2130 | Val mse = 0.3441 Epoch 19: Train mse = 0.2070 | Val mse = 0.3434 Epoch 20: Train mse = 0.2025 | Val mse = 0.3332 Epoch 21: Train mse = 0.1979 | Val mse = 0.3496 Epoch 22: Train mse = 0.1942 | Val mse = 0.3456 Epoch 23: Train mse = 0.1927 | Val mse = 0.3444 Epoch 24: Train mse = 0.1905 | Val mse = 0.3477 Epoch 25: Train mse = 0.1870 | Val mse = 0.3634 Epoch 26: Train mse = 0.1887 | Val mse = 0.3421 Epoch 27: Train mse = 0.1812 | Val mse = 0.3489 Epoch 28: Train mse = 0.1815 | Val mse = 0.3688 Epoch 29: Train mse = 0.1838 | Val mse = 0.3549 Epoch 30: Train mse = 0.1847 | Val mse = 0.3608 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 1.0039 | Val mse = 1.2176 Epoch 2: Train mse = 0.9787 | Val mse = 1.1156 Epoch 3: Train mse = 0.9211 | Val mse = 0.9675 Epoch 4: Train mse = 0.8839 | Val mse = 0.9424 Epoch 5: Train mse = 0.8571 | Val mse = 0.9078 Epoch 6: Train mse = 0.8202 | Val mse = 0.8571 Epoch 7: Train mse = 0.7665 | Val mse = 0.8232 Epoch 8: Train mse = 0.7340 | Val mse = 0.8066 Epoch 9: Train mse = 0.7018 | Val mse = 0.7928 Epoch 10: Train mse = 0.6743 | Val mse = 0.7769 Epoch 11: Train mse = 0.6398 | Val mse = 0.7636 Epoch 12: Train mse = 0.5977 | Val mse = 0.7439 Epoch 13: Train mse = 0.5482 | Val mse = 0.7178 Epoch 14: Train mse = 0.4967 | Val mse = 0.6933 Epoch 15: Train mse = 0.4460 | Val mse = 0.6715 Epoch 16: Train mse = 0.4041 | Val mse = 0.6458 Epoch 17: Train mse = 0.3721 | Val mse = 0.6269 Epoch 18: Train mse = 0.3458 | Val mse = 0.6137 Epoch 19: Train mse = 0.3209 | Val mse = 0.6023 Epoch 20: Train mse = 0.3035 | Val mse = 0.6070 Epoch 21: Train mse = 0.2913 | Val mse = 0.5960 Epoch 22: Train mse = 0.2799 | Val mse = 0.5900 Epoch 23: Train mse = 0.2728 | Val mse = 0.5822 Epoch 24: Train mse = 0.2639 | Val mse = 0.5814 Epoch 25: Train mse = 0.2565 | Val mse = 0.5856 Epoch 26: Train mse = 0.2530 | Val mse = 0.5904 Epoch 27: Train mse = 0.2469 | Val mse = 0.6023 Epoch 28: Train mse = 0.2421 | Val mse = 0.5990 Epoch 29: Train mse = 0.2361 | Val mse = 0.5992 Epoch 30: Train mse = 0.2335 | Val mse = 0.6002 Epoch 31: Train mse = 0.2270 | Val mse = 0.5951 Epoch 32: Train mse = 0.2253 | Val mse = 0.5999 Epoch 33: Train mse = 0.2212 | Val mse = 0.5974 Epoch 34: Train mse = 0.2191 | Val mse = 0.6040 Early stopping triggered at epoch 34. Using device: mps Epoch 1: Train mse = 1.0040 | Val mse = 1.2184 Epoch 2: Train mse = 0.9801 | Val mse = 1.1185 Epoch 3: Train mse = 0.9209 | Val mse = 0.9651 Epoch 4: Train mse = 0.8835 | Val mse = 0.9420 Epoch 5: Train mse = 0.8594 | Val mse = 0.9092 Epoch 6: Train mse = 0.8272 | Val mse = 0.8605 Epoch 7: Train mse = 0.7714 | Val mse = 0.8228 Epoch 8: Train mse = 0.7365 | Val mse = 0.8042 Epoch 9: Train mse = 0.7037 | Val mse = 0.7919 Epoch 10: Train mse = 0.6763 | Val mse = 0.7774 Epoch 11: Train mse = 0.6412 | Val mse = 0.7649 Epoch 12: Train mse = 0.5970 | Val mse = 0.7494 Epoch 13: Train mse = 0.5445 | Val mse = 0.7218 Epoch 14: Train mse = 0.4942 | Val mse = 0.6962 Epoch 15: Train mse = 0.4472 | Val mse = 0.6723 Epoch 16: Train mse = 0.4077 | Val mse = 0.6474 Epoch 17: Train mse = 0.3798 | Val mse = 0.6249 Epoch 18: Train mse = 0.3535 | Val mse = 0.6112 Epoch 19: Train mse = 0.3290 | Val mse = 0.6058 Epoch 20: Train mse = 0.3112 | Val mse = 0.5850 Epoch 21: Train mse = 0.3001 | Val mse = 0.5837 Epoch 22: Train mse = 0.2900 | Val mse = 0.5775 Epoch 23: Train mse = 0.2837 | Val mse = 0.5830 Epoch 24: Train mse = 0.2744 | Val mse = 0.5690 Epoch 25: Train mse = 0.2664 | Val mse = 0.5667 Epoch 26: Train mse = 0.2621 | Val mse = 0.5684 Epoch 27: Train mse = 0.2551 | Val mse = 0.5667 Epoch 28: Train mse = 0.2474 | Val mse = 0.5652 Epoch 29: Train mse = 0.2415 | Val mse = 0.5679 Epoch 30: Train mse = 0.2378 | Val mse = 0.5683 Epoch 31: Train mse = 0.2317 | Val mse = 0.5747 Epoch 32: Train mse = 0.2288 | Val mse = 0.5729 Epoch 33: Train mse = 0.2244 | Val mse = 0.5750 Epoch 34: Train mse = 0.2227 | Val mse = 0.5777 Epoch 35: Train mse = 0.2202 | Val mse = 0.5668 Epoch 36: Train mse = 0.2165 | Val mse = 0.5822 Epoch 37: Train mse = 0.2132 | Val mse = 0.5745 Epoch 38: Train mse = 0.2125 | Val mse = 0.5766 Early stopping triggered at epoch 38. Using device: mps Epoch 1: Train mse = 0.9193 | Val mse = 0.8601 Epoch 2: Train mse = 0.7388 | Val mse = 0.7846 Epoch 3: Train mse = 0.5725 | Val mse = 0.6329 Epoch 4: Train mse = 0.4108 | Val mse = 0.4839 Epoch 5: Train mse = 0.3518 | Val mse = 0.4668 Epoch 6: Train mse = 0.3258 | Val mse = 0.4313 Epoch 7: Train mse = 0.3078 | Val mse = 0.3696 Epoch 8: Train mse = 0.2934 | Val mse = 0.3626 Epoch 9: Train mse = 0.2770 | Val mse = 0.3788 Epoch 10: Train mse = 0.2652 | Val mse = 0.3533 Epoch 11: Train mse = 0.2523 | Val mse = 0.3503 Epoch 12: Train mse = 0.2427 | Val mse = 0.3603 Epoch 13: Train mse = 0.2331 | Val mse = 0.3475 Epoch 14: Train mse = 0.2247 | Val mse = 0.3657 Epoch 15: Train mse = 0.2229 | Val mse = 0.3492 Epoch 16: Train mse = 0.2199 | Val mse = 0.3629 Epoch 17: Train mse = 0.2211 | Val mse = 0.3554 Epoch 18: Train mse = 0.2227 | Val mse = 0.3726 Epoch 19: Train mse = 0.2293 | Val mse = 0.3661 Epoch 20: Train mse = 0.2272 | Val mse = 0.3582 Epoch 21: Train mse = 0.2217 | Val mse = 0.3598 Epoch 22: Train mse = 0.2142 | Val mse = 0.3407 Epoch 23: Train mse = 0.2069 | Val mse = 0.3483 Epoch 24: Train mse = 0.1988 | Val mse = 0.3500 Epoch 25: Train mse = 0.1954 | Val mse = 0.3493 Epoch 26: Train mse = 0.1892 | Val mse = 0.3475 Epoch 27: Train mse = 0.1838 | Val mse = 0.3617 Epoch 28: Train mse = 0.1824 | Val mse = 0.3374 Epoch 29: Train mse = 0.1801 | Val mse = 0.3549 Epoch 30: Train mse = 0.1806 | Val mse = 0.3611 Epoch 31: Train mse = 0.1782 | Val mse = 0.3585 Epoch 32: Train mse = 0.1810 | Val mse = 0.3650 Epoch 33: Train mse = 0.1837 | Val mse = 0.3613 Epoch 34: Train mse = 0.1847 | Val mse = 0.3749 Epoch 35: Train mse = 0.1854 | Val mse = 0.3722 Epoch 36: Train mse = 0.1858 | Val mse = 0.3718 Epoch 37: Train mse = 0.1881 | Val mse = 0.3751 Epoch 38: Train mse = 0.1898 | Val mse = 0.3706 Early stopping triggered at epoch 38. Using device: mps Epoch 1: Train mse = 0.9203 | Val mse = 0.8643 Epoch 2: Train mse = 0.7402 | Val mse = 0.7854 Epoch 3: Train mse = 0.5854 | Val mse = 0.6430 Epoch 4: Train mse = 0.4121 | Val mse = 0.4467 Epoch 5: Train mse = 0.3554 | Val mse = 0.4097 Epoch 6: Train mse = 0.3125 | Val mse = 0.3902 Epoch 7: Train mse = 0.2969 | Val mse = 0.3490 Epoch 8: Train mse = 0.2786 | Val mse = 0.3635 Epoch 9: Train mse = 0.2677 | Val mse = 0.3809 Epoch 10: Train mse = 0.2647 | Val mse = 0.3525 Epoch 11: Train mse = 0.2510 | Val mse = 0.3399 Epoch 12: Train mse = 0.2372 | Val mse = 0.3529 Epoch 13: Train mse = 0.2271 | Val mse = 0.3544 Epoch 14: Train mse = 0.2215 | Val mse = 0.3574 Epoch 15: Train mse = 0.2155 | Val mse = 0.3485 Epoch 16: Train mse = 0.2163 | Val mse = 0.3583 Epoch 17: Train mse = 0.2140 | Val mse = 0.3695 Epoch 18: Train mse = 0.2159 | Val mse = 0.3546 Epoch 19: Train mse = 0.2124 | Val mse = 0.3801 Epoch 20: Train mse = 0.2105 | Val mse = 0.3507 Epoch 21: Train mse = 0.2073 | Val mse = 0.3697 Epoch 22: Train mse = 0.2050 | Val mse = 0.3729 Epoch 23: Train mse = 0.2042 | Val mse = 0.3766 Epoch 24: Train mse = 0.2019 | Val mse = 0.3644 Epoch 25: Train mse = 0.1950 | Val mse = 0.3534 Epoch 26: Train mse = 0.1932 | Val mse = 0.3550 Epoch 27: Train mse = 0.1871 | Val mse = 0.3592 Epoch 28: Train mse = 0.1833 | Val mse = 0.3862 Epoch 29: Train mse = 0.1822 | Val mse = 0.3758 Epoch 30: Train mse = 0.1798 | Val mse = 0.3617 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9891 | Val mse = 1.1586 Epoch 2: Train mse = 0.9297 | Val mse = 0.9621 Epoch 3: Train mse = 0.8721 | Val mse = 0.8875 Epoch 4: Train mse = 0.7890 | Val mse = 0.7991 Epoch 5: Train mse = 0.7035 | Val mse = 0.7187 Epoch 6: Train mse = 0.6185 | Val mse = 0.6440 Epoch 7: Train mse = 0.5365 | Val mse = 0.5738 Epoch 8: Train mse = 0.4596 | Val mse = 0.5065 Epoch 9: Train mse = 0.4008 | Val mse = 0.4597 Epoch 10: Train mse = 0.3600 | Val mse = 0.4206 Epoch 11: Train mse = 0.3325 | Val mse = 0.4036 Epoch 12: Train mse = 0.3182 | Val mse = 0.3847 Epoch 13: Train mse = 0.3087 | Val mse = 0.3750 Epoch 14: Train mse = 0.2968 | Val mse = 0.3693 Epoch 15: Train mse = 0.2904 | Val mse = 0.3651 Epoch 16: Train mse = 0.2834 | Val mse = 0.3551 Epoch 17: Train mse = 0.2744 | Val mse = 0.3523 Epoch 18: Train mse = 0.2687 | Val mse = 0.3450 Epoch 19: Train mse = 0.2606 | Val mse = 0.3464 Epoch 20: Train mse = 0.2562 | Val mse = 0.3458 Epoch 21: Train mse = 0.2523 | Val mse = 0.3497 Epoch 22: Train mse = 0.2471 | Val mse = 0.3455 Epoch 23: Train mse = 0.2425 | Val mse = 0.3503 Epoch 24: Train mse = 0.2392 | Val mse = 0.3429 Epoch 25: Train mse = 0.2331 | Val mse = 0.3440 Epoch 26: Train mse = 0.2296 | Val mse = 0.3515 Epoch 27: Train mse = 0.2262 | Val mse = 0.3469 Epoch 28: Train mse = 0.2215 | Val mse = 0.3524 Epoch 29: Train mse = 0.2191 | Val mse = 0.3509 Epoch 30: Train mse = 0.2145 | Val mse = 0.3565 Epoch 31: Train mse = 0.2128 | Val mse = 0.3573 Epoch 32: Train mse = 0.2072 | Val mse = 0.3644 Epoch 33: Train mse = 0.2063 | Val mse = 0.3638 Epoch 34: Train mse = 0.2033 | Val mse = 0.3682 Early stopping triggered at epoch 34. Using device: mps Epoch 1: Train mse = 0.9895 | Val mse = 1.1604 Epoch 2: Train mse = 0.9310 | Val mse = 0.9653 Epoch 3: Train mse = 0.8752 | Val mse = 0.8917 Epoch 4: Train mse = 0.7960 | Val mse = 0.8020 Epoch 5: Train mse = 0.7110 | Val mse = 0.7217 Epoch 6: Train mse = 0.6279 | Val mse = 0.6489 Epoch 7: Train mse = 0.5475 | Val mse = 0.5795 Epoch 8: Train mse = 0.4715 | Val mse = 0.5115 Epoch 9: Train mse = 0.4111 | Val mse = 0.4625 Epoch 10: Train mse = 0.3679 | Val mse = 0.4207 Epoch 11: Train mse = 0.3380 | Val mse = 0.4023 Epoch 12: Train mse = 0.3224 | Val mse = 0.3824 Epoch 13: Train mse = 0.3131 | Val mse = 0.3728 Epoch 14: Train mse = 0.3011 | Val mse = 0.3666 Epoch 15: Train mse = 0.2947 | Val mse = 0.3619 Epoch 16: Train mse = 0.2882 | Val mse = 0.3517 Epoch 17: Train mse = 0.2793 | Val mse = 0.3494 Epoch 18: Train mse = 0.2739 | Val mse = 0.3413 Epoch 19: Train mse = 0.2661 | Val mse = 0.3423 Epoch 20: Train mse = 0.2621 | Val mse = 0.3419 Epoch 21: Train mse = 0.2585 | Val mse = 0.3457 Epoch 22: Train mse = 0.2535 | Val mse = 0.3419 Epoch 23: Train mse = 0.2492 | Val mse = 0.3467 Epoch 24: Train mse = 0.2463 | Val mse = 0.3388 Epoch 25: Train mse = 0.2405 | Val mse = 0.3389 Epoch 26: Train mse = 0.2374 | Val mse = 0.3475 Epoch 27: Train mse = 0.2346 | Val mse = 0.3424 Epoch 28: Train mse = 0.2302 | Val mse = 0.3480 Epoch 29: Train mse = 0.2278 | Val mse = 0.3448 Epoch 30: Train mse = 0.2234 | Val mse = 0.3489 Epoch 31: Train mse = 0.2220 | Val mse = 0.3504 Epoch 32: Train mse = 0.2167 | Val mse = 0.3590 Epoch 33: Train mse = 0.2162 | Val mse = 0.3569 Epoch 34: Train mse = 0.2127 | Val mse = 0.3593 Early stopping triggered at epoch 34. Using device: mps Epoch 1: Train mse = 0.8164 | Val mse = 0.6790 Epoch 2: Train mse = 0.4640 | Val mse = 0.4361 Epoch 3: Train mse = 0.3329 | Val mse = 0.3608 Epoch 4: Train mse = 0.2912 | Val mse = 0.3350 Epoch 5: Train mse = 0.2691 | Val mse = 0.3300 Epoch 6: Train mse = 0.2497 | Val mse = 0.3403 Epoch 7: Train mse = 0.2379 | Val mse = 0.3550 Epoch 8: Train mse = 0.2225 | Val mse = 0.3667 Epoch 9: Train mse = 0.2147 | Val mse = 0.3620 Epoch 10: Train mse = 0.2076 | Val mse = 0.3788 Epoch 11: Train mse = 0.1950 | Val mse = 0.3731 Epoch 12: Train mse = 0.1892 | Val mse = 0.3769 Epoch 13: Train mse = 0.1818 | Val mse = 0.3749 Epoch 14: Train mse = 0.1792 | Val mse = 0.3705 Epoch 15: Train mse = 0.1746 | Val mse = 0.3811 Epoch 16: Train mse = 0.1659 | Val mse = 0.3784 Epoch 17: Train mse = 0.1583 | Val mse = 0.3941 Epoch 18: Train mse = 0.1526 | Val mse = 0.3998 Epoch 19: Train mse = 0.1513 | Val mse = 0.4020 Epoch 20: Train mse = 0.1469 | Val mse = 0.4061 Epoch 21: Train mse = 0.1470 | Val mse = 0.3970 Epoch 22: Train mse = 0.1450 | Val mse = 0.3957 Epoch 23: Train mse = 0.1470 | Val mse = 0.4242 Epoch 24: Train mse = 0.1441 | Val mse = 0.4140 Epoch 25: Train mse = 0.1372 | Val mse = 0.4029 Epoch 26: Train mse = 0.1308 | Val mse = 0.4139 Epoch 27: Train mse = 0.1233 | Val mse = 0.4265 Epoch 28: Train mse = 0.1167 | Val mse = 0.4139 Epoch 29: Train mse = 0.1127 | Val mse = 0.4228 Epoch 30: Train mse = 0.1089 | Val mse = 0.4339 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.8213 | Val mse = 0.6868 Epoch 2: Train mse = 0.4743 | Val mse = 0.4358 Epoch 3: Train mse = 0.3356 | Val mse = 0.3552 Epoch 4: Train mse = 0.2933 | Val mse = 0.3320 Epoch 5: Train mse = 0.2702 | Val mse = 0.3263 Epoch 6: Train mse = 0.2524 | Val mse = 0.3317 Epoch 7: Train mse = 0.2412 | Val mse = 0.3527 Epoch 8: Train mse = 0.2272 | Val mse = 0.3645 Epoch 9: Train mse = 0.2201 | Val mse = 0.3624 Epoch 10: Train mse = 0.2111 | Val mse = 0.3679 Epoch 11: Train mse = 0.2004 | Val mse = 0.3737 Epoch 12: Train mse = 0.1926 | Val mse = 0.3669 Epoch 13: Train mse = 0.1855 | Val mse = 0.3730 Epoch 14: Train mse = 0.1835 | Val mse = 0.3762 Epoch 15: Train mse = 0.1773 | Val mse = 0.3705 Epoch 16: Train mse = 0.1701 | Val mse = 0.3800 Epoch 17: Train mse = 0.1688 | Val mse = 0.3902 Epoch 18: Train mse = 0.1640 | Val mse = 0.3918 Epoch 19: Train mse = 0.1560 | Val mse = 0.3942 Epoch 20: Train mse = 0.1504 | Val mse = 0.3987 Epoch 21: Train mse = 0.1447 | Val mse = 0.4046 Epoch 22: Train mse = 0.1431 | Val mse = 0.4060 Epoch 23: Train mse = 0.1390 | Val mse = 0.4067 Epoch 24: Train mse = 0.1349 | Val mse = 0.4090 Epoch 25: Train mse = 0.1280 | Val mse = 0.4147 Epoch 26: Train mse = 0.1269 | Val mse = 0.4081 Epoch 27: Train mse = 0.1241 | Val mse = 0.4127 Epoch 28: Train mse = 0.1195 | Val mse = 0.4240 Epoch 29: Train mse = 0.1193 | Val mse = 0.4132 Epoch 30: Train mse = 0.1152 | Val mse = 0.4216 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9851 | Val mse = 1.1486 Epoch 2: Train mse = 0.9190 | Val mse = 0.9320 Epoch 3: Train mse = 0.8589 | Val mse = 0.8801 Epoch 4: Train mse = 0.7795 | Val mse = 0.7869 Epoch 5: Train mse = 0.6830 | Val mse = 0.7042 Epoch 6: Train mse = 0.5855 | Val mse = 0.6328 Epoch 7: Train mse = 0.4988 | Val mse = 0.5689 Epoch 8: Train mse = 0.4225 | Val mse = 0.5105 Epoch 9: Train mse = 0.3640 | Val mse = 0.4568 Epoch 10: Train mse = 0.3266 | Val mse = 0.4309 Epoch 11: Train mse = 0.3093 | Val mse = 0.4144 Epoch 12: Train mse = 0.2910 | Val mse = 0.3993 Epoch 13: Train mse = 0.2771 | Val mse = 0.3872 Epoch 14: Train mse = 0.2632 | Val mse = 0.3818 Epoch 15: Train mse = 0.2545 | Val mse = 0.3796 Epoch 16: Train mse = 0.2427 | Val mse = 0.3710 Epoch 17: Train mse = 0.2325 | Val mse = 0.3751 Epoch 18: Train mse = 0.2259 | Val mse = 0.3733 Epoch 19: Train mse = 0.2156 | Val mse = 0.3713 Epoch 20: Train mse = 0.2074 | Val mse = 0.3745 Epoch 21: Train mse = 0.2017 | Val mse = 0.3745 Epoch 22: Train mse = 0.1955 | Val mse = 0.3778 Epoch 23: Train mse = 0.1899 | Val mse = 0.3793 Epoch 24: Train mse = 0.1855 | Val mse = 0.3771 Epoch 25: Train mse = 0.1826 | Val mse = 0.3810 Epoch 26: Train mse = 0.1791 | Val mse = 0.3798 Epoch 27: Train mse = 0.1740 | Val mse = 0.3831 Epoch 28: Train mse = 0.1714 | Val mse = 0.3848 Epoch 29: Train mse = 0.1674 | Val mse = 0.3868 Epoch 30: Train mse = 0.1644 | Val mse = 0.3910 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9856 | Val mse = 1.1504 Epoch 2: Train mse = 0.9197 | Val mse = 0.9327 Epoch 3: Train mse = 0.8602 | Val mse = 0.8806 Epoch 4: Train mse = 0.7811 | Val mse = 0.7865 Epoch 5: Train mse = 0.6858 | Val mse = 0.7037 Epoch 6: Train mse = 0.5876 | Val mse = 0.6309 Epoch 7: Train mse = 0.5022 | Val mse = 0.5668 Epoch 8: Train mse = 0.4261 | Val mse = 0.5060 Epoch 9: Train mse = 0.3671 | Val mse = 0.4486 Epoch 10: Train mse = 0.3296 | Val mse = 0.4239 Epoch 11: Train mse = 0.3131 | Val mse = 0.4084 Epoch 12: Train mse = 0.2956 | Val mse = 0.3938 Epoch 13: Train mse = 0.2827 | Val mse = 0.3813 Epoch 14: Train mse = 0.2694 | Val mse = 0.3763 Epoch 15: Train mse = 0.2616 | Val mse = 0.3729 Epoch 16: Train mse = 0.2506 | Val mse = 0.3641 Epoch 17: Train mse = 0.2407 | Val mse = 0.3660 Epoch 18: Train mse = 0.2346 | Val mse = 0.3636 Epoch 19: Train mse = 0.2245 | Val mse = 0.3623 Epoch 20: Train mse = 0.2161 | Val mse = 0.3660 Epoch 21: Train mse = 0.2104 | Val mse = 0.3643 Epoch 22: Train mse = 0.2041 | Val mse = 0.3709 Epoch 23: Train mse = 0.1986 | Val mse = 0.3714 Epoch 24: Train mse = 0.1936 | Val mse = 0.3708 Epoch 25: Train mse = 0.1906 | Val mse = 0.3727 Epoch 26: Train mse = 0.1870 | Val mse = 0.3752 Epoch 27: Train mse = 0.1815 | Val mse = 0.3792 Epoch 28: Train mse = 0.1788 | Val mse = 0.3820 Epoch 29: Train mse = 0.1747 | Val mse = 0.3843 Epoch 30: Train mse = 0.1715 | Val mse = 0.3878 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.8049 | Val mse = 0.6466 Epoch 2: Train mse = 0.4288 | Val mse = 0.4093 Epoch 3: Train mse = 0.3206 | Val mse = 0.3712 Epoch 4: Train mse = 0.2815 | Val mse = 0.3548 Epoch 5: Train mse = 0.2494 | Val mse = 0.3454 Epoch 6: Train mse = 0.2320 | Val mse = 0.3591 Epoch 7: Train mse = 0.2162 | Val mse = 0.3581 Epoch 8: Train mse = 0.2051 | Val mse = 0.3524 Epoch 9: Train mse = 0.1948 | Val mse = 0.3624 Epoch 10: Train mse = 0.1863 | Val mse = 0.3598 Epoch 11: Train mse = 0.1809 | Val mse = 0.3589 Epoch 12: Train mse = 0.1737 | Val mse = 0.3660 Epoch 13: Train mse = 0.1722 | Val mse = 0.3753 Epoch 14: Train mse = 0.1707 | Val mse = 0.3758 Epoch 15: Train mse = 0.1685 | Val mse = 0.3726 Epoch 16: Train mse = 0.1658 | Val mse = 0.3753 Epoch 17: Train mse = 0.1603 | Val mse = 0.3816 Epoch 18: Train mse = 0.1572 | Val mse = 0.3861 Epoch 19: Train mse = 0.1552 | Val mse = 0.3881 Epoch 20: Train mse = 0.1512 | Val mse = 0.3838 Epoch 21: Train mse = 0.1505 | Val mse = 0.3932 Epoch 22: Train mse = 0.1464 | Val mse = 0.3970 Epoch 23: Train mse = 0.1434 | Val mse = 0.3945 Epoch 24: Train mse = 0.1378 | Val mse = 0.3973 Epoch 25: Train mse = 0.1354 | Val mse = 0.4028 Epoch 26: Train mse = 0.1316 | Val mse = 0.3924 Epoch 27: Train mse = 0.1254 | Val mse = 0.4011 Epoch 28: Train mse = 0.1216 | Val mse = 0.4163 Epoch 29: Train mse = 0.1174 | Val mse = 0.4034 Epoch 30: Train mse = 0.1123 | Val mse = 0.4214 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.8063 | Val mse = 0.6536 Epoch 2: Train mse = 0.4317 | Val mse = 0.4050 Epoch 3: Train mse = 0.3199 | Val mse = 0.3733 Epoch 4: Train mse = 0.2813 | Val mse = 0.3474 Epoch 5: Train mse = 0.2508 | Val mse = 0.3456 Epoch 6: Train mse = 0.2341 | Val mse = 0.3536 Epoch 7: Train mse = 0.2217 | Val mse = 0.3558 Epoch 8: Train mse = 0.2067 | Val mse = 0.3585 Epoch 9: Train mse = 0.1944 | Val mse = 0.3626 Epoch 10: Train mse = 0.1820 | Val mse = 0.3549 Epoch 11: Train mse = 0.1736 | Val mse = 0.3603 Epoch 12: Train mse = 0.1655 | Val mse = 0.3615 Epoch 13: Train mse = 0.1624 | Val mse = 0.3725 Epoch 14: Train mse = 0.1601 | Val mse = 0.3775 Epoch 15: Train mse = 0.1584 | Val mse = 0.3708 Epoch 16: Train mse = 0.1565 | Val mse = 0.3677 Epoch 17: Train mse = 0.1583 | Val mse = 0.3830 Epoch 18: Train mse = 0.1555 | Val mse = 0.3733 Epoch 19: Train mse = 0.1527 | Val mse = 0.3838 Epoch 20: Train mse = 0.1481 | Val mse = 0.3866 Epoch 21: Train mse = 0.1490 | Val mse = 0.3836 Epoch 22: Train mse = 0.1409 | Val mse = 0.3980 Epoch 23: Train mse = 0.1364 | Val mse = 0.4032 Epoch 24: Train mse = 0.1347 | Val mse = 0.4027 Epoch 25: Train mse = 0.1297 | Val mse = 0.3957 Epoch 26: Train mse = 0.1248 | Val mse = 0.4041 Epoch 27: Train mse = 0.1204 | Val mse = 0.4165 Epoch 28: Train mse = 0.1196 | Val mse = 0.4088 Epoch 29: Train mse = 0.1143 | Val mse = 0.4061 Epoch 30: Train mse = 0.1131 | Val mse = 0.3958 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9920 | Val mse = 1.1842 Epoch 2: Train mse = 0.9306 | Val mse = 0.9563 Epoch 3: Train mse = 0.8821 | Val mse = 0.9477 Epoch 4: Train mse = 0.8628 | Val mse = 0.9404 Epoch 5: Train mse = 0.8418 | Val mse = 0.9080 Epoch 6: Train mse = 0.7873 | Val mse = 0.8209 Epoch 7: Train mse = 0.7269 | Val mse = 0.7880 Epoch 8: Train mse = 0.6898 | Val mse = 0.7523 Epoch 9: Train mse = 0.6205 | Val mse = 0.6541 Epoch 10: Train mse = 0.5238 | Val mse = 0.5822 Epoch 11: Train mse = 0.4446 | Val mse = 0.5165 Epoch 12: Train mse = 0.3724 | Val mse = 0.4482 Epoch 13: Train mse = 0.3318 | Val mse = 0.4155 Epoch 14: Train mse = 0.3064 | Val mse = 0.3948 Epoch 15: Train mse = 0.2932 | Val mse = 0.3729 Epoch 16: Train mse = 0.2834 | Val mse = 0.3691 Epoch 17: Train mse = 0.2752 | Val mse = 0.3540 Epoch 18: Train mse = 0.2662 | Val mse = 0.3702 Epoch 19: Train mse = 0.2612 | Val mse = 0.3529 Epoch 20: Train mse = 0.2544 | Val mse = 0.3466 Epoch 21: Train mse = 0.2487 | Val mse = 0.3411 Epoch 22: Train mse = 0.2453 | Val mse = 0.3469 Epoch 23: Train mse = 0.2419 | Val mse = 0.3421 Epoch 24: Train mse = 0.2374 | Val mse = 0.3493 Epoch 25: Train mse = 0.2336 | Val mse = 0.3434 Epoch 26: Train mse = 0.2301 | Val mse = 0.3470 Epoch 27: Train mse = 0.2271 | Val mse = 0.3516 Epoch 28: Train mse = 0.2250 | Val mse = 0.3597 Epoch 29: Train mse = 0.2214 | Val mse = 0.3586 Epoch 30: Train mse = 0.2197 | Val mse = 0.3602 Epoch 31: Train mse = 0.2163 | Val mse = 0.3541 Early stopping triggered at epoch 31. Using device: mps Epoch 1: Train mse = 0.9923 | Val mse = 1.1869 Epoch 2: Train mse = 0.9321 | Val mse = 0.9561 Epoch 3: Train mse = 0.8830 | Val mse = 0.9479 Epoch 4: Train mse = 0.8648 | Val mse = 0.9410 Epoch 5: Train mse = 0.8454 | Val mse = 0.9131 Epoch 6: Train mse = 0.7999 | Val mse = 0.8317 Epoch 7: Train mse = 0.7340 | Val mse = 0.7867 Epoch 8: Train mse = 0.6987 | Val mse = 0.7598 Epoch 9: Train mse = 0.6386 | Val mse = 0.6686 Epoch 10: Train mse = 0.5426 | Val mse = 0.5879 Epoch 11: Train mse = 0.4625 | Val mse = 0.5245 Epoch 12: Train mse = 0.3886 | Val mse = 0.4541 Epoch 13: Train mse = 0.3429 | Val mse = 0.4163 Epoch 14: Train mse = 0.3145 | Val mse = 0.3934 Epoch 15: Train mse = 0.2992 | Val mse = 0.3733 Epoch 16: Train mse = 0.2895 | Val mse = 0.3693 Epoch 17: Train mse = 0.2813 | Val mse = 0.3539 Epoch 18: Train mse = 0.2727 | Val mse = 0.3683 Epoch 19: Train mse = 0.2673 | Val mse = 0.3485 Epoch 20: Train mse = 0.2600 | Val mse = 0.3449 Epoch 21: Train mse = 0.2550 | Val mse = 0.3393 Epoch 22: Train mse = 0.2521 | Val mse = 0.3419 Epoch 23: Train mse = 0.2490 | Val mse = 0.3410 Epoch 24: Train mse = 0.2445 | Val mse = 0.3450 Epoch 25: Train mse = 0.2412 | Val mse = 0.3394 Epoch 26: Train mse = 0.2381 | Val mse = 0.3427 Epoch 27: Train mse = 0.2353 | Val mse = 0.3493 Epoch 28: Train mse = 0.2338 | Val mse = 0.3545 Epoch 29: Train mse = 0.2309 | Val mse = 0.3498 Epoch 30: Train mse = 0.2295 | Val mse = 0.3512 Epoch 31: Train mse = 0.2257 | Val mse = 0.3472 Early stopping triggered at epoch 31. Using device: mps Epoch 1: Train mse = 0.8886 | Val mse = 0.8520 Epoch 2: Train mse = 0.6514 | Val mse = 0.5752 Epoch 3: Train mse = 0.3994 | Val mse = 0.4508 Epoch 4: Train mse = 0.3325 | Val mse = 0.3879 Epoch 5: Train mse = 0.3103 | Val mse = 0.3575 Epoch 6: Train mse = 0.2915 | Val mse = 0.3387 Epoch 7: Train mse = 0.2794 | Val mse = 0.3250 Epoch 8: Train mse = 0.2641 | Val mse = 0.3459 Epoch 9: Train mse = 0.2510 | Val mse = 0.3343 Epoch 10: Train mse = 0.2443 | Val mse = 0.3427 Epoch 11: Train mse = 0.2412 | Val mse = 0.3499 Epoch 12: Train mse = 0.2335 | Val mse = 0.3432 Epoch 13: Train mse = 0.2285 | Val mse = 0.3433 Epoch 14: Train mse = 0.2250 | Val mse = 0.3431 Epoch 15: Train mse = 0.2208 | Val mse = 0.3460 Epoch 16: Train mse = 0.2146 | Val mse = 0.3454 Epoch 17: Train mse = 0.2070 | Val mse = 0.3439 Epoch 18: Train mse = 0.2023 | Val mse = 0.3484 Epoch 19: Train mse = 0.1955 | Val mse = 0.3502 Epoch 20: Train mse = 0.1942 | Val mse = 0.3488 Epoch 21: Train mse = 0.1895 | Val mse = 0.3386 Epoch 22: Train mse = 0.1834 | Val mse = 0.3445 Epoch 23: Train mse = 0.1786 | Val mse = 0.3472 Epoch 24: Train mse = 0.1750 | Val mse = 0.3632 Epoch 25: Train mse = 0.1725 | Val mse = 0.3575 Epoch 26: Train mse = 0.1691 | Val mse = 0.3677 Epoch 27: Train mse = 0.1650 | Val mse = 0.3626 Epoch 28: Train mse = 0.1627 | Val mse = 0.3614 Epoch 29: Train mse = 0.1605 | Val mse = 0.3847 Epoch 30: Train mse = 0.1598 | Val mse = 0.3776 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.8919 | Val mse = 0.8754 Epoch 2: Train mse = 0.6615 | Val mse = 0.5760 Epoch 3: Train mse = 0.4062 | Val mse = 0.4394 Epoch 4: Train mse = 0.3335 | Val mse = 0.3664 Epoch 5: Train mse = 0.3110 | Val mse = 0.3669 Epoch 6: Train mse = 0.2864 | Val mse = 0.3261 Epoch 7: Train mse = 0.2806 | Val mse = 0.3184 Epoch 8: Train mse = 0.2672 | Val mse = 0.3448 Epoch 9: Train mse = 0.2533 | Val mse = 0.3241 Epoch 10: Train mse = 0.2428 | Val mse = 0.3329 Epoch 11: Train mse = 0.2385 | Val mse = 0.3340 Epoch 12: Train mse = 0.2280 | Val mse = 0.3362 Epoch 13: Train mse = 0.2230 | Val mse = 0.3406 Epoch 14: Train mse = 0.2171 | Val mse = 0.3374 Epoch 15: Train mse = 0.2120 | Val mse = 0.3476 Epoch 16: Train mse = 0.2085 | Val mse = 0.3471 Epoch 17: Train mse = 0.2062 | Val mse = 0.3484 Epoch 18: Train mse = 0.2035 | Val mse = 0.3516 Epoch 19: Train mse = 0.1990 | Val mse = 0.3531 Epoch 20: Train mse = 0.1946 | Val mse = 0.3513 Epoch 21: Train mse = 0.1911 | Val mse = 0.3535 Epoch 22: Train mse = 0.1858 | Val mse = 0.3569 Epoch 23: Train mse = 0.1864 | Val mse = 0.3667 Epoch 24: Train mse = 0.1818 | Val mse = 0.3607 Epoch 25: Train mse = 0.1775 | Val mse = 0.3510 Epoch 26: Train mse = 0.1723 | Val mse = 0.3620 Epoch 27: Train mse = 0.1703 | Val mse = 0.3744 Epoch 28: Train mse = 0.1761 | Val mse = 0.3753 Epoch 29: Train mse = 0.1740 | Val mse = 0.3551 Epoch 30: Train mse = 0.1726 | Val mse = 0.3705 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9988 | Val mse = 1.1751 Epoch 2: Train mse = 0.9280 | Val mse = 0.9471 Epoch 3: Train mse = 0.8810 | Val mse = 0.9378 Epoch 4: Train mse = 0.8550 | Val mse = 0.9119 Epoch 5: Train mse = 0.8345 | Val mse = 0.8945 Epoch 6: Train mse = 0.7742 | Val mse = 0.8082 Epoch 7: Train mse = 0.7215 | Val mse = 0.7927 Epoch 8: Train mse = 0.6819 | Val mse = 0.7664 Epoch 9: Train mse = 0.6341 | Val mse = 0.7204 Epoch 10: Train mse = 0.5571 | Val mse = 0.6648 Epoch 11: Train mse = 0.4751 | Val mse = 0.5926 Epoch 12: Train mse = 0.3998 | Val mse = 0.5485 Epoch 13: Train mse = 0.3382 | Val mse = 0.5109 Epoch 14: Train mse = 0.3027 | Val mse = 0.5084 Epoch 15: Train mse = 0.2828 | Val mse = 0.4817 Epoch 16: Train mse = 0.2656 | Val mse = 0.4528 Epoch 17: Train mse = 0.2547 | Val mse = 0.4682 Epoch 18: Train mse = 0.2454 | Val mse = 0.4539 Epoch 19: Train mse = 0.2347 | Val mse = 0.4548 Epoch 20: Train mse = 0.2250 | Val mse = 0.4490 Epoch 21: Train mse = 0.2189 | Val mse = 0.4601 Epoch 22: Train mse = 0.2114 | Val mse = 0.4677 Epoch 23: Train mse = 0.2072 | Val mse = 0.4722 Epoch 24: Train mse = 0.2030 | Val mse = 0.4683 Epoch 25: Train mse = 0.1983 | Val mse = 0.4620 Epoch 26: Train mse = 0.1947 | Val mse = 0.4800 Epoch 27: Train mse = 0.1911 | Val mse = 0.4577 Epoch 28: Train mse = 0.1884 | Val mse = 0.4758 Epoch 29: Train mse = 0.1877 | Val mse = 0.4661 Epoch 30: Train mse = 0.1832 | Val mse = 0.4848 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9992 | Val mse = 1.1787 Epoch 2: Train mse = 0.9287 | Val mse = 0.9470 Epoch 3: Train mse = 0.8813 | Val mse = 0.9392 Epoch 4: Train mse = 0.8562 | Val mse = 0.9119 Epoch 5: Train mse = 0.8387 | Val mse = 0.8986 Epoch 6: Train mse = 0.7818 | Val mse = 0.8097 Epoch 7: Train mse = 0.7254 | Val mse = 0.7911 Epoch 8: Train mse = 0.6867 | Val mse = 0.7659 Epoch 9: Train mse = 0.6406 | Val mse = 0.7197 Epoch 10: Train mse = 0.5664 | Val mse = 0.6621 Epoch 11: Train mse = 0.4877 | Val mse = 0.5861 Epoch 12: Train mse = 0.4117 | Val mse = 0.5321 Epoch 13: Train mse = 0.3471 | Val mse = 0.4927 Epoch 14: Train mse = 0.3082 | Val mse = 0.5053 Epoch 15: Train mse = 0.2889 | Val mse = 0.4721 Epoch 16: Train mse = 0.2713 | Val mse = 0.4498 Epoch 17: Train mse = 0.2603 | Val mse = 0.4476 Epoch 18: Train mse = 0.2528 | Val mse = 0.4408 Epoch 19: Train mse = 0.2436 | Val mse = 0.4272 Epoch 20: Train mse = 0.2337 | Val mse = 0.4355 Epoch 21: Train mse = 0.2264 | Val mse = 0.4367 Epoch 22: Train mse = 0.2193 | Val mse = 0.4564 Epoch 23: Train mse = 0.2146 | Val mse = 0.4592 Epoch 24: Train mse = 0.2096 | Val mse = 0.4445 Epoch 25: Train mse = 0.2034 | Val mse = 0.4596 Epoch 26: Train mse = 0.1994 | Val mse = 0.4618 Epoch 27: Train mse = 0.1965 | Val mse = 0.4433 Epoch 28: Train mse = 0.1945 | Val mse = 0.4529 Epoch 29: Train mse = 0.1932 | Val mse = 0.4524 Epoch 30: Train mse = 0.1890 | Val mse = 0.4613 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.8878 | Val mse = 0.8138 Epoch 2: Train mse = 0.6905 | Val mse = 0.6508 Epoch 3: Train mse = 0.4295 | Val mse = 0.4367 Epoch 4: Train mse = 0.3451 | Val mse = 0.3707 Epoch 5: Train mse = 0.3028 | Val mse = 0.3690 Epoch 6: Train mse = 0.2800 | Val mse = 0.3378 Epoch 7: Train mse = 0.2650 | Val mse = 0.3452 Epoch 8: Train mse = 0.2506 | Val mse = 0.3379 Epoch 9: Train mse = 0.2460 | Val mse = 0.3521 Epoch 10: Train mse = 0.2394 | Val mse = 0.3462 Epoch 11: Train mse = 0.2339 | Val mse = 0.3489 Epoch 12: Train mse = 0.2280 | Val mse = 0.3334 Epoch 13: Train mse = 0.2248 | Val mse = 0.3383 Epoch 14: Train mse = 0.2240 | Val mse = 0.3471 Epoch 15: Train mse = 0.2191 | Val mse = 0.3510 Epoch 16: Train mse = 0.2130 | Val mse = 0.3411 Epoch 17: Train mse = 0.2091 | Val mse = 0.3493 Epoch 18: Train mse = 0.2029 | Val mse = 0.3450 Epoch 19: Train mse = 0.1986 | Val mse = 0.3413 Epoch 20: Train mse = 0.1954 | Val mse = 0.3473 Epoch 21: Train mse = 0.1909 | Val mse = 0.3534 Epoch 22: Train mse = 0.1877 | Val mse = 0.3440 Epoch 23: Train mse = 0.1867 | Val mse = 0.3562 Epoch 24: Train mse = 0.1874 | Val mse = 0.3670 Epoch 25: Train mse = 0.1945 | Val mse = 0.3704 Epoch 26: Train mse = 0.1907 | Val mse = 0.3692 Epoch 27: Train mse = 0.1879 | Val mse = 0.3554 Epoch 28: Train mse = 0.1876 | Val mse = 0.3595 Epoch 29: Train mse = 0.1877 | Val mse = 0.3609 Epoch 30: Train mse = 0.1882 | Val mse = 0.3643 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.8900 | Val mse = 0.8101 Epoch 2: Train mse = 0.6919 | Val mse = 0.6540 Epoch 3: Train mse = 0.4364 | Val mse = 0.4463 Epoch 4: Train mse = 0.3461 | Val mse = 0.3738 Epoch 5: Train mse = 0.3094 | Val mse = 0.3558 Epoch 6: Train mse = 0.2847 | Val mse = 0.3552 Epoch 7: Train mse = 0.2695 | Val mse = 0.3399 Epoch 8: Train mse = 0.2680 | Val mse = 0.3689 Epoch 9: Train mse = 0.2580 | Val mse = 0.3465 Epoch 10: Train mse = 0.2422 | Val mse = 0.3390 Epoch 11: Train mse = 0.2320 | Val mse = 0.3189 Epoch 12: Train mse = 0.2166 | Val mse = 0.3341 Epoch 13: Train mse = 0.2098 | Val mse = 0.3279 Epoch 14: Train mse = 0.2035 | Val mse = 0.3244 Epoch 15: Train mse = 0.1961 | Val mse = 0.3385 Epoch 16: Train mse = 0.1923 | Val mse = 0.3361 Epoch 17: Train mse = 0.1891 | Val mse = 0.3299 Epoch 18: Train mse = 0.1844 | Val mse = 0.3393 Epoch 19: Train mse = 0.1823 | Val mse = 0.3531 Epoch 20: Train mse = 0.1819 | Val mse = 0.3418 Epoch 21: Train mse = 0.1810 | Val mse = 0.3528 Epoch 22: Train mse = 0.1852 | Val mse = 0.3572 Epoch 23: Train mse = 0.1925 | Val mse = 0.3621 Epoch 24: Train mse = 0.2041 | Val mse = 0.3551 Epoch 25: Train mse = 0.2138 | Val mse = 0.3832 Epoch 26: Train mse = 0.2124 | Val mse = 0.3488 Epoch 27: Train mse = 0.2108 | Val mse = 0.3638 Epoch 28: Train mse = 0.2045 | Val mse = 0.3574 Epoch 29: Train mse = 0.1985 | Val mse = 0.3611 Epoch 30: Train mse = 0.1932 | Val mse = 0.3468 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9971 | Val mse = 1.2215 Epoch 2: Train mse = 0.9967 | Val mse = 1.2121 Epoch 3: Train mse = 0.9869 | Val mse = 1.1968 Epoch 4: Train mse = 0.9766 | Val mse = 1.1718 Epoch 5: Train mse = 0.9652 | Val mse = 1.1337 Epoch 6: Train mse = 0.9452 | Val mse = 1.0829 Epoch 7: Train mse = 0.9337 | Val mse = 1.0290 Epoch 8: Train mse = 0.9181 | Val mse = 0.9867 Epoch 9: Train mse = 0.9064 | Val mse = 0.9627 Epoch 10: Train mse = 0.8912 | Val mse = 0.9504 Epoch 11: Train mse = 0.8809 | Val mse = 0.9436 Epoch 12: Train mse = 0.8807 | Val mse = 0.9316 Epoch 13: Train mse = 0.8712 | Val mse = 0.9212 Epoch 14: Train mse = 0.8571 | Val mse = 0.9107 Epoch 15: Train mse = 0.8510 | Val mse = 0.9003 Epoch 16: Train mse = 0.8380 | Val mse = 0.8892 Epoch 17: Train mse = 0.8251 | Val mse = 0.8737 Epoch 18: Train mse = 0.8075 | Val mse = 0.8534 Epoch 19: Train mse = 0.7861 | Val mse = 0.8309 Epoch 20: Train mse = 0.7684 | Val mse = 0.8063 Epoch 21: Train mse = 0.7415 | Val mse = 0.7827 Epoch 22: Train mse = 0.7218 | Val mse = 0.7591 Epoch 23: Train mse = 0.6983 | Val mse = 0.7368 Epoch 24: Train mse = 0.6705 | Val mse = 0.7149 Epoch 25: Train mse = 0.6453 | Val mse = 0.6948 Epoch 26: Train mse = 0.6253 | Val mse = 0.6770 Epoch 27: Train mse = 0.6027 | Val mse = 0.6631 Epoch 28: Train mse = 0.5836 | Val mse = 0.6460 Epoch 29: Train mse = 0.5623 | Val mse = 0.6307 Epoch 30: Train mse = 0.5388 | Val mse = 0.6171 Epoch 31: Train mse = 0.5204 | Val mse = 0.6008 Epoch 32: Train mse = 0.5050 | Val mse = 0.5843 Epoch 33: Train mse = 0.4853 | Val mse = 0.5652 Epoch 34: Train mse = 0.4643 | Val mse = 0.5476 Epoch 35: Train mse = 0.4491 | Val mse = 0.5313 Epoch 36: Train mse = 0.4276 | Val mse = 0.5157 Epoch 37: Train mse = 0.4134 | Val mse = 0.5000 Epoch 38: Train mse = 0.3968 | Val mse = 0.4812 Epoch 39: Train mse = 0.3819 | Val mse = 0.4667 Epoch 40: Train mse = 0.3678 | Val mse = 0.4525 Epoch 41: Train mse = 0.3501 | Val mse = 0.4417 Epoch 42: Train mse = 0.3416 | Val mse = 0.4266 Epoch 43: Train mse = 0.3321 | Val mse = 0.4179 Epoch 44: Train mse = 0.3202 | Val mse = 0.4132 Epoch 45: Train mse = 0.3146 | Val mse = 0.4019 Epoch 46: Train mse = 0.3098 | Val mse = 0.3958 Epoch 47: Train mse = 0.2984 | Val mse = 0.3911 Epoch 48: Train mse = 0.3008 | Val mse = 0.3845 Epoch 49: Train mse = 0.2917 | Val mse = 0.3816 Epoch 50: Train mse = 0.2900 | Val mse = 0.3825 Using device: mps Epoch 1: Train mse = 0.9972 | Val mse = 1.2215 Epoch 2: Train mse = 0.9970 | Val mse = 1.2122 Epoch 3: Train mse = 0.9874 | Val mse = 1.1973 Epoch 4: Train mse = 0.9773 | Val mse = 1.1731 Epoch 5: Train mse = 0.9661 | Val mse = 1.1356 Epoch 6: Train mse = 0.9462 | Val mse = 1.0851 Epoch 7: Train mse = 0.9346 | Val mse = 1.0309 Epoch 8: Train mse = 0.9189 | Val mse = 0.9882 Epoch 9: Train mse = 0.9073 | Val mse = 0.9643 Epoch 10: Train mse = 0.8922 | Val mse = 0.9520 Epoch 11: Train mse = 0.8820 | Val mse = 0.9450 Epoch 12: Train mse = 0.8820 | Val mse = 0.9326 Epoch 13: Train mse = 0.8726 | Val mse = 0.9217 Epoch 14: Train mse = 0.8590 | Val mse = 0.9110 Epoch 15: Train mse = 0.8535 | Val mse = 0.9010 Epoch 16: Train mse = 0.8415 | Val mse = 0.8908 Epoch 17: Train mse = 0.8300 | Val mse = 0.8764 Epoch 18: Train mse = 0.8138 | Val mse = 0.8565 Epoch 19: Train mse = 0.7937 | Val mse = 0.8340 Epoch 20: Train mse = 0.7770 | Val mse = 0.8093 Epoch 21: Train mse = 0.7508 | Val mse = 0.7854 Epoch 22: Train mse = 0.7311 | Val mse = 0.7615 Epoch 23: Train mse = 0.7075 | Val mse = 0.7385 Epoch 24: Train mse = 0.6795 | Val mse = 0.7161 Epoch 25: Train mse = 0.6540 | Val mse = 0.6958 Epoch 26: Train mse = 0.6337 | Val mse = 0.6782 Epoch 27: Train mse = 0.6111 | Val mse = 0.6647 Epoch 28: Train mse = 0.5919 | Val mse = 0.6476 Epoch 29: Train mse = 0.5709 | Val mse = 0.6325 Epoch 30: Train mse = 0.5474 | Val mse = 0.6192 Epoch 31: Train mse = 0.5296 | Val mse = 0.6033 Epoch 32: Train mse = 0.5146 | Val mse = 0.5868 Epoch 33: Train mse = 0.4951 | Val mse = 0.5672 Epoch 34: Train mse = 0.4743 | Val mse = 0.5492 Epoch 35: Train mse = 0.4594 | Val mse = 0.5323 Epoch 36: Train mse = 0.4376 | Val mse = 0.5157 Epoch 37: Train mse = 0.4234 | Val mse = 0.4993 Epoch 38: Train mse = 0.4064 | Val mse = 0.4799 Epoch 39: Train mse = 0.3910 | Val mse = 0.4643 Epoch 40: Train mse = 0.3762 | Val mse = 0.4496 Epoch 41: Train mse = 0.3580 | Val mse = 0.4378 Epoch 42: Train mse = 0.3489 | Val mse = 0.4219 Epoch 43: Train mse = 0.3390 | Val mse = 0.4126 Epoch 44: Train mse = 0.3265 | Val mse = 0.4072 Epoch 45: Train mse = 0.3205 | Val mse = 0.3955 Epoch 46: Train mse = 0.3156 | Val mse = 0.3889 Epoch 47: Train mse = 0.3037 | Val mse = 0.3835 Epoch 48: Train mse = 0.3062 | Val mse = 0.3768 Epoch 49: Train mse = 0.2971 | Val mse = 0.3735 Epoch 50: Train mse = 0.2952 | Val mse = 0.3739 Using device: mps Epoch 1: Train mse = 0.9685 | Val mse = 0.9857 Epoch 2: Train mse = 0.8959 | Val mse = 0.9374 Epoch 3: Train mse = 0.8397 | Val mse = 0.8505 Epoch 4: Train mse = 0.7428 | Val mse = 0.7307 Epoch 5: Train mse = 0.6379 | Val mse = 0.6530 Epoch 6: Train mse = 0.5467 | Val mse = 0.5881 Epoch 7: Train mse = 0.4532 | Val mse = 0.4906 Epoch 8: Train mse = 0.3698 | Val mse = 0.4048 Epoch 9: Train mse = 0.3205 | Val mse = 0.3730 Epoch 10: Train mse = 0.2980 | Val mse = 0.3779 Epoch 11: Train mse = 0.2837 | Val mse = 0.3471 Epoch 12: Train mse = 0.2719 | Val mse = 0.3548 Epoch 13: Train mse = 0.2591 | Val mse = 0.3498 Epoch 14: Train mse = 0.2527 | Val mse = 0.3551 Epoch 15: Train mse = 0.2438 | Val mse = 0.3635 Epoch 16: Train mse = 0.2394 | Val mse = 0.3649 Epoch 17: Train mse = 0.2278 | Val mse = 0.3666 Epoch 18: Train mse = 0.2201 | Val mse = 0.3757 Epoch 19: Train mse = 0.2131 | Val mse = 0.3766 Epoch 20: Train mse = 0.2091 | Val mse = 0.3754 Epoch 21: Train mse = 0.2044 | Val mse = 0.3770 Epoch 22: Train mse = 0.2012 | Val mse = 0.3778 Epoch 23: Train mse = 0.1955 | Val mse = 0.3878 Epoch 24: Train mse = 0.1897 | Val mse = 0.3858 Epoch 25: Train mse = 0.1835 | Val mse = 0.3926 Epoch 26: Train mse = 0.1804 | Val mse = 0.3928 Epoch 27: Train mse = 0.1795 | Val mse = 0.3954 Epoch 28: Train mse = 0.1759 | Val mse = 0.3904 Epoch 29: Train mse = 0.1723 | Val mse = 0.3996 Epoch 30: Train mse = 0.1684 | Val mse = 0.4006 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9691 | Val mse = 0.9872 Epoch 2: Train mse = 0.8967 | Val mse = 0.9410 Epoch 3: Train mse = 0.8445 | Val mse = 0.8512 Epoch 4: Train mse = 0.7522 | Val mse = 0.7375 Epoch 5: Train mse = 0.6476 | Val mse = 0.6567 Epoch 6: Train mse = 0.5574 | Val mse = 0.5951 Epoch 7: Train mse = 0.4656 | Val mse = 0.4990 Epoch 8: Train mse = 0.3798 | Val mse = 0.4065 Epoch 9: Train mse = 0.3258 | Val mse = 0.3683 Epoch 10: Train mse = 0.3023 | Val mse = 0.3696 Epoch 11: Train mse = 0.2872 | Val mse = 0.3416 Epoch 12: Train mse = 0.2771 | Val mse = 0.3469 Epoch 13: Train mse = 0.2643 | Val mse = 0.3434 Epoch 14: Train mse = 0.2586 | Val mse = 0.3487 Epoch 15: Train mse = 0.2494 | Val mse = 0.3661 Epoch 16: Train mse = 0.2443 | Val mse = 0.3610 Epoch 17: Train mse = 0.2340 | Val mse = 0.3653 Epoch 18: Train mse = 0.2265 | Val mse = 0.3780 Epoch 19: Train mse = 0.2203 | Val mse = 0.3750 Epoch 20: Train mse = 0.2172 | Val mse = 0.3766 Epoch 21: Train mse = 0.2124 | Val mse = 0.3728 Epoch 22: Train mse = 0.2101 | Val mse = 0.3729 Epoch 23: Train mse = 0.2046 | Val mse = 0.3809 Epoch 24: Train mse = 0.1973 | Val mse = 0.3844 Epoch 25: Train mse = 0.1903 | Val mse = 0.3899 Epoch 26: Train mse = 0.1865 | Val mse = 0.3909 Epoch 27: Train mse = 0.1848 | Val mse = 0.3929 Epoch 28: Train mse = 0.1815 | Val mse = 0.3957 Epoch 29: Train mse = 0.1761 | Val mse = 0.4010 Epoch 30: Train mse = 0.1725 | Val mse = 0.3972 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 1.0003 | Val mse = 1.2205 Epoch 2: Train mse = 0.9890 | Val mse = 1.2104 Epoch 3: Train mse = 0.9823 | Val mse = 1.1921 Epoch 4: Train mse = 0.9777 | Val mse = 1.1622 Epoch 5: Train mse = 0.9638 | Val mse = 1.1203 Epoch 6: Train mse = 0.9450 | Val mse = 1.0697 Epoch 7: Train mse = 0.9274 | Val mse = 1.0211 Epoch 8: Train mse = 0.9158 | Val mse = 0.9860 Epoch 9: Train mse = 0.9024 | Val mse = 0.9656 Epoch 10: Train mse = 0.8959 | Val mse = 0.9560 Epoch 11: Train mse = 0.8769 | Val mse = 0.9443 Epoch 12: Train mse = 0.8711 | Val mse = 0.9284 Epoch 13: Train mse = 0.8590 | Val mse = 0.9121 Epoch 14: Train mse = 0.8429 | Val mse = 0.8967 Epoch 15: Train mse = 0.8291 | Val mse = 0.8825 Epoch 16: Train mse = 0.8137 | Val mse = 0.8664 Epoch 17: Train mse = 0.7918 | Val mse = 0.8488 Epoch 18: Train mse = 0.7765 | Val mse = 0.8346 Epoch 19: Train mse = 0.7573 | Val mse = 0.8192 Epoch 20: Train mse = 0.7417 | Val mse = 0.8031 Epoch 21: Train mse = 0.7213 | Val mse = 0.7870 Epoch 22: Train mse = 0.7006 | Val mse = 0.7703 Epoch 23: Train mse = 0.6852 | Val mse = 0.7534 Epoch 24: Train mse = 0.6648 | Val mse = 0.7355 Epoch 25: Train mse = 0.6419 | Val mse = 0.7169 Epoch 26: Train mse = 0.6209 | Val mse = 0.6989 Epoch 27: Train mse = 0.6000 | Val mse = 0.6790 Epoch 28: Train mse = 0.5737 | Val mse = 0.6633 Epoch 29: Train mse = 0.5543 | Val mse = 0.6472 Epoch 30: Train mse = 0.5344 | Val mse = 0.6296 Epoch 31: Train mse = 0.5092 | Val mse = 0.6117 Epoch 32: Train mse = 0.4918 | Val mse = 0.5952 Epoch 33: Train mse = 0.4680 | Val mse = 0.5791 Epoch 34: Train mse = 0.4502 | Val mse = 0.5638 Epoch 35: Train mse = 0.4286 | Val mse = 0.5497 Epoch 36: Train mse = 0.4092 | Val mse = 0.5340 Epoch 37: Train mse = 0.3948 | Val mse = 0.5201 Epoch 38: Train mse = 0.3773 | Val mse = 0.5053 Epoch 39: Train mse = 0.3664 | Val mse = 0.4928 Epoch 40: Train mse = 0.3513 | Val mse = 0.4816 Epoch 41: Train mse = 0.3358 | Val mse = 0.4703 Epoch 42: Train mse = 0.3289 | Val mse = 0.4594 Epoch 43: Train mse = 0.3171 | Val mse = 0.4533 Epoch 44: Train mse = 0.3025 | Val mse = 0.4437 Epoch 45: Train mse = 0.2993 | Val mse = 0.4380 Epoch 46: Train mse = 0.2910 | Val mse = 0.4340 Epoch 47: Train mse = 0.2840 | Val mse = 0.4289 Epoch 48: Train mse = 0.2770 | Val mse = 0.4241 Epoch 49: Train mse = 0.2721 | Val mse = 0.4217 Epoch 50: Train mse = 0.2631 | Val mse = 0.4179 Using device: mps Epoch 1: Train mse = 1.0004 | Val mse = 1.2205 Epoch 2: Train mse = 0.9893 | Val mse = 1.2109 Epoch 3: Train mse = 0.9828 | Val mse = 1.1932 Epoch 4: Train mse = 0.9783 | Val mse = 1.1638 Epoch 5: Train mse = 0.9644 | Val mse = 1.1220 Epoch 6: Train mse = 0.9455 | Val mse = 1.0709 Epoch 7: Train mse = 0.9276 | Val mse = 1.0217 Epoch 8: Train mse = 0.9159 | Val mse = 0.9863 Epoch 9: Train mse = 0.9024 | Val mse = 0.9659 Epoch 10: Train mse = 0.8959 | Val mse = 0.9563 Epoch 11: Train mse = 0.8769 | Val mse = 0.9443 Epoch 12: Train mse = 0.8712 | Val mse = 0.9281 Epoch 13: Train mse = 0.8594 | Val mse = 0.9117 Epoch 14: Train mse = 0.8432 | Val mse = 0.8964 Epoch 15: Train mse = 0.8292 | Val mse = 0.8821 Epoch 16: Train mse = 0.8135 | Val mse = 0.8652 Epoch 17: Train mse = 0.7913 | Val mse = 0.8469 Epoch 18: Train mse = 0.7762 | Val mse = 0.8324 Epoch 19: Train mse = 0.7570 | Val mse = 0.8169 Epoch 20: Train mse = 0.7420 | Val mse = 0.8008 Epoch 21: Train mse = 0.7221 | Val mse = 0.7852 Epoch 22: Train mse = 0.7022 | Val mse = 0.7690 Epoch 23: Train mse = 0.6876 | Val mse = 0.7528 Epoch 24: Train mse = 0.6678 | Val mse = 0.7353 Epoch 25: Train mse = 0.6450 | Val mse = 0.7160 Epoch 26: Train mse = 0.6243 | Val mse = 0.6973 Epoch 27: Train mse = 0.6036 | Val mse = 0.6767 Epoch 28: Train mse = 0.5775 | Val mse = 0.6605 Epoch 29: Train mse = 0.5584 | Val mse = 0.6443 Epoch 30: Train mse = 0.5386 | Val mse = 0.6269 Epoch 31: Train mse = 0.5135 | Val mse = 0.6090 Epoch 32: Train mse = 0.4960 | Val mse = 0.5923 Epoch 33: Train mse = 0.4723 | Val mse = 0.5754 Epoch 34: Train mse = 0.4543 | Val mse = 0.5594 Epoch 35: Train mse = 0.4320 | Val mse = 0.5450 Epoch 36: Train mse = 0.4122 | Val mse = 0.5282 Epoch 37: Train mse = 0.3972 | Val mse = 0.5132 Epoch 38: Train mse = 0.3793 | Val mse = 0.4972 Epoch 39: Train mse = 0.3685 | Val mse = 0.4836 Epoch 40: Train mse = 0.3533 | Val mse = 0.4715 Epoch 41: Train mse = 0.3380 | Val mse = 0.4603 Epoch 42: Train mse = 0.3314 | Val mse = 0.4475 Epoch 43: Train mse = 0.3196 | Val mse = 0.4404 Epoch 44: Train mse = 0.3050 | Val mse = 0.4300 Epoch 45: Train mse = 0.3024 | Val mse = 0.4233 Epoch 46: Train mse = 0.2942 | Val mse = 0.4186 Epoch 47: Train mse = 0.2873 | Val mse = 0.4133 Epoch 48: Train mse = 0.2807 | Val mse = 0.4082 Epoch 49: Train mse = 0.2765 | Val mse = 0.4047 Epoch 50: Train mse = 0.2674 | Val mse = 0.4018 Using device: mps Epoch 1: Train mse = 0.9725 | Val mse = 0.9854 Epoch 2: Train mse = 0.8858 | Val mse = 0.9098 Epoch 3: Train mse = 0.8097 | Val mse = 0.8100 Epoch 4: Train mse = 0.7196 | Val mse = 0.7290 Epoch 5: Train mse = 0.6143 | Val mse = 0.6412 Epoch 6: Train mse = 0.5035 | Val mse = 0.5620 Epoch 7: Train mse = 0.4042 | Val mse = 0.4604 Epoch 8: Train mse = 0.3351 | Val mse = 0.4207 Epoch 9: Train mse = 0.2987 | Val mse = 0.3953 Epoch 10: Train mse = 0.2837 | Val mse = 0.3808 Epoch 11: Train mse = 0.2623 | Val mse = 0.3761 Epoch 12: Train mse = 0.2461 | Val mse = 0.3612 Epoch 13: Train mse = 0.2336 | Val mse = 0.3740 Epoch 14: Train mse = 0.2208 | Val mse = 0.3623 Epoch 15: Train mse = 0.2107 | Val mse = 0.3624 Epoch 16: Train mse = 0.2036 | Val mse = 0.3724 Epoch 17: Train mse = 0.1975 | Val mse = 0.3778 Epoch 18: Train mse = 0.1885 | Val mse = 0.3697 Epoch 19: Train mse = 0.1838 | Val mse = 0.3862 Epoch 20: Train mse = 0.1801 | Val mse = 0.3824 Epoch 21: Train mse = 0.1765 | Val mse = 0.3806 Epoch 22: Train mse = 0.1693 | Val mse = 0.3813 Epoch 23: Train mse = 0.1656 | Val mse = 0.3917 Epoch 24: Train mse = 0.1624 | Val mse = 0.3988 Epoch 25: Train mse = 0.1588 | Val mse = 0.3978 Epoch 26: Train mse = 0.1554 | Val mse = 0.3988 Epoch 27: Train mse = 0.1505 | Val mse = 0.4061 Epoch 28: Train mse = 0.1486 | Val mse = 0.4034 Epoch 29: Train mse = 0.1454 | Val mse = 0.4072 Epoch 30: Train mse = 0.1440 | Val mse = 0.4168 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9730 | Val mse = 0.9869 Epoch 2: Train mse = 0.8856 | Val mse = 0.9103 Epoch 3: Train mse = 0.8099 | Val mse = 0.8111 Epoch 4: Train mse = 0.7220 | Val mse = 0.7307 Epoch 5: Train mse = 0.6197 | Val mse = 0.6445 Epoch 6: Train mse = 0.5105 | Val mse = 0.5608 Epoch 7: Train mse = 0.4113 | Val mse = 0.4553 Epoch 8: Train mse = 0.3402 | Val mse = 0.4135 Epoch 9: Train mse = 0.3013 | Val mse = 0.3983 Epoch 10: Train mse = 0.2856 | Val mse = 0.3728 Epoch 11: Train mse = 0.2661 | Val mse = 0.3780 Epoch 12: Train mse = 0.2521 | Val mse = 0.3584 Epoch 13: Train mse = 0.2381 | Val mse = 0.3517 Epoch 14: Train mse = 0.2250 | Val mse = 0.3613 Epoch 15: Train mse = 0.2170 | Val mse = 0.3554 Epoch 16: Train mse = 0.2089 | Val mse = 0.3588 Epoch 17: Train mse = 0.2013 | Val mse = 0.3610 Epoch 18: Train mse = 0.1925 | Val mse = 0.3558 Epoch 19: Train mse = 0.1883 | Val mse = 0.3613 Epoch 20: Train mse = 0.1844 | Val mse = 0.3647 Epoch 21: Train mse = 0.1802 | Val mse = 0.3620 Epoch 22: Train mse = 0.1734 | Val mse = 0.3704 Epoch 23: Train mse = 0.1695 | Val mse = 0.3689 Epoch 24: Train mse = 0.1663 | Val mse = 0.3801 Epoch 25: Train mse = 0.1624 | Val mse = 0.3773 Epoch 26: Train mse = 0.1581 | Val mse = 0.3725 Epoch 27: Train mse = 0.1538 | Val mse = 0.3744 Epoch 28: Train mse = 0.1516 | Val mse = 0.3809 Epoch 29: Train mse = 0.1476 | Val mse = 0.3962 Epoch 30: Train mse = 0.1447 | Val mse = 0.3978 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9959 | Val mse = 1.2294 Epoch 2: Train mse = 1.0000 | Val mse = 1.2266 Epoch 3: Train mse = 0.9927 | Val mse = 1.2216 Epoch 4: Train mse = 0.9919 | Val mse = 1.2132 Epoch 5: Train mse = 0.9918 | Val mse = 1.1940 Epoch 6: Train mse = 0.9723 | Val mse = 1.1559 Epoch 7: Train mse = 0.9559 | Val mse = 1.0955 Epoch 8: Train mse = 0.9308 | Val mse = 1.0264 Epoch 9: Train mse = 0.9158 | Val mse = 0.9763 Epoch 10: Train mse = 0.8956 | Val mse = 0.9580 Epoch 11: Train mse = 0.8934 | Val mse = 0.9535 Epoch 12: Train mse = 0.8865 | Val mse = 0.9500 Epoch 13: Train mse = 0.8778 | Val mse = 0.9471 Epoch 14: Train mse = 0.8692 | Val mse = 0.9399 Epoch 15: Train mse = 0.8697 | Val mse = 0.9301 Epoch 16: Train mse = 0.8576 | Val mse = 0.9263 Epoch 17: Train mse = 0.8611 | Val mse = 0.9244 Epoch 18: Train mse = 0.8570 | Val mse = 0.9215 Epoch 19: Train mse = 0.8527 | Val mse = 0.9176 Epoch 20: Train mse = 0.8502 | Val mse = 0.9128 Epoch 21: Train mse = 0.8487 | Val mse = 0.9102 Epoch 22: Train mse = 0.8437 | Val mse = 0.9072 Epoch 23: Train mse = 0.8362 | Val mse = 0.9037 Epoch 24: Train mse = 0.8389 | Val mse = 0.8995 Epoch 25: Train mse = 0.8288 | Val mse = 0.8954 Epoch 26: Train mse = 0.8232 | Val mse = 0.8855 Epoch 27: Train mse = 0.8132 | Val mse = 0.8733 Epoch 28: Train mse = 0.7970 | Val mse = 0.8591 Epoch 29: Train mse = 0.7784 | Val mse = 0.8358 Epoch 30: Train mse = 0.7625 | Val mse = 0.8188 Epoch 31: Train mse = 0.7369 | Val mse = 0.8008 Epoch 32: Train mse = 0.7172 | Val mse = 0.7896 Epoch 33: Train mse = 0.7083 | Val mse = 0.7892 Epoch 34: Train mse = 0.6941 | Val mse = 0.7871 Epoch 35: Train mse = 0.6749 | Val mse = 0.7872 Epoch 36: Train mse = 0.6637 | Val mse = 0.7874 Epoch 37: Train mse = 0.6472 | Val mse = 0.7830 Epoch 38: Train mse = 0.6307 | Val mse = 0.7790 Epoch 39: Train mse = 0.6154 | Val mse = 0.7707 Epoch 40: Train mse = 0.6066 | Val mse = 0.7593 Epoch 41: Train mse = 0.5862 | Val mse = 0.7457 Epoch 42: Train mse = 0.5705 | Val mse = 0.7339 Epoch 43: Train mse = 0.5487 | Val mse = 0.7166 Epoch 44: Train mse = 0.5328 | Val mse = 0.7000 Epoch 45: Train mse = 0.5126 | Val mse = 0.6875 Epoch 46: Train mse = 0.4952 | Val mse = 0.6736 Epoch 47: Train mse = 0.4788 | Val mse = 0.6600 Epoch 48: Train mse = 0.4669 | Val mse = 0.6488 Epoch 49: Train mse = 0.4476 | Val mse = 0.6359 Epoch 50: Train mse = 0.4311 | Val mse = 0.6287 Using device: mps Epoch 1: Train mse = 0.9959 | Val mse = 1.2294 Epoch 2: Train mse = 1.0000 | Val mse = 1.2266 Epoch 3: Train mse = 0.9929 | Val mse = 1.2218 Epoch 4: Train mse = 0.9923 | Val mse = 1.2142 Epoch 5: Train mse = 0.9928 | Val mse = 1.1966 Epoch 6: Train mse = 0.9740 | Val mse = 1.1608 Epoch 7: Train mse = 0.9581 | Val mse = 1.1005 Epoch 8: Train mse = 0.9325 | Val mse = 1.0272 Epoch 9: Train mse = 0.9167 | Val mse = 0.9742 Epoch 10: Train mse = 0.8960 | Val mse = 0.9565 Epoch 11: Train mse = 0.8934 | Val mse = 0.9528 Epoch 12: Train mse = 0.8868 | Val mse = 0.9502 Epoch 13: Train mse = 0.8786 | Val mse = 0.9482 Epoch 14: Train mse = 0.8704 | Val mse = 0.9422 Epoch 15: Train mse = 0.8710 | Val mse = 0.9329 Epoch 16: Train mse = 0.8590 | Val mse = 0.9282 Epoch 17: Train mse = 0.8625 | Val mse = 0.9254 Epoch 18: Train mse = 0.8584 | Val mse = 0.9232 Epoch 19: Train mse = 0.8543 | Val mse = 0.9200 Epoch 20: Train mse = 0.8519 | Val mse = 0.9151 Epoch 21: Train mse = 0.8508 | Val mse = 0.9116 Epoch 22: Train mse = 0.8462 | Val mse = 0.9087 Epoch 23: Train mse = 0.8395 | Val mse = 0.9059 Epoch 24: Train mse = 0.8434 | Val mse = 0.9020 Epoch 25: Train mse = 0.8346 | Val mse = 0.8985 Epoch 26: Train mse = 0.8316 | Val mse = 0.8913 Epoch 27: Train mse = 0.8242 | Val mse = 0.8815 Epoch 28: Train mse = 0.8117 | Val mse = 0.8712 Epoch 29: Train mse = 0.7969 | Val mse = 0.8505 Epoch 30: Train mse = 0.7829 | Val mse = 0.8322 Epoch 31: Train mse = 0.7564 | Val mse = 0.8109 Epoch 32: Train mse = 0.7335 | Val mse = 0.7938 Epoch 33: Train mse = 0.7239 | Val mse = 0.7896 Epoch 34: Train mse = 0.7100 | Val mse = 0.7837 Epoch 35: Train mse = 0.6906 | Val mse = 0.7815 Epoch 36: Train mse = 0.6803 | Val mse = 0.7826 Epoch 37: Train mse = 0.6637 | Val mse = 0.7811 Epoch 38: Train mse = 0.6466 | Val mse = 0.7768 Epoch 39: Train mse = 0.6308 | Val mse = 0.7698 Epoch 40: Train mse = 0.6217 | Val mse = 0.7599 Epoch 41: Train mse = 0.6011 | Val mse = 0.7475 Epoch 42: Train mse = 0.5852 | Val mse = 0.7349 Epoch 43: Train mse = 0.5640 | Val mse = 0.7161 Epoch 44: Train mse = 0.5471 | Val mse = 0.6951 Epoch 45: Train mse = 0.5263 | Val mse = 0.6784 Epoch 46: Train mse = 0.5080 | Val mse = 0.6599 Epoch 47: Train mse = 0.4903 | Val mse = 0.6433 Epoch 48: Train mse = 0.4790 | Val mse = 0.6308 Epoch 49: Train mse = 0.4579 | Val mse = 0.6161 Epoch 50: Train mse = 0.4397 | Val mse = 0.6083 Using device: mps Epoch 1: Train mse = 0.9877 | Val mse = 1.1144 Epoch 2: Train mse = 0.9130 | Val mse = 0.9581 Epoch 3: Train mse = 0.8794 | Val mse = 0.9841 Epoch 4: Train mse = 0.8658 | Val mse = 0.9116 Epoch 5: Train mse = 0.8495 | Val mse = 0.9059 Epoch 6: Train mse = 0.8013 | Val mse = 0.8323 Epoch 7: Train mse = 0.7451 | Val mse = 0.7851 Epoch 8: Train mse = 0.7050 | Val mse = 0.7991 Epoch 9: Train mse = 0.6524 | Val mse = 0.6986 Epoch 10: Train mse = 0.5454 | Val mse = 0.5976 Epoch 11: Train mse = 0.4423 | Val mse = 0.5205 Epoch 12: Train mse = 0.3598 | Val mse = 0.4112 Epoch 13: Train mse = 0.3123 | Val mse = 0.3804 Epoch 14: Train mse = 0.3024 | Val mse = 0.3625 Epoch 15: Train mse = 0.2879 | Val mse = 0.3482 Epoch 16: Train mse = 0.2720 | Val mse = 0.3673 Epoch 17: Train mse = 0.2646 | Val mse = 0.3371 Epoch 18: Train mse = 0.2560 | Val mse = 0.3430 Epoch 19: Train mse = 0.2485 | Val mse = 0.3613 Epoch 20: Train mse = 0.2421 | Val mse = 0.3318 Epoch 21: Train mse = 0.2389 | Val mse = 0.3369 Epoch 22: Train mse = 0.2324 | Val mse = 0.3529 Epoch 23: Train mse = 0.2313 | Val mse = 0.3347 Epoch 24: Train mse = 0.2232 | Val mse = 0.3586 Epoch 25: Train mse = 0.2160 | Val mse = 0.3612 Epoch 26: Train mse = 0.2124 | Val mse = 0.3576 Epoch 27: Train mse = 0.2066 | Val mse = 0.3592 Epoch 28: Train mse = 0.2052 | Val mse = 0.3601 Epoch 29: Train mse = 0.2024 | Val mse = 0.3549 Epoch 30: Train mse = 0.1997 | Val mse = 0.3692 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9883 | Val mse = 1.1191 Epoch 2: Train mse = 0.9136 | Val mse = 0.9599 Epoch 3: Train mse = 0.8804 | Val mse = 0.9836 Epoch 4: Train mse = 0.8675 | Val mse = 0.9163 Epoch 5: Train mse = 0.8538 | Val mse = 0.9114 Epoch 6: Train mse = 0.8175 | Val mse = 0.8481 Epoch 7: Train mse = 0.7555 | Val mse = 0.7845 Epoch 8: Train mse = 0.7156 | Val mse = 0.8005 Epoch 9: Train mse = 0.6692 | Val mse = 0.7174 Epoch 10: Train mse = 0.5679 | Val mse = 0.6145 Epoch 11: Train mse = 0.4609 | Val mse = 0.5413 Epoch 12: Train mse = 0.3703 | Val mse = 0.4254 Epoch 13: Train mse = 0.3195 | Val mse = 0.3699 Epoch 14: Train mse = 0.3036 | Val mse = 0.3503 Epoch 15: Train mse = 0.2936 | Val mse = 0.3342 Epoch 16: Train mse = 0.2747 | Val mse = 0.3589 Epoch 17: Train mse = 0.2680 | Val mse = 0.3381 Epoch 18: Train mse = 0.2593 | Val mse = 0.3469 Epoch 19: Train mse = 0.2537 | Val mse = 0.3219 Epoch 20: Train mse = 0.2487 | Val mse = 0.3262 Epoch 21: Train mse = 0.2448 | Val mse = 0.3167 Epoch 22: Train mse = 0.2394 | Val mse = 0.3207 Epoch 23: Train mse = 0.2362 | Val mse = 0.3344 Epoch 24: Train mse = 0.2309 | Val mse = 0.3465 Epoch 25: Train mse = 0.2236 | Val mse = 0.3455 Epoch 26: Train mse = 0.2214 | Val mse = 0.3325 Epoch 27: Train mse = 0.2163 | Val mse = 0.3406 Epoch 28: Train mse = 0.2152 | Val mse = 0.3578 Epoch 29: Train mse = 0.2127 | Val mse = 0.3443 Epoch 30: Train mse = 0.2078 | Val mse = 0.3574 Epoch 31: Train mse = 0.2034 | Val mse = 0.3573 Early stopping triggered at epoch 31. Using device: mps Epoch 1: Train mse = 1.0042 | Val mse = 1.2253 Epoch 2: Train mse = 0.9962 | Val mse = 1.2243 Epoch 3: Train mse = 0.9996 | Val mse = 1.2200 Epoch 4: Train mse = 0.9954 | Val mse = 1.2096 Epoch 5: Train mse = 0.9793 | Val mse = 1.1860 Epoch 6: Train mse = 0.9734 | Val mse = 1.1467 Epoch 7: Train mse = 0.9580 | Val mse = 1.0980 Epoch 8: Train mse = 0.9360 | Val mse = 1.0445 Epoch 9: Train mse = 0.9174 | Val mse = 0.9961 Epoch 10: Train mse = 0.9065 | Val mse = 0.9715 Epoch 11: Train mse = 0.8950 | Val mse = 0.9608 Epoch 12: Train mse = 0.8840 | Val mse = 0.9477 Epoch 13: Train mse = 0.8738 | Val mse = 0.9324 Epoch 14: Train mse = 0.8641 | Val mse = 0.9184 Epoch 15: Train mse = 0.8519 | Val mse = 0.9102 Epoch 16: Train mse = 0.8351 | Val mse = 0.8984 Epoch 17: Train mse = 0.8194 | Val mse = 0.8854 Epoch 18: Train mse = 0.8039 | Val mse = 0.8652 Epoch 19: Train mse = 0.7865 | Val mse = 0.8460 Epoch 20: Train mse = 0.7666 | Val mse = 0.8330 Epoch 21: Train mse = 0.7552 | Val mse = 0.8188 Epoch 22: Train mse = 0.7366 | Val mse = 0.8120 Epoch 23: Train mse = 0.7282 | Val mse = 0.8041 Epoch 24: Train mse = 0.7134 | Val mse = 0.7979 Epoch 25: Train mse = 0.7038 | Val mse = 0.7946 Epoch 26: Train mse = 0.6927 | Val mse = 0.7871 Epoch 27: Train mse = 0.6832 | Val mse = 0.7849 Epoch 28: Train mse = 0.6697 | Val mse = 0.7754 Epoch 29: Train mse = 0.6572 | Val mse = 0.7715 Epoch 30: Train mse = 0.6450 | Val mse = 0.7641 Epoch 31: Train mse = 0.6233 | Val mse = 0.7573 Epoch 32: Train mse = 0.6139 | Val mse = 0.7500 Epoch 33: Train mse = 0.5949 | Val mse = 0.7411 Epoch 34: Train mse = 0.5816 | Val mse = 0.7327 Epoch 35: Train mse = 0.5646 | Val mse = 0.7238 Epoch 36: Train mse = 0.5434 | Val mse = 0.7144 Epoch 37: Train mse = 0.5258 | Val mse = 0.7056 Epoch 38: Train mse = 0.5110 | Val mse = 0.6941 Epoch 39: Train mse = 0.4909 | Val mse = 0.6856 Epoch 40: Train mse = 0.4676 | Val mse = 0.6776 Epoch 41: Train mse = 0.4553 | Val mse = 0.6674 Epoch 42: Train mse = 0.4373 | Val mse = 0.6614 Epoch 43: Train mse = 0.4132 | Val mse = 0.6506 Epoch 44: Train mse = 0.3985 | Val mse = 0.6415 Epoch 45: Train mse = 0.3774 | Val mse = 0.6391 Epoch 46: Train mse = 0.3640 | Val mse = 0.6335 Epoch 47: Train mse = 0.3503 | Val mse = 0.6268 Epoch 48: Train mse = 0.3311 | Val mse = 0.6260 Epoch 49: Train mse = 0.3231 | Val mse = 0.6187 Epoch 50: Train mse = 0.3074 | Val mse = 0.6145 Using device: mps Epoch 1: Train mse = 1.0042 | Val mse = 1.2252 Epoch 2: Train mse = 0.9963 | Val mse = 1.2244 Epoch 3: Train mse = 0.9998 | Val mse = 1.2205 Epoch 4: Train mse = 0.9961 | Val mse = 1.2115 Epoch 5: Train mse = 0.9806 | Val mse = 1.1902 Epoch 6: Train mse = 0.9753 | Val mse = 1.1512 Epoch 7: Train mse = 0.9596 | Val mse = 1.0985 Epoch 8: Train mse = 0.9363 | Val mse = 1.0408 Epoch 9: Train mse = 0.9165 | Val mse = 0.9916 Epoch 10: Train mse = 0.9056 | Val mse = 0.9685 Epoch 11: Train mse = 0.8945 | Val mse = 0.9598 Epoch 12: Train mse = 0.8842 | Val mse = 0.9480 Epoch 13: Train mse = 0.8747 | Val mse = 0.9332 Epoch 14: Train mse = 0.8659 | Val mse = 0.9202 Epoch 15: Train mse = 0.8556 | Val mse = 0.9129 Epoch 16: Train mse = 0.8407 | Val mse = 0.9030 Epoch 17: Train mse = 0.8273 | Val mse = 0.8923 Epoch 18: Train mse = 0.8132 | Val mse = 0.8731 Epoch 19: Train mse = 0.7955 | Val mse = 0.8530 Epoch 20: Train mse = 0.7741 | Val mse = 0.8389 Epoch 21: Train mse = 0.7609 | Val mse = 0.8236 Epoch 22: Train mse = 0.7412 | Val mse = 0.8154 Epoch 23: Train mse = 0.7325 | Val mse = 0.8065 Epoch 24: Train mse = 0.7176 | Val mse = 0.8003 Epoch 25: Train mse = 0.7084 | Val mse = 0.7983 Epoch 26: Train mse = 0.6985 | Val mse = 0.7911 Epoch 27: Train mse = 0.6893 | Val mse = 0.7905 Epoch 28: Train mse = 0.6771 | Val mse = 0.7814 Epoch 29: Train mse = 0.6653 | Val mse = 0.7779 Epoch 30: Train mse = 0.6551 | Val mse = 0.7720 Epoch 31: Train mse = 0.6349 | Val mse = 0.7673 Epoch 32: Train mse = 0.6268 | Val mse = 0.7595 Epoch 33: Train mse = 0.6086 | Val mse = 0.7525 Epoch 34: Train mse = 0.5942 | Val mse = 0.7441 Epoch 35: Train mse = 0.5753 | Val mse = 0.7348 Epoch 36: Train mse = 0.5509 | Val mse = 0.7270 Epoch 37: Train mse = 0.5309 | Val mse = 0.7169 Epoch 38: Train mse = 0.5128 | Val mse = 0.7087 Epoch 39: Train mse = 0.4906 | Val mse = 0.6956 Epoch 40: Train mse = 0.4689 | Val mse = 0.6851 Epoch 41: Train mse = 0.4558 | Val mse = 0.6704 Epoch 42: Train mse = 0.4413 | Val mse = 0.6645 Epoch 43: Train mse = 0.4186 | Val mse = 0.6484 Epoch 44: Train mse = 0.4081 | Val mse = 0.6369 Epoch 45: Train mse = 0.3888 | Val mse = 0.6308 Epoch 46: Train mse = 0.3773 | Val mse = 0.6259 Epoch 47: Train mse = 0.3655 | Val mse = 0.6161 Epoch 48: Train mse = 0.3495 | Val mse = 0.6079 Epoch 49: Train mse = 0.3421 | Val mse = 0.6020 Epoch 50: Train mse = 0.3262 | Val mse = 0.5966 Using device: mps Epoch 1: Train mse = 0.9950 | Val mse = 1.1095 Epoch 2: Train mse = 0.9179 | Val mse = 0.9480 Epoch 3: Train mse = 0.8571 | Val mse = 0.8984 Epoch 4: Train mse = 0.7745 | Val mse = 0.7978 Epoch 5: Train mse = 0.7361 | Val mse = 0.8109 Epoch 6: Train mse = 0.6988 | Val mse = 0.7795 Epoch 7: Train mse = 0.6378 | Val mse = 0.7459 Epoch 8: Train mse = 0.5500 | Val mse = 0.6649 Epoch 9: Train mse = 0.4716 | Val mse = 0.5968 Epoch 10: Train mse = 0.4013 | Val mse = 0.5028 Epoch 11: Train mse = 0.3427 | Val mse = 0.4719 Epoch 12: Train mse = 0.3139 | Val mse = 0.4313 Epoch 13: Train mse = 0.2980 | Val mse = 0.4001 Epoch 14: Train mse = 0.2863 | Val mse = 0.4160 Epoch 15: Train mse = 0.2813 | Val mse = 0.3893 Epoch 16: Train mse = 0.2789 | Val mse = 0.4276 Epoch 17: Train mse = 0.2720 | Val mse = 0.3727 Epoch 18: Train mse = 0.2588 | Val mse = 0.3894 Epoch 19: Train mse = 0.2468 | Val mse = 0.3996 Epoch 20: Train mse = 0.2369 | Val mse = 0.3639 Epoch 21: Train mse = 0.2280 | Val mse = 0.3668 Epoch 22: Train mse = 0.2235 | Val mse = 0.3745 Epoch 23: Train mse = 0.2206 | Val mse = 0.3728 Epoch 24: Train mse = 0.2092 | Val mse = 0.3879 Epoch 25: Train mse = 0.2044 | Val mse = 0.3712 Epoch 26: Train mse = 0.1970 | Val mse = 0.3760 Epoch 27: Train mse = 0.1944 | Val mse = 0.3875 Epoch 28: Train mse = 0.1884 | Val mse = 0.3818 Epoch 29: Train mse = 0.1847 | Val mse = 0.3859 Epoch 30: Train mse = 0.1815 | Val mse = 0.3938 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9956 | Val mse = 1.1108 Epoch 2: Train mse = 0.9177 | Val mse = 0.9536 Epoch 3: Train mse = 0.8616 | Val mse = 0.9026 Epoch 4: Train mse = 0.7769 | Val mse = 0.7976 Epoch 5: Train mse = 0.7398 | Val mse = 0.8092 Epoch 6: Train mse = 0.7040 | Val mse = 0.7838 Epoch 7: Train mse = 0.6496 | Val mse = 0.7480 Epoch 8: Train mse = 0.5691 | Val mse = 0.6640 Epoch 9: Train mse = 0.4840 | Val mse = 0.5972 Epoch 10: Train mse = 0.4067 | Val mse = 0.5006 Epoch 11: Train mse = 0.3492 | Val mse = 0.4449 Epoch 12: Train mse = 0.3117 | Val mse = 0.4251 Epoch 13: Train mse = 0.2975 | Val mse = 0.3779 Epoch 14: Train mse = 0.2815 | Val mse = 0.3813 Epoch 15: Train mse = 0.2730 | Val mse = 0.3524 Epoch 16: Train mse = 0.2588 | Val mse = 0.3724 Epoch 17: Train mse = 0.2526 | Val mse = 0.3579 Epoch 18: Train mse = 0.2391 | Val mse = 0.3488 Epoch 19: Train mse = 0.2336 | Val mse = 0.3599 Epoch 20: Train mse = 0.2254 | Val mse = 0.3500 Epoch 21: Train mse = 0.2177 | Val mse = 0.3710 Epoch 22: Train mse = 0.2137 | Val mse = 0.3685 Epoch 23: Train mse = 0.2150 | Val mse = 0.3600 Epoch 24: Train mse = 0.2037 | Val mse = 0.3696 Epoch 25: Train mse = 0.2016 | Val mse = 0.3672 Epoch 26: Train mse = 0.1958 | Val mse = 0.3565 Epoch 27: Train mse = 0.1927 | Val mse = 0.3697 Epoch 28: Train mse = 0.1872 | Val mse = 0.3704 Epoch 29: Train mse = 0.1842 | Val mse = 0.3618 Epoch 30: Train mse = 0.1820 | Val mse = 0.3647 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 1.0012 | Val mse = 1.2192 Epoch 2: Train mse = 0.9915 | Val mse = 1.2008 Epoch 3: Train mse = 0.9809 | Val mse = 1.1677 Epoch 4: Train mse = 0.9543 | Val mse = 1.1097 Epoch 5: Train mse = 0.9311 | Val mse = 1.0348 Epoch 6: Train mse = 0.9117 | Val mse = 0.9799 Epoch 7: Train mse = 0.8989 | Val mse = 0.9519 Epoch 8: Train mse = 0.8723 | Val mse = 0.9308 Epoch 9: Train mse = 0.8520 | Val mse = 0.9020 Epoch 10: Train mse = 0.8311 | Val mse = 0.8659 Epoch 11: Train mse = 0.8073 | Val mse = 0.8298 Epoch 12: Train mse = 0.7741 | Val mse = 0.8058 Epoch 13: Train mse = 0.7509 | Val mse = 0.7848 Epoch 14: Train mse = 0.7236 | Val mse = 0.7598 Epoch 15: Train mse = 0.6911 | Val mse = 0.7319 Epoch 16: Train mse = 0.6645 | Val mse = 0.7028 Epoch 17: Train mse = 0.6289 | Val mse = 0.6764 Epoch 18: Train mse = 0.5988 | Val mse = 0.6532 Epoch 19: Train mse = 0.5767 | Val mse = 0.6284 Epoch 20: Train mse = 0.5462 | Val mse = 0.6065 Epoch 21: Train mse = 0.5171 | Val mse = 0.5840 Epoch 22: Train mse = 0.4943 | Val mse = 0.5574 Epoch 23: Train mse = 0.4714 | Val mse = 0.5333 Epoch 24: Train mse = 0.4479 | Val mse = 0.5113 Epoch 25: Train mse = 0.4203 | Val mse = 0.4867 Epoch 26: Train mse = 0.3972 | Val mse = 0.4677 Epoch 27: Train mse = 0.3841 | Val mse = 0.4524 Epoch 28: Train mse = 0.3665 | Val mse = 0.4327 Epoch 29: Train mse = 0.3554 | Val mse = 0.4291 Epoch 30: Train mse = 0.3412 | Val mse = 0.4162 Epoch 31: Train mse = 0.3336 | Val mse = 0.4051 Epoch 32: Train mse = 0.3238 | Val mse = 0.3997 Epoch 33: Train mse = 0.3186 | Val mse = 0.3926 Epoch 34: Train mse = 0.3071 | Val mse = 0.3869 Epoch 35: Train mse = 0.3041 | Val mse = 0.3833 Epoch 36: Train mse = 0.3010 | Val mse = 0.3786 Epoch 37: Train mse = 0.2977 | Val mse = 0.3742 Epoch 38: Train mse = 0.2974 | Val mse = 0.3750 Epoch 39: Train mse = 0.2875 | Val mse = 0.3695 Epoch 40: Train mse = 0.2842 | Val mse = 0.3676 Epoch 41: Train mse = 0.2801 | Val mse = 0.3625 Epoch 42: Train mse = 0.2764 | Val mse = 0.3630 Epoch 43: Train mse = 0.2724 | Val mse = 0.3622 Epoch 44: Train mse = 0.2729 | Val mse = 0.3590 Epoch 45: Train mse = 0.2688 | Val mse = 0.3579 Epoch 46: Train mse = 0.2670 | Val mse = 0.3553 Epoch 47: Train mse = 0.2587 | Val mse = 0.3541 Epoch 48: Train mse = 0.2588 | Val mse = 0.3546 Epoch 49: Train mse = 0.2568 | Val mse = 0.3517 Epoch 50: Train mse = 0.2547 | Val mse = 0.3513 Using device: mps Epoch 1: Train mse = 1.0013 | Val mse = 1.2192 Epoch 2: Train mse = 0.9919 | Val mse = 1.2011 Epoch 3: Train mse = 0.9816 | Val mse = 1.1690 Epoch 4: Train mse = 0.9554 | Val mse = 1.1122 Epoch 5: Train mse = 0.9324 | Val mse = 1.0380 Epoch 6: Train mse = 0.9131 | Val mse = 0.9835 Epoch 7: Train mse = 0.9006 | Val mse = 0.9562 Epoch 8: Train mse = 0.8745 | Val mse = 0.9351 Epoch 9: Train mse = 0.8553 | Val mse = 0.9059 Epoch 10: Train mse = 0.8357 | Val mse = 0.8695 Epoch 11: Train mse = 0.8132 | Val mse = 0.8335 Epoch 12: Train mse = 0.7807 | Val mse = 0.8088 Epoch 13: Train mse = 0.7580 | Val mse = 0.7873 Epoch 14: Train mse = 0.7307 | Val mse = 0.7622 Epoch 15: Train mse = 0.6985 | Val mse = 0.7347 Epoch 16: Train mse = 0.6727 | Val mse = 0.7060 Epoch 17: Train mse = 0.6380 | Val mse = 0.6804 Epoch 18: Train mse = 0.6084 | Val mse = 0.6583 Epoch 19: Train mse = 0.5868 | Val mse = 0.6342 Epoch 20: Train mse = 0.5569 | Val mse = 0.6124 Epoch 21: Train mse = 0.5284 | Val mse = 0.5898 Epoch 22: Train mse = 0.5064 | Val mse = 0.5629 Epoch 23: Train mse = 0.4836 | Val mse = 0.5386 Epoch 24: Train mse = 0.4604 | Val mse = 0.5162 Epoch 25: Train mse = 0.4323 | Val mse = 0.4911 Epoch 26: Train mse = 0.4087 | Val mse = 0.4714 Epoch 27: Train mse = 0.3950 | Val mse = 0.4550 Epoch 28: Train mse = 0.3765 | Val mse = 0.4344 Epoch 29: Train mse = 0.3647 | Val mse = 0.4301 Epoch 30: Train mse = 0.3492 | Val mse = 0.4163 Epoch 31: Train mse = 0.3408 | Val mse = 0.4042 Epoch 32: Train mse = 0.3302 | Val mse = 0.3981 Epoch 33: Train mse = 0.3243 | Val mse = 0.3905 Epoch 34: Train mse = 0.3124 | Val mse = 0.3846 Epoch 35: Train mse = 0.3094 | Val mse = 0.3808 Epoch 36: Train mse = 0.3062 | Val mse = 0.3759 Epoch 37: Train mse = 0.3030 | Val mse = 0.3708 Epoch 38: Train mse = 0.3028 | Val mse = 0.3717 Epoch 39: Train mse = 0.2928 | Val mse = 0.3658 Epoch 40: Train mse = 0.2897 | Val mse = 0.3639 Epoch 41: Train mse = 0.2856 | Val mse = 0.3588 Epoch 42: Train mse = 0.2818 | Val mse = 0.3591 Epoch 43: Train mse = 0.2778 | Val mse = 0.3579 Epoch 44: Train mse = 0.2788 | Val mse = 0.3547 Epoch 45: Train mse = 0.2746 | Val mse = 0.3536 Epoch 46: Train mse = 0.2731 | Val mse = 0.3505 Epoch 47: Train mse = 0.2647 | Val mse = 0.3489 Epoch 48: Train mse = 0.2650 | Val mse = 0.3497 Epoch 49: Train mse = 0.2630 | Val mse = 0.3465 Epoch 50: Train mse = 0.2610 | Val mse = 0.3457 Using device: mps Epoch 1: Train mse = 0.9624 | Val mse = 0.9216 Epoch 2: Train mse = 0.8226 | Val mse = 0.7913 Epoch 3: Train mse = 0.6864 | Val mse = 0.6668 Epoch 4: Train mse = 0.5513 | Val mse = 0.5875 Epoch 5: Train mse = 0.4435 | Val mse = 0.4673 Epoch 6: Train mse = 0.3682 | Val mse = 0.4035 Epoch 7: Train mse = 0.3283 | Val mse = 0.3683 Epoch 8: Train mse = 0.2967 | Val mse = 0.3531 Epoch 9: Train mse = 0.2806 | Val mse = 0.3451 Epoch 10: Train mse = 0.2651 | Val mse = 0.3320 Epoch 11: Train mse = 0.2535 | Val mse = 0.3478 Epoch 12: Train mse = 0.2438 | Val mse = 0.3488 Epoch 13: Train mse = 0.2361 | Val mse = 0.3471 Epoch 14: Train mse = 0.2245 | Val mse = 0.3472 Epoch 15: Train mse = 0.2205 | Val mse = 0.3639 Epoch 16: Train mse = 0.2127 | Val mse = 0.3621 Epoch 17: Train mse = 0.2042 | Val mse = 0.3720 Epoch 18: Train mse = 0.1988 | Val mse = 0.3727 Epoch 19: Train mse = 0.1915 | Val mse = 0.3981 Epoch 20: Train mse = 0.1870 | Val mse = 0.3898 Epoch 21: Train mse = 0.1797 | Val mse = 0.3911 Epoch 22: Train mse = 0.1775 | Val mse = 0.3903 Epoch 23: Train mse = 0.1728 | Val mse = 0.4091 Epoch 24: Train mse = 0.1679 | Val mse = 0.3931 Epoch 25: Train mse = 0.1596 | Val mse = 0.3979 Epoch 26: Train mse = 0.1528 | Val mse = 0.4080 Epoch 27: Train mse = 0.1516 | Val mse = 0.4086 Epoch 28: Train mse = 0.1458 | Val mse = 0.4118 Epoch 29: Train mse = 0.1426 | Val mse = 0.4090 Epoch 30: Train mse = 0.1375 | Val mse = 0.4209 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9635 | Val mse = 0.9267 Epoch 2: Train mse = 0.8269 | Val mse = 0.7940 Epoch 3: Train mse = 0.6933 | Val mse = 0.6728 Epoch 4: Train mse = 0.5617 | Val mse = 0.5967 Epoch 5: Train mse = 0.4551 | Val mse = 0.4740 Epoch 6: Train mse = 0.3772 | Val mse = 0.4059 Epoch 7: Train mse = 0.3327 | Val mse = 0.3681 Epoch 8: Train mse = 0.3015 | Val mse = 0.3503 Epoch 9: Train mse = 0.2846 | Val mse = 0.3389 Epoch 10: Train mse = 0.2689 | Val mse = 0.3303 Epoch 11: Train mse = 0.2582 | Val mse = 0.3465 Epoch 12: Train mse = 0.2497 | Val mse = 0.3487 Epoch 13: Train mse = 0.2419 | Val mse = 0.3415 Epoch 14: Train mse = 0.2303 | Val mse = 0.3462 Epoch 15: Train mse = 0.2262 | Val mse = 0.3574 Epoch 16: Train mse = 0.2181 | Val mse = 0.3568 Epoch 17: Train mse = 0.2104 | Val mse = 0.3621 Epoch 18: Train mse = 0.2043 | Val mse = 0.3706 Epoch 19: Train mse = 0.1987 | Val mse = 0.3904 Epoch 20: Train mse = 0.1937 | Val mse = 0.3830 Epoch 21: Train mse = 0.1874 | Val mse = 0.3913 Epoch 22: Train mse = 0.1855 | Val mse = 0.3925 Epoch 23: Train mse = 0.1817 | Val mse = 0.4017 Epoch 24: Train mse = 0.1766 | Val mse = 0.3922 Epoch 25: Train mse = 0.1690 | Val mse = 0.3979 Epoch 26: Train mse = 0.1624 | Val mse = 0.4017 Epoch 27: Train mse = 0.1608 | Val mse = 0.4134 Epoch 28: Train mse = 0.1554 | Val mse = 0.4079 Epoch 29: Train mse = 0.1520 | Val mse = 0.4076 Epoch 30: Train mse = 0.1465 | Val mse = 0.4128 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9927 | Val mse = 1.2175 Epoch 2: Train mse = 0.9853 | Val mse = 1.1984 Epoch 3: Train mse = 0.9728 | Val mse = 1.1593 Epoch 4: Train mse = 0.9517 | Val mse = 1.0927 Epoch 5: Train mse = 0.9245 | Val mse = 1.0070 Epoch 6: Train mse = 0.9067 | Val mse = 0.9420 Epoch 7: Train mse = 0.8813 | Val mse = 0.9190 Epoch 8: Train mse = 0.8721 | Val mse = 0.9085 Epoch 9: Train mse = 0.8464 | Val mse = 0.8949 Epoch 10: Train mse = 0.8268 | Val mse = 0.8656 Epoch 11: Train mse = 0.7954 | Val mse = 0.8289 Epoch 12: Train mse = 0.7659 | Val mse = 0.7990 Epoch 13: Train mse = 0.7397 | Val mse = 0.7745 Epoch 14: Train mse = 0.7038 | Val mse = 0.7476 Epoch 15: Train mse = 0.6773 | Val mse = 0.7154 Epoch 16: Train mse = 0.6356 | Val mse = 0.6836 Epoch 17: Train mse = 0.6061 | Val mse = 0.6580 Epoch 18: Train mse = 0.5701 | Val mse = 0.6354 Epoch 19: Train mse = 0.5346 | Val mse = 0.6117 Epoch 20: Train mse = 0.5109 | Val mse = 0.5900 Epoch 21: Train mse = 0.4777 | Val mse = 0.5686 Epoch 22: Train mse = 0.4483 | Val mse = 0.5440 Epoch 23: Train mse = 0.4212 | Val mse = 0.5215 Epoch 24: Train mse = 0.3998 | Val mse = 0.5005 Epoch 25: Train mse = 0.3796 | Val mse = 0.4770 Epoch 26: Train mse = 0.3566 | Val mse = 0.4631 Epoch 27: Train mse = 0.3337 | Val mse = 0.4474 Epoch 28: Train mse = 0.3243 | Val mse = 0.4340 Epoch 29: Train mse = 0.3114 | Val mse = 0.4256 Epoch 30: Train mse = 0.3009 | Val mse = 0.4163 Epoch 31: Train mse = 0.2945 | Val mse = 0.4088 Epoch 32: Train mse = 0.2881 | Val mse = 0.4055 Epoch 33: Train mse = 0.2804 | Val mse = 0.4003 Epoch 34: Train mse = 0.2731 | Val mse = 0.3961 Epoch 35: Train mse = 0.2642 | Val mse = 0.3943 Epoch 36: Train mse = 0.2630 | Val mse = 0.3904 Epoch 37: Train mse = 0.2548 | Val mse = 0.3873 Epoch 38: Train mse = 0.2498 | Val mse = 0.3855 Epoch 39: Train mse = 0.2457 | Val mse = 0.3845 Epoch 40: Train mse = 0.2415 | Val mse = 0.3849 Epoch 41: Train mse = 0.2325 | Val mse = 0.3824 Epoch 42: Train mse = 0.2311 | Val mse = 0.3796 Epoch 43: Train mse = 0.2273 | Val mse = 0.3806 Epoch 44: Train mse = 0.2250 | Val mse = 0.3792 Epoch 45: Train mse = 0.2201 | Val mse = 0.3800 Epoch 46: Train mse = 0.2144 | Val mse = 0.3809 Epoch 47: Train mse = 0.2141 | Val mse = 0.3792 Epoch 48: Train mse = 0.2117 | Val mse = 0.3819 Epoch 49: Train mse = 0.2047 | Val mse = 0.3814 Epoch 50: Train mse = 0.2035 | Val mse = 0.3813 Using device: mps Epoch 1: Train mse = 0.9928 | Val mse = 1.2177 Epoch 2: Train mse = 0.9858 | Val mse = 1.1994 Epoch 3: Train mse = 0.9735 | Val mse = 1.1614 Epoch 4: Train mse = 0.9526 | Val mse = 1.0952 Epoch 5: Train mse = 0.9252 | Val mse = 1.0084 Epoch 6: Train mse = 0.9074 | Val mse = 0.9429 Epoch 7: Train mse = 0.8820 | Val mse = 0.9205 Epoch 8: Train mse = 0.8731 | Val mse = 0.9103 Epoch 9: Train mse = 0.8479 | Val mse = 0.8962 Epoch 10: Train mse = 0.8285 | Val mse = 0.8662 Epoch 11: Train mse = 0.7972 | Val mse = 0.8291 Epoch 12: Train mse = 0.7676 | Val mse = 0.7995 Epoch 13: Train mse = 0.7418 | Val mse = 0.7753 Epoch 14: Train mse = 0.7064 | Val mse = 0.7482 Epoch 15: Train mse = 0.6801 | Val mse = 0.7157 Epoch 16: Train mse = 0.6381 | Val mse = 0.6835 Epoch 17: Train mse = 0.6084 | Val mse = 0.6577 Epoch 18: Train mse = 0.5729 | Val mse = 0.6351 Epoch 19: Train mse = 0.5382 | Val mse = 0.6112 Epoch 20: Train mse = 0.5149 | Val mse = 0.5894 Epoch 21: Train mse = 0.4824 | Val mse = 0.5680 Epoch 22: Train mse = 0.4530 | Val mse = 0.5429 Epoch 23: Train mse = 0.4264 | Val mse = 0.5196 Epoch 24: Train mse = 0.4050 | Val mse = 0.4983 Epoch 25: Train mse = 0.3849 | Val mse = 0.4731 Epoch 26: Train mse = 0.3620 | Val mse = 0.4588 Epoch 27: Train mse = 0.3388 | Val mse = 0.4433 Epoch 28: Train mse = 0.3298 | Val mse = 0.4291 Epoch 29: Train mse = 0.3172 | Val mse = 0.4208 Epoch 30: Train mse = 0.3069 | Val mse = 0.4119 Epoch 31: Train mse = 0.3008 | Val mse = 0.4042 Epoch 32: Train mse = 0.2949 | Val mse = 0.4008 Epoch 33: Train mse = 0.2876 | Val mse = 0.3960 Epoch 34: Train mse = 0.2806 | Val mse = 0.3913 Epoch 35: Train mse = 0.2719 | Val mse = 0.3898 Epoch 36: Train mse = 0.2712 | Val mse = 0.3846 Epoch 37: Train mse = 0.2630 | Val mse = 0.3820 Epoch 38: Train mse = 0.2583 | Val mse = 0.3782 Epoch 39: Train mse = 0.2550 | Val mse = 0.3776 Epoch 40: Train mse = 0.2510 | Val mse = 0.3760 Epoch 41: Train mse = 0.2418 | Val mse = 0.3760 Epoch 42: Train mse = 0.2410 | Val mse = 0.3704 Epoch 43: Train mse = 0.2370 | Val mse = 0.3719 Epoch 44: Train mse = 0.2354 | Val mse = 0.3686 Epoch 45: Train mse = 0.2304 | Val mse = 0.3700 Epoch 46: Train mse = 0.2239 | Val mse = 0.3703 Epoch 47: Train mse = 0.2241 | Val mse = 0.3679 Epoch 48: Train mse = 0.2212 | Val mse = 0.3709 Epoch 49: Train mse = 0.2137 | Val mse = 0.3697 Epoch 50: Train mse = 0.2128 | Val mse = 0.3706 Using device: mps Epoch 1: Train mse = 0.9518 | Val mse = 0.8928 Epoch 2: Train mse = 0.8219 | Val mse = 0.8095 Epoch 3: Train mse = 0.6658 | Val mse = 0.6459 Epoch 4: Train mse = 0.5235 | Val mse = 0.5363 Epoch 5: Train mse = 0.4001 | Val mse = 0.4471 Epoch 6: Train mse = 0.3464 | Val mse = 0.3974 Epoch 7: Train mse = 0.3090 | Val mse = 0.3873 Epoch 8: Train mse = 0.2810 | Val mse = 0.3796 Epoch 9: Train mse = 0.2581 | Val mse = 0.3562 Epoch 10: Train mse = 0.2409 | Val mse = 0.3584 Epoch 11: Train mse = 0.2247 | Val mse = 0.3580 Epoch 12: Train mse = 0.2095 | Val mse = 0.3567 Epoch 13: Train mse = 0.1999 | Val mse = 0.3575 Epoch 14: Train mse = 0.1879 | Val mse = 0.3655 Epoch 15: Train mse = 0.1823 | Val mse = 0.3669 Epoch 16: Train mse = 0.1724 | Val mse = 0.3635 Epoch 17: Train mse = 0.1655 | Val mse = 0.3692 Epoch 18: Train mse = 0.1600 | Val mse = 0.3770 Epoch 19: Train mse = 0.1515 | Val mse = 0.3797 Epoch 20: Train mse = 0.1445 | Val mse = 0.3762 Epoch 21: Train mse = 0.1377 | Val mse = 0.3821 Epoch 22: Train mse = 0.1289 | Val mse = 0.3875 Epoch 23: Train mse = 0.1233 | Val mse = 0.3852 Epoch 24: Train mse = 0.1176 | Val mse = 0.3922 Epoch 25: Train mse = 0.1135 | Val mse = 0.3986 Epoch 26: Train mse = 0.1104 | Val mse = 0.4166 Epoch 27: Train mse = 0.1077 | Val mse = 0.4142 Epoch 28: Train mse = 0.1048 | Val mse = 0.4169 Epoch 29: Train mse = 0.1047 | Val mse = 0.4146 Epoch 30: Train mse = 0.1021 | Val mse = 0.4268 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9527 | Val mse = 0.8959 Epoch 2: Train mse = 0.8236 | Val mse = 0.8103 Epoch 3: Train mse = 0.6698 | Val mse = 0.6513 Epoch 4: Train mse = 0.5301 | Val mse = 0.5418 Epoch 5: Train mse = 0.4072 | Val mse = 0.4557 Epoch 6: Train mse = 0.3505 | Val mse = 0.3962 Epoch 7: Train mse = 0.3143 | Val mse = 0.3836 Epoch 8: Train mse = 0.2853 | Val mse = 0.3745 Epoch 9: Train mse = 0.2642 | Val mse = 0.3488 Epoch 10: Train mse = 0.2475 | Val mse = 0.3508 Epoch 11: Train mse = 0.2316 | Val mse = 0.3517 Epoch 12: Train mse = 0.2157 | Val mse = 0.3501 Epoch 13: Train mse = 0.2059 | Val mse = 0.3485 Epoch 14: Train mse = 0.1938 | Val mse = 0.3538 Epoch 15: Train mse = 0.1876 | Val mse = 0.3550 Epoch 16: Train mse = 0.1767 | Val mse = 0.3675 Epoch 17: Train mse = 0.1697 | Val mse = 0.3643 Epoch 18: Train mse = 0.1641 | Val mse = 0.3729 Epoch 19: Train mse = 0.1561 | Val mse = 0.3754 Epoch 20: Train mse = 0.1503 | Val mse = 0.3681 Epoch 21: Train mse = 0.1439 | Val mse = 0.3887 Epoch 22: Train mse = 0.1366 | Val mse = 0.3891 Epoch 23: Train mse = 0.1323 | Val mse = 0.3830 Epoch 24: Train mse = 0.1244 | Val mse = 0.4009 Epoch 25: Train mse = 0.1196 | Val mse = 0.3922 Epoch 26: Train mse = 0.1140 | Val mse = 0.4062 Epoch 27: Train mse = 0.1095 | Val mse = 0.4115 Epoch 28: Train mse = 0.1053 | Val mse = 0.4065 Epoch 29: Train mse = 0.1031 | Val mse = 0.4101 Epoch 30: Train mse = 0.0992 | Val mse = 0.4156 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9936 | Val mse = 1.2283 Epoch 2: Train mse = 0.9982 | Val mse = 1.2186 Epoch 3: Train mse = 0.9889 | Val mse = 1.1945 Epoch 4: Train mse = 0.9754 | Val mse = 1.1352 Epoch 5: Train mse = 0.9427 | Val mse = 1.0310 Epoch 6: Train mse = 0.9151 | Val mse = 0.9591 Epoch 7: Train mse = 0.8997 | Val mse = 0.9516 Epoch 8: Train mse = 0.8907 | Val mse = 0.9536 Epoch 9: Train mse = 0.8797 | Val mse = 0.9493 Epoch 10: Train mse = 0.8693 | Val mse = 0.9431 Epoch 11: Train mse = 0.8649 | Val mse = 0.9383 Epoch 12: Train mse = 0.8560 | Val mse = 0.9285 Epoch 13: Train mse = 0.8502 | Val mse = 0.9227 Epoch 14: Train mse = 0.8482 | Val mse = 0.9166 Epoch 15: Train mse = 0.8393 | Val mse = 0.9012 Epoch 16: Train mse = 0.8234 | Val mse = 0.8818 Epoch 17: Train mse = 0.7981 | Val mse = 0.8554 Epoch 18: Train mse = 0.7651 | Val mse = 0.8176 Epoch 19: Train mse = 0.7409 | Val mse = 0.7946 Epoch 20: Train mse = 0.7263 | Val mse = 0.7859 Epoch 21: Train mse = 0.7165 | Val mse = 0.7875 Epoch 22: Train mse = 0.6963 | Val mse = 0.7774 Epoch 23: Train mse = 0.6756 | Val mse = 0.7477 Epoch 24: Train mse = 0.6490 | Val mse = 0.7153 Epoch 25: Train mse = 0.6175 | Val mse = 0.6792 Epoch 26: Train mse = 0.5809 | Val mse = 0.6465 Epoch 27: Train mse = 0.5449 | Val mse = 0.6219 Epoch 28: Train mse = 0.5169 | Val mse = 0.6003 Epoch 29: Train mse = 0.4917 | Val mse = 0.5828 Epoch 30: Train mse = 0.4577 | Val mse = 0.5568 Epoch 31: Train mse = 0.4279 | Val mse = 0.5254 Epoch 32: Train mse = 0.4009 | Val mse = 0.4989 Epoch 33: Train mse = 0.3717 | Val mse = 0.4672 Epoch 34: Train mse = 0.3540 | Val mse = 0.4575 Epoch 35: Train mse = 0.3344 | Val mse = 0.4315 Epoch 36: Train mse = 0.3226 | Val mse = 0.4204 Epoch 37: Train mse = 0.3081 | Val mse = 0.4034 Epoch 38: Train mse = 0.3005 | Val mse = 0.3943 Epoch 39: Train mse = 0.2926 | Val mse = 0.3988 Epoch 40: Train mse = 0.2845 | Val mse = 0.3819 Epoch 41: Train mse = 0.2802 | Val mse = 0.3831 Epoch 42: Train mse = 0.2788 | Val mse = 0.3785 Epoch 43: Train mse = 0.2714 | Val mse = 0.3722 Epoch 44: Train mse = 0.2695 | Val mse = 0.3635 Epoch 45: Train mse = 0.2645 | Val mse = 0.3684 Epoch 46: Train mse = 0.2607 | Val mse = 0.3603 Epoch 47: Train mse = 0.2571 | Val mse = 0.3600 Epoch 48: Train mse = 0.2550 | Val mse = 0.3570 Epoch 49: Train mse = 0.2540 | Val mse = 0.3572 Epoch 50: Train mse = 0.2472 | Val mse = 0.3603 Using device: mps Epoch 1: Train mse = 0.9936 | Val mse = 1.2284 Epoch 2: Train mse = 0.9983 | Val mse = 1.2189 Epoch 3: Train mse = 0.9895 | Val mse = 1.1963 Epoch 4: Train mse = 0.9770 | Val mse = 1.1401 Epoch 5: Train mse = 0.9448 | Val mse = 1.0356 Epoch 6: Train mse = 0.9163 | Val mse = 0.9591 Epoch 7: Train mse = 0.9005 | Val mse = 0.9510 Epoch 8: Train mse = 0.8914 | Val mse = 0.9532 Epoch 9: Train mse = 0.8803 | Val mse = 0.9492 Epoch 10: Train mse = 0.8706 | Val mse = 0.9438 Epoch 11: Train mse = 0.8668 | Val mse = 0.9401 Epoch 12: Train mse = 0.8581 | Val mse = 0.9306 Epoch 13: Train mse = 0.8524 | Val mse = 0.9236 Epoch 14: Train mse = 0.8509 | Val mse = 0.9184 Epoch 15: Train mse = 0.8434 | Val mse = 0.9049 Epoch 16: Train mse = 0.8302 | Val mse = 0.8874 Epoch 17: Train mse = 0.8087 | Val mse = 0.8638 Epoch 18: Train mse = 0.7778 | Val mse = 0.8264 Epoch 19: Train mse = 0.7495 | Val mse = 0.7995 Epoch 20: Train mse = 0.7328 | Val mse = 0.7842 Epoch 21: Train mse = 0.7235 | Val mse = 0.7859 Epoch 22: Train mse = 0.7047 | Val mse = 0.7813 Epoch 23: Train mse = 0.6861 | Val mse = 0.7559 Epoch 24: Train mse = 0.6630 | Val mse = 0.7253 Epoch 25: Train mse = 0.6345 | Val mse = 0.6896 Epoch 26: Train mse = 0.5969 | Val mse = 0.6537 Epoch 27: Train mse = 0.5600 | Val mse = 0.6282 Epoch 28: Train mse = 0.5326 | Val mse = 0.6032 Epoch 29: Train mse = 0.5072 | Val mse = 0.5868 Epoch 30: Train mse = 0.4738 | Val mse = 0.5629 Epoch 31: Train mse = 0.4442 | Val mse = 0.5332 Epoch 32: Train mse = 0.4167 | Val mse = 0.5046 Epoch 33: Train mse = 0.3864 | Val mse = 0.4729 Epoch 34: Train mse = 0.3674 | Val mse = 0.4603 Epoch 35: Train mse = 0.3462 | Val mse = 0.4349 Epoch 36: Train mse = 0.3328 | Val mse = 0.4209 Epoch 37: Train mse = 0.3175 | Val mse = 0.4047 Epoch 38: Train mse = 0.3085 | Val mse = 0.3924 Epoch 39: Train mse = 0.3003 | Val mse = 0.3966 Epoch 40: Train mse = 0.2920 | Val mse = 0.3796 Epoch 41: Train mse = 0.2872 | Val mse = 0.3804 Epoch 42: Train mse = 0.2859 | Val mse = 0.3769 Epoch 43: Train mse = 0.2780 | Val mse = 0.3703 Epoch 44: Train mse = 0.2763 | Val mse = 0.3609 Epoch 45: Train mse = 0.2709 | Val mse = 0.3672 Epoch 46: Train mse = 0.2672 | Val mse = 0.3560 Epoch 47: Train mse = 0.2637 | Val mse = 0.3586 Epoch 48: Train mse = 0.2614 | Val mse = 0.3530 Epoch 49: Train mse = 0.2609 | Val mse = 0.3527 Epoch 50: Train mse = 0.2539 | Val mse = 0.3566 Using device: mps Epoch 1: Train mse = 0.9670 | Val mse = 0.9504 Epoch 2: Train mse = 0.8856 | Val mse = 0.9804 Epoch 3: Train mse = 0.8273 | Val mse = 0.8402 Epoch 4: Train mse = 0.7315 | Val mse = 0.7376 Epoch 5: Train mse = 0.6258 | Val mse = 0.6752 Epoch 6: Train mse = 0.5374 | Val mse = 0.5965 Epoch 7: Train mse = 0.4317 | Val mse = 0.4662 Epoch 8: Train mse = 0.3520 | Val mse = 0.4094 Epoch 9: Train mse = 0.3169 | Val mse = 0.3744 Epoch 10: Train mse = 0.2972 | Val mse = 0.3555 Epoch 11: Train mse = 0.2862 | Val mse = 0.3700 Epoch 12: Train mse = 0.2736 | Val mse = 0.3342 Epoch 13: Train mse = 0.2781 | Val mse = 0.3442 Epoch 14: Train mse = 0.2704 | Val mse = 0.3453 Epoch 15: Train mse = 0.2632 | Val mse = 0.3937 Epoch 16: Train mse = 0.2617 | Val mse = 0.3356 Epoch 17: Train mse = 0.2506 | Val mse = 0.3289 Epoch 18: Train mse = 0.2408 | Val mse = 0.3314 Epoch 19: Train mse = 0.2354 | Val mse = 0.3346 Epoch 20: Train mse = 0.2262 | Val mse = 0.3326 Epoch 21: Train mse = 0.2250 | Val mse = 0.3349 Epoch 22: Train mse = 0.2159 | Val mse = 0.3452 Epoch 23: Train mse = 0.2158 | Val mse = 0.3500 Epoch 24: Train mse = 0.2106 | Val mse = 0.3347 Epoch 25: Train mse = 0.2072 | Val mse = 0.3418 Epoch 26: Train mse = 0.2035 | Val mse = 0.3506 Epoch 27: Train mse = 0.2000 | Val mse = 0.3450 Epoch 28: Train mse = 0.1964 | Val mse = 0.3603 Epoch 29: Train mse = 0.1986 | Val mse = 0.3568 Epoch 30: Train mse = 0.1941 | Val mse = 0.3545 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9676 | Val mse = 0.9500 Epoch 2: Train mse = 0.8866 | Val mse = 0.9816 Epoch 3: Train mse = 0.8365 | Val mse = 0.8529 Epoch 4: Train mse = 0.7412 | Val mse = 0.7380 Epoch 5: Train mse = 0.6355 | Val mse = 0.6927 Epoch 6: Train mse = 0.5522 | Val mse = 0.6008 Epoch 7: Train mse = 0.4499 | Val mse = 0.4806 Epoch 8: Train mse = 0.3642 | Val mse = 0.4055 Epoch 9: Train mse = 0.3225 | Val mse = 0.3773 Epoch 10: Train mse = 0.3009 | Val mse = 0.3503 Epoch 11: Train mse = 0.2881 | Val mse = 0.3576 Epoch 12: Train mse = 0.2728 | Val mse = 0.3294 Epoch 13: Train mse = 0.2702 | Val mse = 0.3282 Epoch 14: Train mse = 0.2672 | Val mse = 0.3190 Epoch 15: Train mse = 0.2581 | Val mse = 0.3256 Epoch 16: Train mse = 0.2538 | Val mse = 0.3569 Epoch 17: Train mse = 0.2493 | Val mse = 0.3440 Epoch 18: Train mse = 0.2412 | Val mse = 0.3414 Epoch 19: Train mse = 0.2361 | Val mse = 0.3409 Epoch 20: Train mse = 0.2303 | Val mse = 0.3432 Epoch 21: Train mse = 0.2279 | Val mse = 0.3335 Epoch 22: Train mse = 0.2188 | Val mse = 0.3507 Epoch 23: Train mse = 0.2175 | Val mse = 0.3614 Epoch 24: Train mse = 0.2126 | Val mse = 0.3556 Epoch 25: Train mse = 0.2101 | Val mse = 0.3473 Epoch 26: Train mse = 0.2069 | Val mse = 0.3534 Epoch 27: Train mse = 0.2037 | Val mse = 0.3591 Epoch 28: Train mse = 0.2023 | Val mse = 0.3616 Epoch 29: Train mse = 0.2032 | Val mse = 0.3522 Epoch 30: Train mse = 0.1963 | Val mse = 0.3619 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 1.0033 | Val mse = 1.2245 Epoch 2: Train mse = 1.0001 | Val mse = 1.2186 Epoch 3: Train mse = 0.9911 | Val mse = 1.1926 Epoch 4: Train mse = 0.9720 | Val mse = 1.1295 Epoch 5: Train mse = 0.9411 | Val mse = 1.0364 Epoch 6: Train mse = 0.9114 | Val mse = 0.9608 Epoch 7: Train mse = 0.9069 | Val mse = 0.9505 Epoch 8: Train mse = 0.8905 | Val mse = 0.9586 Epoch 9: Train mse = 0.8784 | Val mse = 0.9482 Epoch 10: Train mse = 0.8633 | Val mse = 0.9327 Epoch 11: Train mse = 0.8556 | Val mse = 0.9191 Epoch 12: Train mse = 0.8461 | Val mse = 0.9150 Epoch 13: Train mse = 0.8450 | Val mse = 0.9116 Epoch 14: Train mse = 0.8342 | Val mse = 0.9002 Epoch 15: Train mse = 0.8226 | Val mse = 0.8909 Epoch 16: Train mse = 0.7979 | Val mse = 0.8656 Epoch 17: Train mse = 0.7730 | Val mse = 0.8392 Epoch 18: Train mse = 0.7467 | Val mse = 0.8092 Epoch 19: Train mse = 0.7309 | Val mse = 0.7957 Epoch 20: Train mse = 0.7147 | Val mse = 0.7918 Epoch 21: Train mse = 0.6991 | Val mse = 0.7904 Epoch 22: Train mse = 0.6871 | Val mse = 0.7808 Epoch 23: Train mse = 0.6699 | Val mse = 0.7743 Epoch 24: Train mse = 0.6550 | Val mse = 0.7533 Epoch 25: Train mse = 0.6332 | Val mse = 0.7451 Epoch 26: Train mse = 0.6106 | Val mse = 0.7278 Epoch 27: Train mse = 0.5810 | Val mse = 0.7117 Epoch 28: Train mse = 0.5530 | Val mse = 0.6824 Epoch 29: Train mse = 0.5215 | Val mse = 0.6589 Epoch 30: Train mse = 0.4873 | Val mse = 0.6348 Epoch 31: Train mse = 0.4520 | Val mse = 0.6055 Epoch 32: Train mse = 0.4212 | Val mse = 0.5853 Epoch 33: Train mse = 0.3878 | Val mse = 0.5761 Epoch 34: Train mse = 0.3566 | Val mse = 0.5528 Epoch 35: Train mse = 0.3334 | Val mse = 0.5504 Epoch 36: Train mse = 0.3125 | Val mse = 0.5280 Epoch 37: Train mse = 0.2865 | Val mse = 0.5215 Epoch 38: Train mse = 0.2748 | Val mse = 0.5137 Epoch 39: Train mse = 0.2619 | Val mse = 0.5075 Epoch 40: Train mse = 0.2507 | Val mse = 0.5108 Epoch 41: Train mse = 0.2457 | Val mse = 0.5018 Epoch 42: Train mse = 0.2384 | Val mse = 0.4963 Epoch 43: Train mse = 0.2308 | Val mse = 0.4920 Epoch 44: Train mse = 0.2296 | Val mse = 0.5042 Epoch 45: Train mse = 0.2256 | Val mse = 0.4863 Epoch 46: Train mse = 0.2195 | Val mse = 0.4986 Epoch 47: Train mse = 0.2204 | Val mse = 0.4998 Epoch 48: Train mse = 0.2155 | Val mse = 0.4941 Epoch 49: Train mse = 0.2126 | Val mse = 0.5091 Epoch 50: Train mse = 0.2079 | Val mse = 0.5001 Using device: mps Epoch 1: Train mse = 1.0033 | Val mse = 1.2244 Epoch 2: Train mse = 1.0003 | Val mse = 1.2193 Epoch 3: Train mse = 0.9920 | Val mse = 1.1956 Epoch 4: Train mse = 0.9736 | Val mse = 1.1342 Epoch 5: Train mse = 0.9423 | Val mse = 1.0371 Epoch 6: Train mse = 0.9113 | Val mse = 0.9600 Epoch 7: Train mse = 0.9063 | Val mse = 0.9501 Epoch 8: Train mse = 0.8904 | Val mse = 0.9592 Epoch 9: Train mse = 0.8790 | Val mse = 0.9504 Epoch 10: Train mse = 0.8647 | Val mse = 0.9349 Epoch 11: Train mse = 0.8569 | Val mse = 0.9219 Epoch 12: Train mse = 0.8478 | Val mse = 0.9165 Epoch 13: Train mse = 0.8476 | Val mse = 0.9141 Epoch 14: Train mse = 0.8388 | Val mse = 0.9038 Epoch 15: Train mse = 0.8301 | Val mse = 0.8970 Epoch 16: Train mse = 0.8081 | Val mse = 0.8751 Epoch 17: Train mse = 0.7848 | Val mse = 0.8487 Epoch 18: Train mse = 0.7569 | Val mse = 0.8147 Epoch 19: Train mse = 0.7379 | Val mse = 0.7973 Epoch 20: Train mse = 0.7223 | Val mse = 0.7912 Epoch 21: Train mse = 0.7071 | Val mse = 0.7887 Epoch 22: Train mse = 0.6956 | Val mse = 0.7815 Epoch 23: Train mse = 0.6797 | Val mse = 0.7770 Epoch 24: Train mse = 0.6668 | Val mse = 0.7570 Epoch 25: Train mse = 0.6470 | Val mse = 0.7460 Epoch 26: Train mse = 0.6262 | Val mse = 0.7329 Epoch 27: Train mse = 0.5985 | Val mse = 0.7141 Epoch 28: Train mse = 0.5729 | Val mse = 0.6868 Epoch 29: Train mse = 0.5432 | Val mse = 0.6658 Epoch 30: Train mse = 0.5100 | Val mse = 0.6379 Epoch 31: Train mse = 0.4768 | Val mse = 0.6051 Epoch 32: Train mse = 0.4457 | Val mse = 0.5848 Epoch 33: Train mse = 0.4121 | Val mse = 0.5727 Epoch 34: Train mse = 0.3792 | Val mse = 0.5470 Epoch 35: Train mse = 0.3537 | Val mse = 0.5294 Epoch 36: Train mse = 0.3312 | Val mse = 0.5122 Epoch 37: Train mse = 0.3027 | Val mse = 0.4972 Epoch 38: Train mse = 0.2904 | Val mse = 0.4954 Epoch 39: Train mse = 0.2761 | Val mse = 0.4866 Epoch 40: Train mse = 0.2632 | Val mse = 0.4830 Epoch 41: Train mse = 0.2574 | Val mse = 0.4780 Epoch 42: Train mse = 0.2498 | Val mse = 0.4685 Epoch 43: Train mse = 0.2410 | Val mse = 0.4668 Epoch 44: Train mse = 0.2402 | Val mse = 0.4709 Epoch 45: Train mse = 0.2348 | Val mse = 0.4562 Epoch 46: Train mse = 0.2279 | Val mse = 0.4719 Epoch 47: Train mse = 0.2297 | Val mse = 0.4691 Epoch 48: Train mse = 0.2233 | Val mse = 0.4762 Epoch 49: Train mse = 0.2210 | Val mse = 0.4880 Epoch 50: Train mse = 0.2165 | Val mse = 0.4765 Using device: mps Epoch 1: Train mse = 0.9744 | Val mse = 0.9415 Epoch 2: Train mse = 0.8960 | Val mse = 0.9540 Epoch 3: Train mse = 0.8040 | Val mse = 0.8227 Epoch 4: Train mse = 0.7461 | Val mse = 0.8008 Epoch 5: Train mse = 0.6854 | Val mse = 0.7477 Epoch 6: Train mse = 0.5741 | Val mse = 0.5974 Epoch 7: Train mse = 0.4586 | Val mse = 0.5259 Epoch 8: Train mse = 0.3606 | Val mse = 0.4601 Epoch 9: Train mse = 0.3157 | Val mse = 0.4407 Epoch 10: Train mse = 0.2881 | Val mse = 0.3841 Epoch 11: Train mse = 0.2652 | Val mse = 0.3525 Epoch 12: Train mse = 0.2496 | Val mse = 0.3580 Epoch 13: Train mse = 0.2363 | Val mse = 0.3488 Epoch 14: Train mse = 0.2277 | Val mse = 0.3655 Epoch 15: Train mse = 0.2225 | Val mse = 0.3476 Epoch 16: Train mse = 0.2131 | Val mse = 0.3632 Epoch 17: Train mse = 0.2131 | Val mse = 0.3720 Epoch 18: Train mse = 0.2027 | Val mse = 0.3564 Epoch 19: Train mse = 0.2025 | Val mse = 0.3445 Epoch 20: Train mse = 0.1968 | Val mse = 0.3738 Epoch 21: Train mse = 0.1940 | Val mse = 0.3512 Epoch 22: Train mse = 0.1878 | Val mse = 0.3772 Epoch 23: Train mse = 0.1844 | Val mse = 0.3476 Epoch 24: Train mse = 0.1806 | Val mse = 0.3530 Epoch 25: Train mse = 0.1743 | Val mse = 0.3503 Epoch 26: Train mse = 0.1704 | Val mse = 0.3619 Epoch 27: Train mse = 0.1668 | Val mse = 0.3648 Epoch 28: Train mse = 0.1624 | Val mse = 0.3706 Epoch 29: Train mse = 0.1589 | Val mse = 0.3756 Epoch 30: Train mse = 0.1556 | Val mse = 0.3746 Early stopping triggered at epoch 30. Using device: mps Epoch 1: Train mse = 0.9748 | Val mse = 0.9430 Epoch 2: Train mse = 0.8970 | Val mse = 0.9633 Epoch 3: Train mse = 0.8126 | Val mse = 0.8296 Epoch 4: Train mse = 0.7504 | Val mse = 0.7954 Epoch 5: Train mse = 0.6923 | Val mse = 0.7529 Epoch 6: Train mse = 0.5845 | Val mse = 0.6024 Epoch 7: Train mse = 0.4690 | Val mse = 0.5276 Epoch 8: Train mse = 0.3682 | Val mse = 0.4392 Epoch 9: Train mse = 0.3117 | Val mse = 0.4288 Epoch 10: Train mse = 0.2876 | Val mse = 0.3671 Epoch 11: Train mse = 0.2697 | Val mse = 0.3527 Epoch 12: Train mse = 0.2562 | Val mse = 0.3475 Epoch 13: Train mse = 0.2455 | Val mse = 0.3311 Epoch 14: Train mse = 0.2300 | Val mse = 0.3483 Epoch 15: Train mse = 0.2246 | Val mse = 0.3490 Epoch 16: Train mse = 0.2161 | Val mse = 0.3360 Epoch 17: Train mse = 0.2100 | Val mse = 0.3403 Epoch 18: Train mse = 0.2032 | Val mse = 0.3571 Epoch 19: Train mse = 0.2025 | Val mse = 0.3507 Epoch 20: Train mse = 0.1935 | Val mse = 0.3507 Epoch 21: Train mse = 0.1871 | Val mse = 0.3474 Epoch 22: Train mse = 0.1806 | Val mse = 0.3596 Epoch 23: Train mse = 0.1781 | Val mse = 0.3523 Epoch 24: Train mse = 0.1761 | Val mse = 0.3617 Epoch 25: Train mse = 0.1709 | Val mse = 0.3591 Epoch 26: Train mse = 0.1674 | Val mse = 0.3667 Epoch 27: Train mse = 0.1642 | Val mse = 0.3626 Epoch 28: Train mse = 0.1603 | Val mse = 0.3714 Epoch 29: Train mse = 0.1582 | Val mse = 0.3668 Epoch 30: Train mse = 0.1557 | Val mse = 0.3704 Early stopping triggered at epoch 30. Best parameters found in single holdout (grid): batch_size: 128 hidden_dim: 32 hidden_layers: 4 knots: 10 lr: 0.005 spline_power: 7 Refitting with selected hyperparameters on (train_core + train_val), new early-stop split from that union… Using device: mps Epoch 1: Train mse = 0.9255 | Val mse = 0.9724 Epoch 2: Train mse = 0.7916 | Val mse = 0.8157 Epoch 3: Train mse = 0.6388 | Val mse = 0.6205 Epoch 4: Train mse = 0.4332 | Val mse = 0.4116 Epoch 5: Train mse = 0.3296 | Val mse = 0.3722 Epoch 6: Train mse = 0.3018 | Val mse = 0.3335 Epoch 7: Train mse = 0.2849 | Val mse = 0.3253 Epoch 8: Train mse = 0.2774 | Val mse = 0.3497 Epoch 9: Train mse = 0.2692 | Val mse = 0.3287 Epoch 10: Train mse = 0.2599 | Val mse = 0.3315 Epoch 11: Train mse = 0.2544 | Val mse = 0.3151 Epoch 12: Train mse = 0.2460 | Val mse = 0.3386 Epoch 13: Train mse = 0.2403 | Val mse = 0.3294 Epoch 14: Train mse = 0.2354 | Val mse = 0.3443 Epoch 15: Train mse = 0.2291 | Val mse = 0.3545 Epoch 16: Train mse = 0.2242 | Val mse = 0.3569 Epoch 17: Train mse = 0.2173 | Val mse = 0.3504 Epoch 18: Train mse = 0.2116 | Val mse = 0.3506 Epoch 19: Train mse = 0.2065 | Val mse = 0.3498 Epoch 20: Train mse = 0.2033 | Val mse = 0.3552 Epoch 21: Train mse = 0.2022 | Val mse = 0.3462 Epoch 22: Train mse = 0.1950 | Val mse = 0.3599 Epoch 23: Train mse = 0.1962 | Val mse = 0.3545 Epoch 24: Train mse = 0.1912 | Val mse = 0.3658 Epoch 25: Train mse = 0.1906 | Val mse = 0.3605 Epoch 26: Train mse = 0.1898 | Val mse = 0.3614 Epoch 27: Train mse = 0.1870 | Val mse = 0.3495 Epoch 28: Train mse = 0.1837 | Val mse = 0.3576 Epoch 29: Train mse = 0.1843 | Val mse = 0.3768 Epoch 30: Train mse = 0.1824 | Val mse = 0.3539 Early stopping triggered at epoch 30. Results for the refit model (single holdout): --- Task 1 --- 1 day(s) MAE : 0.09252019 1 day(s) RMSE : 0.17764964 1 day(s) R2 : 0.30856554 1 day(s) Pearson r : 0.56776015 1 day(s) QLIKE : 0.36329599 3 day(s) MAE : 0.09563189 3 day(s) RMSE : 0.18181676 3 day(s) R2 : 0.27015979 3 day(s) Pearson r : 0.54133039 3 day(s) QLIKE : 0.41800874 5 day(s) MAE : 0.09779209 5 day(s) RMSE : 0.18419081 5 day(s) R2 : 0.24246419 5 day(s) Pearson r : 0.52675256 5 day(s) QLIKE : 0.44165327 10 day(s) MAE : 0.09992824 10 day(s) RMSE : 0.19002092 10 day(s) R2 : 0.18605322 10 day(s) Pearson r : 0.48376168 10 day(s) QLIKE : 0.45850896 20 day(s) MAE : 0.10213812 20 day(s) RMSE : 0.19326780 20 day(s) R2 : 0.14806914 20 day(s) Pearson r : 0.44689942 20 day(s) QLIKE : 0.49436390 full horizon MAE : 0.10213812 full horizon RMSE : 0.19326780 full horizon R2 : 0.14806914 full horizon Pearson r : 0.44689942 full horizon QLIKE : 0.49436390 Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00319653, max=0.952788 Best single-holdout (refit) model saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/tune_saved_object/EURUSD/Simple_KAN_H20.pkl === AAPL | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714204 Min value: -0.03434294798653995 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.13326641523511 Min value: -2.354189421011421 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.051920323212192546 Min value: -0.0338203458440787 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.906984328061974 Min value: -1.6295009124362931 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.19306601030720755 Min value: -0.03421247164703902 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.8950185841626346 Min value: -2.119931409321887 Epoch 1: Train mse = 1.0053 | Val mse = 0.5075 Epoch 2: Train mse = 0.9592 | Val mse = 0.5476 Epoch 3: Train mse = 0.9382 | Val mse = 0.5856 Epoch 4: Train mse = 0.9258 | Val mse = 0.5513 Epoch 5: Train mse = 0.9315 | Val mse = 0.5267 Epoch 6: Train mse = 0.9337 | Val mse = 0.5410 Epoch 7: Train mse = 0.9313 | Val mse = 0.5645 Epoch 8: Train mse = 0.9175 | Val mse = 0.5654 Epoch 9: Train mse = 0.9042 | Val mse = 0.5033 Epoch 10: Train mse = 0.8865 | Val mse = 0.6743 Epoch 11: Train mse = 0.8625 | Val mse = 0.4420 Epoch 12: Train mse = 0.8238 | Val mse = 0.3964 Epoch 13: Train mse = 0.7722 | Val mse = 0.4718 Epoch 14: Train mse = 0.7341 | Val mse = 0.3833 Epoch 15: Train mse = 0.6993 | Val mse = 0.5085 Epoch 16: Train mse = 0.7034 | Val mse = 0.5098 Epoch 17: Train mse = 0.6912 | Val mse = 0.4392 Epoch 18: Train mse = 0.7164 | Val mse = 0.5452 Epoch 19: Train mse = 0.6878 | Val mse = 0.3483 Epoch 20: Train mse = 0.6456 | Val mse = 0.4198 Epoch 21: Train mse = 0.6019 | Val mse = 0.3500 Epoch 22: Train mse = 0.6041 | Val mse = 0.3548 Epoch 23: Train mse = 0.5795 | Val mse = 0.3265 Epoch 24: Train mse = 0.5646 | Val mse = 0.3849 Epoch 25: Train mse = 0.5594 | Val mse = 0.3231 Epoch 26: Train mse = 0.5475 | Val mse = 0.3961 Epoch 27: Train mse = 0.5630 | Val mse = 0.3441 Epoch 28: Train mse = 0.5349 | Val mse = 0.3475 Epoch 29: Train mse = 0.5329 | Val mse = 0.3180 Epoch 30: Train mse = 0.5142 | Val mse = 0.3192 Epoch 31: Train mse = 0.5479 | Val mse = 0.3958 Epoch 32: Train mse = 0.5473 | Val mse = 0.3665 Epoch 33: Train mse = 0.5457 | Val mse = 0.3636 Epoch 34: Train mse = 0.5480 | Val mse = 0.3179 Epoch 35: Train mse = 0.5275 | Val mse = 0.3302 Epoch 36: Train mse = 0.5292 | Val mse = 0.3561 Epoch 37: Train mse = 0.5314 | Val mse = 0.3120 Epoch 38: Train mse = 0.5017 | Val mse = 0.3151 Epoch 39: Train mse = 0.5105 | Val mse = 0.3610 Epoch 40: Train mse = 0.5172 | Val mse = 0.3075 Epoch 41: Train mse = 0.5025 | Val mse = 0.3185 Epoch 42: Train mse = 0.4980 | Val mse = 0.3455 Epoch 43: Train mse = 0.5004 | Val mse = 0.3180 Epoch 44: Train mse = 0.4911 | Val mse = 0.3026 Epoch 45: Train mse = 0.4946 | Val mse = 0.3092 Epoch 46: Train mse = 0.4841 | Val mse = 0.3065 Epoch 47: Train mse = 0.4772 | Val mse = 0.3006 Epoch 48: Train mse = 0.4944 | Val mse = 0.2971 Epoch 49: Train mse = 0.4707 | Val mse = 0.3146 Epoch 50: Train mse = 0.4783 | Val mse = 0.2992 Parameters used in the single-fit model: input_dim: 60 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.09291805 1 day(s) RMSE : 7.94515940 1 day(s) R2 : 0.15488039 1 day(s) Pearson r : 0.42508621 1 day(s) QLIKE : 0.27848419 full horizon MAE : 2.09291805 full horizon RMSE : 7.94515940 full horizon R2 : 0.15488039 full horizon Pearson r : 0.42508621 full horizon QLIKE : 0.27848419 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/AAPL/Simple_KAN_H1.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=0.910116, max=42.1542 === AAPL | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714204 Min value: -0.03434294798653995 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.140231129276094 Min value: -2.358314580069225 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.051920323212192546 Min value: -0.0338203458440787 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.907938252663739 Min value: -1.632926464574096 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.19306601030720755 Min value: -0.03421247164703902 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.896926346992255 Min value: -2.123830418076347 Epoch 1: Train mse = 0.9891 | Val mse = 0.5359 Epoch 2: Train mse = 0.9560 | Val mse = 0.5720 Epoch 3: Train mse = 0.9503 | Val mse = 0.5444 Epoch 4: Train mse = 0.9461 | Val mse = 0.5649 Epoch 5: Train mse = 0.9304 | Val mse = 0.5143 Epoch 6: Train mse = 0.9098 | Val mse = 0.5279 Epoch 7: Train mse = 0.8673 | Val mse = 0.8307 Epoch 8: Train mse = 0.8977 | Val mse = 0.4213 Epoch 9: Train mse = 0.8272 | Val mse = 0.5242 Epoch 10: Train mse = 0.8127 | Val mse = 0.3998 Epoch 11: Train mse = 0.7924 | Val mse = 0.5530 Epoch 12: Train mse = 0.7598 | Val mse = 0.3725 Epoch 13: Train mse = 0.7171 | Val mse = 0.3614 Epoch 14: Train mse = 0.6899 | Val mse = 0.3632 Epoch 15: Train mse = 0.6846 | Val mse = 0.3885 Epoch 16: Train mse = 0.6722 | Val mse = 0.3592 Epoch 17: Train mse = 0.6530 | Val mse = 0.3651 Epoch 18: Train mse = 0.6485 | Val mse = 0.3718 Epoch 19: Train mse = 0.6275 | Val mse = 0.3714 Epoch 20: Train mse = 0.6338 | Val mse = 0.4259 Epoch 21: Train mse = 0.6296 | Val mse = 0.5326 Epoch 22: Train mse = 0.6995 | Val mse = 0.4398 Epoch 23: Train mse = 0.6728 | Val mse = 0.3708 Epoch 24: Train mse = 0.6525 | Val mse = 0.4712 Epoch 25: Train mse = 0.6376 | Val mse = 0.4230 Epoch 26: Train mse = 0.6805 | Val mse = 0.3777 Epoch 27: Train mse = 0.6278 | Val mse = 0.3803 Epoch 28: Train mse = 0.6282 | Val mse = 0.3783 Epoch 29: Train mse = 0.6348 | Val mse = 0.4645 Epoch 30: Train mse = 0.6227 | Val mse = 0.3748 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.25110548 1 day(s) RMSE : 8.34395350 1 day(s) R2 : 0.06791246 1 day(s) Pearson r : 0.45744229 1 day(s) QLIKE : 0.33601700 3 day(s) MAE : 2.37829843 3 day(s) RMSE : 8.68364087 3 day(s) R2 : 0.04583297 3 day(s) Pearson r : 0.41146993 3 day(s) QLIKE : 0.37382729 5 day(s) MAE : 2.42497110 5 day(s) RMSE : 8.81880666 5 day(s) R2 : 0.02675646 5 day(s) Pearson r : 0.34551270 5 day(s) QLIKE : 0.40330327 full horizon MAE : 2.42497110 full horizon RMSE : 8.81880666 full horizon R2 : 0.02675646 full horizon Pearson r : 0.34551270 full horizon QLIKE : 0.40330327 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/AAPL/Simple_KAN_H5.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=1.10249, max=10.0947 === AAPL | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714204 Min value: -0.03434294798653995 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.14985814599714 Min value: -2.3634631558377808 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.051920323212192546 Min value: -0.0338203458440787 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.909556794039435 Min value: -1.6371429190585964 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.19306601030720755 Min value: -0.03421247164703902 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.8998157347365474 Min value: -2.1286776824868885 Epoch 1: Train mse = 0.9898 | Val mse = 0.5641 Epoch 2: Train mse = 0.9701 | Val mse = 0.5563 Epoch 3: Train mse = 0.9656 | Val mse = 0.5651 Epoch 4: Train mse = 0.9581 | Val mse = 0.5675 Epoch 5: Train mse = 0.9407 | Val mse = 0.4860 Epoch 6: Train mse = 0.9193 | Val mse = 0.4606 Epoch 7: Train mse = 0.8759 | Val mse = 0.8520 Epoch 8: Train mse = 0.9160 | Val mse = 0.4071 Epoch 9: Train mse = 0.8396 | Val mse = 0.6457 Epoch 10: Train mse = 0.8397 | Val mse = 0.4058 Epoch 11: Train mse = 0.8139 | Val mse = 0.5295 Epoch 12: Train mse = 0.7736 | Val mse = 0.3798 Epoch 13: Train mse = 0.7621 | Val mse = 0.3747 Epoch 14: Train mse = 0.7423 | Val mse = 0.3802 Epoch 15: Train mse = 0.7299 | Val mse = 0.3755 Epoch 16: Train mse = 0.7030 | Val mse = 0.4419 Epoch 17: Train mse = 0.7069 | Val mse = 0.5444 Epoch 18: Train mse = 0.7402 | Val mse = 0.4959 Epoch 19: Train mse = 0.7195 | Val mse = 0.3984 Epoch 20: Train mse = 0.7311 | Val mse = 0.4568 Epoch 21: Train mse = 0.7095 | Val mse = 0.3802 Epoch 22: Train mse = 0.6900 | Val mse = 0.3766 Epoch 23: Train mse = 0.6784 | Val mse = 0.4264 Epoch 24: Train mse = 0.6762 | Val mse = 0.4274 Epoch 25: Train mse = 0.6693 | Val mse = 0.4022 Epoch 26: Train mse = 0.7168 | Val mse = 0.3735 Epoch 27: Train mse = 0.6855 | Val mse = 0.4669 Epoch 28: Train mse = 0.6843 | Val mse = 0.4031 Epoch 29: Train mse = 0.6993 | Val mse = 0.4212 Epoch 30: Train mse = 0.6723 | Val mse = 0.3904 Epoch 31: Train mse = 0.6590 | Val mse = 0.3731 Epoch 32: Train mse = 0.6582 | Val mse = 0.4205 Epoch 33: Train mse = 0.6651 | Val mse = 0.3853 Epoch 34: Train mse = 0.6573 | Val mse = 0.3919 Epoch 35: Train mse = 0.6774 | Val mse = 0.4104 Epoch 36: Train mse = 0.6671 | Val mse = 0.3988 Epoch 37: Train mse = 0.6543 | Val mse = 0.3899 Epoch 38: Train mse = 0.6672 | Val mse = 0.4333 Epoch 39: Train mse = 0.6700 | Val mse = 0.3790 Epoch 40: Train mse = 0.6513 | Val mse = 0.3726 Epoch 41: Train mse = 0.6575 | Val mse = 0.4257 Epoch 42: Train mse = 0.6578 | Val mse = 0.3751 Epoch 43: Train mse = 0.6402 | Val mse = 0.3728 Epoch 44: Train mse = 0.6399 | Val mse = 0.3736 Epoch 45: Train mse = 0.6406 | Val mse = 0.3955 Epoch 46: Train mse = 0.6427 | Val mse = 0.3813 Epoch 47: Train mse = 0.6361 | Val mse = 0.3743 Epoch 48: Train mse = 0.6406 | Val mse = 0.4132 Epoch 49: Train mse = 0.6409 | Val mse = 0.3798 Epoch 50: Train mse = 0.6331 | Val mse = 0.3734 Early stopping triggered at epoch 50. Parameters used in the single-fit model: input_dim: 60 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.20014539 1 day(s) RMSE : 8.22099640 1 day(s) R2 : 0.09518068 1 day(s) Pearson r : 0.48762716 1 day(s) QLIKE : 0.30315272 3 day(s) MAE : 2.35183440 3 day(s) RMSE : 8.54207272 3 day(s) R2 : 0.07669066 3 day(s) Pearson r : 0.44946218 3 day(s) QLIKE : 0.33890736 5 day(s) MAE : 2.41954112 5 day(s) RMSE : 8.70379274 5 day(s) R2 : 0.05197679 5 day(s) Pearson r : 0.37753169 5 day(s) QLIKE : 0.37228861 10 day(s) MAE : 2.50346173 10 day(s) RMSE : 8.91896613 10 day(s) R2 : 0.01271223 10 day(s) Pearson r : 0.25150718 10 day(s) QLIKE : 0.42610093 full horizon MAE : 2.50346173 full horizon RMSE : 8.91896613 full horizon R2 : 0.01271223 full horizon Pearson r : 0.25150718 full horizon QLIKE : 0.42610093 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/AAPL/Simple_KAN_H10.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=0.934215, max=13.0407 === AAPL | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714204 Min value: -0.03434294798653995 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.17404540383974 Min value: -2.37751047680313 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.051920323212192546 Min value: -0.0338203458440787 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9130206404899854 Min value: -1.6487782037743786 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.19306601030720755 Min value: -0.03421247164703902 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.906568131251477 Min value: -2.14194530457574 Epoch 1: Train mse = 0.9910 | Val mse = 0.5617 Epoch 2: Train mse = 0.9783 | Val mse = 0.5729 Epoch 3: Train mse = 0.9744 | Val mse = 0.5637 Epoch 4: Train mse = 0.9718 | Val mse = 0.5857 Epoch 5: Train mse = 0.9566 | Val mse = 0.5004 Epoch 6: Train mse = 0.9451 | Val mse = 0.5708 Epoch 7: Train mse = 0.9194 | Val mse = 0.7146 Epoch 8: Train mse = 0.9179 | Val mse = 0.4578 Epoch 9: Train mse = 0.8673 | Val mse = 0.4113 Epoch 10: Train mse = 0.8416 | Val mse = 0.6305 Epoch 11: Train mse = 0.8394 | Val mse = 0.3961 Epoch 12: Train mse = 0.8093 | Val mse = 0.3752 Epoch 13: Train mse = 0.8231 | Val mse = 0.4683 Epoch 14: Train mse = 0.7944 | Val mse = 0.4825 Epoch 15: Train mse = 0.7757 | Val mse = 0.3699 Epoch 16: Train mse = 0.7705 | Val mse = 0.3751 Epoch 17: Train mse = 0.7754 | Val mse = 0.3782 Epoch 18: Train mse = 0.7501 | Val mse = 0.4101 Epoch 19: Train mse = 0.7404 | Val mse = 0.4434 Epoch 20: Train mse = 0.7555 | Val mse = 0.5498 Epoch 21: Train mse = 0.7871 | Val mse = 0.4997 Epoch 22: Train mse = 0.7771 | Val mse = 0.3684 Epoch 23: Train mse = 0.7644 | Val mse = 0.4714 Epoch 24: Train mse = 0.7532 | Val mse = 0.3880 Epoch 25: Train mse = 0.7521 | Val mse = 0.3824 Epoch 26: Train mse = 0.7720 | Val mse = 0.4754 Epoch 27: Train mse = 0.7496 | Val mse = 0.3846 Epoch 28: Train mse = 0.7619 | Val mse = 0.3691 Epoch 29: Train mse = 0.7627 | Val mse = 0.5362 Epoch 30: Train mse = 0.7520 | Val mse = 0.3719 Epoch 31: Train mse = 0.7504 | Val mse = 0.4614 Epoch 32: Train mse = 0.7414 | Val mse = 0.3854 Early stopping triggered at epoch 32. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.30159873 1 day(s) RMSE : 8.52796474 1 day(s) R2 : 0.02634804 1 day(s) Pearson r : 0.43032947 1 day(s) QLIKE : 0.34556555 3 day(s) MAE : 2.41229240 3 day(s) RMSE : 8.82574264 3 day(s) R2 : 0.01434889 3 day(s) Pearson r : 0.39985512 3 day(s) QLIKE : 0.37992373 5 day(s) MAE : 2.44877101 5 day(s) RMSE : 8.93104856 5 day(s) R2 : 0.00182477 5 day(s) Pearson r : 0.33653280 5 day(s) QLIKE : 0.40626129 10 day(s) MAE : 2.48935098 10 day(s) RMSE : 9.04631660 10 day(s) R2 : -0.01568326 10 day(s) Pearson r : 0.23878478 10 day(s) QLIKE : 0.44873021 20 day(s) MAE : 2.51857575 20 day(s) RMSE : 9.10493087 20 day(s) R2 : -0.02451126 20 day(s) Pearson r : 0.18519968 20 day(s) QLIKE : 0.47639951 full horizon MAE : 2.51857575 full horizon RMSE : 9.10493087 full horizon R2 : -0.02451126 full horizon Pearson r : 0.18519968 full horizon QLIKE : 0.47639951 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/AAPL/Simple_KAN_H20.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=1.04742, max=8.53787 === MSFT | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.550928529745955 Min value: -0.49195094366183095 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.2181198470171735 Min value: -2.80514498305857 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.9099560750963094 Min value: -0.42477627271922275 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.894759677334685 Min value: -1.3150487901568384 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.030086059790822 Min value: -0.4794650148863312 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.930619914036178 Min value: -2.329935051243564 Epoch 1: Train mse = 0.9254 | Val mse = 0.4698 Epoch 2: Train mse = 0.7538 | Val mse = 0.4615 Epoch 3: Train mse = 0.6147 | Val mse = 0.5705 Epoch 4: Train mse = 0.5394 | Val mse = 0.5249 Epoch 5: Train mse = 0.4789 | Val mse = 0.5537 Epoch 6: Train mse = 0.4585 | Val mse = 0.4981 Epoch 7: Train mse = 0.4176 | Val mse = 0.4731 Epoch 8: Train mse = 0.3971 | Val mse = 0.5471 Epoch 9: Train mse = 0.3738 | Val mse = 0.5202 Epoch 10: Train mse = 0.3604 | Val mse = 0.5683 Epoch 11: Train mse = 0.3516 | Val mse = 0.5048 Epoch 12: Train mse = 0.3277 | Val mse = 0.5299 Epoch 13: Train mse = 0.3119 | Val mse = 0.4805 Epoch 14: Train mse = 0.3033 | Val mse = 0.5719 Epoch 15: Train mse = 0.2895 | Val mse = 0.5775 Epoch 16: Train mse = 0.2641 | Val mse = 0.5606 Epoch 17: Train mse = 0.2481 | Val mse = 0.6413 Epoch 18: Train mse = 0.2235 | Val mse = 0.6416 Epoch 19: Train mse = 0.2074 | Val mse = 0.6176 Epoch 20: Train mse = 0.2026 | Val mse = 0.6393 Epoch 21: Train mse = 0.1849 | Val mse = 0.7020 Epoch 22: Train mse = 0.1649 | Val mse = 0.6636 Epoch 23: Train mse = 0.1580 | Val mse = 0.7256 Epoch 24: Train mse = 0.1394 | Val mse = 0.6844 Epoch 25: Train mse = 0.1263 | Val mse = 0.6885 Epoch 26: Train mse = 0.1222 | Val mse = 0.7086 Epoch 27: Train mse = 0.1096 | Val mse = 0.7359 Epoch 28: Train mse = 0.1021 | Val mse = 0.7173 Epoch 29: Train mse = 0.0972 | Val mse = 0.7057 Epoch 30: Train mse = 0.0848 | Val mse = 0.7699 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.05990044 1 day(s) RMSE : 6.69425260 1 day(s) R2 : 0.00254031 1 day(s) Pearson r : 0.13176566 1 day(s) QLIKE : 0.48850694 full horizon MAE : 2.05990044 full horizon RMSE : 6.69425260 full horizon R2 : 0.00254031 full horizon Pearson r : 0.13176566 full horizon QLIKE : 0.48850694 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/MSFT/Simple_KAN_H1.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=1.29444, max=6.91675 === MSFT | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.550928529745955 Min value: -0.49195094366183095 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.21838735918136 Min value: -2.8084790413953242 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.9099560750963094 Min value: -0.42477627271922275 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.8943485628110612 Min value: -1.3176187186164257 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.030086059790822 Min value: -0.4794650148863312 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.9307399945851973 Min value: -2.33302541920076 Epoch 1: Train mse = 0.9563 | Val mse = 0.4579 Epoch 2: Train mse = 0.7624 | Val mse = 0.5573 Epoch 3: Train mse = 0.6701 | Val mse = 0.5553 Epoch 4: Train mse = 0.6024 | Val mse = 0.5101 Epoch 5: Train mse = 0.5674 | Val mse = 0.5017 Epoch 6: Train mse = 0.5519 | Val mse = 0.5015 Epoch 7: Train mse = 0.5330 | Val mse = 0.5092 Epoch 8: Train mse = 0.5163 | Val mse = 0.5042 Epoch 9: Train mse = 0.5010 | Val mse = 0.5265 Epoch 10: Train mse = 0.4867 | Val mse = 0.5352 Epoch 11: Train mse = 0.4768 | Val mse = 0.5345 Epoch 12: Train mse = 0.4623 | Val mse = 0.5143 Epoch 13: Train mse = 0.4517 | Val mse = 0.5425 Epoch 14: Train mse = 0.4337 | Val mse = 0.5918 Epoch 15: Train mse = 0.4233 | Val mse = 0.5521 Epoch 16: Train mse = 0.4086 | Val mse = 0.5846 Epoch 17: Train mse = 0.3898 | Val mse = 0.6368 Epoch 18: Train mse = 0.3727 | Val mse = 0.6296 Epoch 19: Train mse = 0.3474 | Val mse = 0.6888 Epoch 20: Train mse = 0.3335 | Val mse = 0.6975 Epoch 21: Train mse = 0.3151 | Val mse = 0.6753 Epoch 22: Train mse = 0.3042 | Val mse = 0.7631 Epoch 23: Train mse = 0.2956 | Val mse = 0.7191 Epoch 24: Train mse = 0.2853 | Val mse = 0.7395 Epoch 25: Train mse = 0.2596 | Val mse = 0.6841 Epoch 26: Train mse = 0.2447 | Val mse = 0.7641 Epoch 27: Train mse = 0.2353 | Val mse = 0.7305 Epoch 28: Train mse = 0.2196 | Val mse = 0.7143 Epoch 29: Train mse = 0.2047 | Val mse = 0.7753 Epoch 30: Train mse = 0.1907 | Val mse = 0.7252 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.08697930 1 day(s) RMSE : 6.74999687 1 day(s) R2 : -0.01414092 1 day(s) Pearson r : 0.04331799 1 day(s) QLIKE : 0.47820556 3 day(s) MAE : 2.09707068 3 day(s) RMSE : 6.77808809 3 day(s) R2 : -0.02266788 3 day(s) Pearson r : 0.01284365 3 day(s) QLIKE : 0.49117872 5 day(s) MAE : 2.10297842 5 day(s) RMSE : 6.78954492 5 day(s) R2 : -0.02601286 5 day(s) Pearson r : -0.00831240 5 day(s) QLIKE : 0.49696362 full horizon MAE : 2.10297842 full horizon RMSE : 6.78954492 full horizon R2 : -0.02601286 full horizon Pearson r : -0.00831240 full horizon QLIKE : 0.49696362 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/MSFT/Simple_KAN_H5.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=1.72465, max=6.17752 === MSFT | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.550928529745955 Min value: -0.49195094366183095 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.2208777591762505 Min value: -2.8143642605456183 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.9099560750963094 Min value: -0.42477627271922275 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.8952607849867555 Min value: -1.3217269183173053 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.030086059790822 Min value: -0.4794650148863312 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.9328875355250505 Min value: -2.3383439250775 Epoch 1: Train mse = 0.9629 | Val mse = 0.4491 Epoch 2: Train mse = 0.8065 | Val mse = 0.5414 Epoch 3: Train mse = 0.7313 | Val mse = 0.5498 Epoch 4: Train mse = 0.6766 | Val mse = 0.5387 Epoch 5: Train mse = 0.6424 | Val mse = 0.4807 Epoch 6: Train mse = 0.6213 | Val mse = 0.4916 Epoch 7: Train mse = 0.6001 | Val mse = 0.5406 Epoch 8: Train mse = 0.5889 | Val mse = 0.5193 Epoch 9: Train mse = 0.5702 | Val mse = 0.5264 Epoch 10: Train mse = 0.5605 | Val mse = 0.5407 Epoch 11: Train mse = 0.5428 | Val mse = 0.5692 Epoch 12: Train mse = 0.5335 | Val mse = 0.5546 Epoch 13: Train mse = 0.5160 | Val mse = 0.5746 Epoch 14: Train mse = 0.5024 | Val mse = 0.5861 Epoch 15: Train mse = 0.4849 | Val mse = 0.6377 Epoch 16: Train mse = 0.4669 | Val mse = 0.6235 Epoch 17: Train mse = 0.4462 | Val mse = 0.6726 Epoch 18: Train mse = 0.4249 | Val mse = 0.6777 Epoch 19: Train mse = 0.4209 | Val mse = 0.6784 Epoch 20: Train mse = 0.4117 | Val mse = 0.6468 Epoch 21: Train mse = 0.3875 | Val mse = 0.6715 Epoch 22: Train mse = 0.3778 | Val mse = 0.6640 Epoch 23: Train mse = 0.3612 | Val mse = 0.6825 Epoch 24: Train mse = 0.3444 | Val mse = 0.6414 Epoch 25: Train mse = 0.3340 | Val mse = 0.7065 Epoch 26: Train mse = 0.3285 | Val mse = 0.6746 Epoch 27: Train mse = 0.3130 | Val mse = 0.6802 Epoch 28: Train mse = 0.3007 | Val mse = 0.6831 Epoch 29: Train mse = 0.2919 | Val mse = 0.6829 Epoch 30: Train mse = 0.2864 | Val mse = 0.6772 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.09371599 1 day(s) RMSE : 6.74870822 1 day(s) R2 : -0.01375373 1 day(s) Pearson r : 0.03685442 1 day(s) QLIKE : 0.47787292 3 day(s) MAE : 2.10093592 3 day(s) RMSE : 6.78171489 3 day(s) R2 : -0.02376258 3 day(s) Pearson r : 0.00789979 3 day(s) QLIKE : 0.49269768 5 day(s) MAE : 2.10425180 5 day(s) RMSE : 6.79225105 5 day(s) R2 : -0.02683090 5 day(s) Pearson r : -0.01282886 5 day(s) QLIKE : 0.49814073 10 day(s) MAE : 2.12759607 10 day(s) RMSE : 6.80651790 10 day(s) R2 : -0.03107345 10 day(s) Pearson r : -0.03254652 10 day(s) QLIKE : 0.50281527 full horizon MAE : 2.12759607 full horizon RMSE : 6.80651790 full horizon R2 : -0.03107345 full horizon Pearson r : -0.03254652 full horizon QLIKE : 0.50281527 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/MSFT/Simple_KAN_H10.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=1.67026, max=6.31979 === MSFT | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.550928529745955 Min value: -0.49195094366183095 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.2181890995511795 Min value: -2.821740645483995 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.9099560750963094 Min value: -0.42477627271922275 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.891688839722294 Min value: -1.3281087285369817 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.030086059790822 Min value: -0.4794650148863312 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.930006982137461 Min value: -2.3454031279530607 Epoch 1: Train mse = 0.9729 | Val mse = 0.4179 Epoch 2: Train mse = 0.8606 | Val mse = 0.5052 Epoch 3: Train mse = 0.7965 | Val mse = 0.4968 Epoch 4: Train mse = 0.7578 | Val mse = 0.5107 Epoch 5: Train mse = 0.7229 | Val mse = 0.4864 Epoch 6: Train mse = 0.6967 | Val mse = 0.4898 Epoch 7: Train mse = 0.6810 | Val mse = 0.5122 Epoch 8: Train mse = 0.6715 | Val mse = 0.5057 Epoch 9: Train mse = 0.6540 | Val mse = 0.5154 Epoch 10: Train mse = 0.6401 | Val mse = 0.5123 Epoch 11: Train mse = 0.6226 | Val mse = 0.5372 Epoch 12: Train mse = 0.6152 | Val mse = 0.5199 Epoch 13: Train mse = 0.5941 | Val mse = 0.5393 Epoch 14: Train mse = 0.5846 | Val mse = 0.5380 Epoch 15: Train mse = 0.5673 | Val mse = 0.5911 Epoch 16: Train mse = 0.5468 | Val mse = 0.5313 Epoch 17: Train mse = 0.5385 | Val mse = 0.5858 Epoch 18: Train mse = 0.5247 | Val mse = 0.5715 Epoch 19: Train mse = 0.5109 | Val mse = 0.7150 Epoch 20: Train mse = 0.4996 | Val mse = 0.5361 Epoch 21: Train mse = 0.4855 | Val mse = 0.6277 Epoch 22: Train mse = 0.4613 | Val mse = 0.5562 Epoch 23: Train mse = 0.4379 | Val mse = 0.5822 Epoch 24: Train mse = 0.4223 | Val mse = 0.6124 Epoch 25: Train mse = 0.4083 | Val mse = 0.5499 Epoch 26: Train mse = 0.3985 | Val mse = 0.5674 Epoch 27: Train mse = 0.3869 | Val mse = 0.5961 Epoch 28: Train mse = 0.3742 | Val mse = 0.5470 Epoch 29: Train mse = 0.3646 | Val mse = 0.5922 Epoch 30: Train mse = 0.3529 | Val mse = 0.5725 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.08556181 1 day(s) RMSE : 6.74987247 1 day(s) R2 : -0.01410353 1 day(s) Pearson r : 0.03039663 1 day(s) QLIKE : 0.47572714 3 day(s) MAE : 2.09948756 3 day(s) RMSE : 6.78082289 3 day(s) R2 : -0.02349328 3 day(s) Pearson r : 0.00300031 3 day(s) QLIKE : 0.49200768 5 day(s) MAE : 2.10071868 5 day(s) RMSE : 6.78909584 5 day(s) R2 : -0.02587714 5 day(s) Pearson r : -0.01649653 5 day(s) QLIKE : 0.49618999 10 day(s) MAE : 2.12444029 10 day(s) RMSE : 6.80147687 10 day(s) R2 : -0.02954676 10 day(s) Pearson r : -0.03414388 10 day(s) QLIKE : 0.50029684 20 day(s) MAE : 2.17795226 20 day(s) RMSE : 6.99889758 20 day(s) R2 : -0.02726827 20 day(s) Pearson r : -0.02294529 20 day(s) QLIKE : 0.50306697 full horizon MAE : 2.17795226 full horizon RMSE : 6.99889758 full horizon R2 : -0.02726827 full horizon Pearson r : -0.02294529 full horizon QLIKE : 0.50306697 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/MSFT/Simple_KAN_H20.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=1.70931, max=5.88749 === GE | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238557 Min value: -0.033788504883313815 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.030029259880504 Min value: -2.652656432671519 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4221295497976101 Min value: -0.03368162544886536 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.0718644033108644 Min value: -2.366307284351862 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4422974929765818 Min value: -0.03368162544886536 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.646703896552383 Min value: -2.148420825574933 Epoch 1: Train mse = 0.9938 | Val mse = 0.7719 Epoch 2: Train mse = 0.9907 | Val mse = 0.8884 Epoch 3: Train mse = 0.9874 | Val mse = 0.8601 Epoch 4: Train mse = 0.9702 | Val mse = 0.8258 Epoch 5: Train mse = 0.9800 | Val mse = 0.8268 Epoch 6: Train mse = 0.9722 | Val mse = 0.8654 Epoch 7: Train mse = 0.9538 | Val mse = 0.8206 Epoch 8: Train mse = 0.8813 | Val mse = 1.2264 Epoch 9: Train mse = 0.8915 | Val mse = 0.8191 Epoch 10: Train mse = 0.7528 | Val mse = 0.4925 Epoch 11: Train mse = 0.7462 | Val mse = 0.8326 Epoch 12: Train mse = 0.7046 | Val mse = 0.5297 Epoch 13: Train mse = 0.6734 | Val mse = 0.9093 Epoch 14: Train mse = 0.6730 | Val mse = 0.8770 Epoch 15: Train mse = 0.6560 | Val mse = 0.6344 Epoch 16: Train mse = 0.6435 | Val mse = 0.9138 Epoch 17: Train mse = 0.6360 | Val mse = 0.6632 Epoch 18: Train mse = 0.6410 | Val mse = 0.4678 Epoch 19: Train mse = 0.6611 | Val mse = 0.8444 Epoch 20: Train mse = 0.6299 | Val mse = 0.5433 Epoch 21: Train mse = 0.6144 | Val mse = 0.5621 Epoch 22: Train mse = 0.5870 | Val mse = 0.7546 Epoch 23: Train mse = 0.5703 | Val mse = 0.5304 Epoch 24: Train mse = 0.5810 | Val mse = 0.5670 Epoch 25: Train mse = 0.5645 | Val mse = 0.5737 Epoch 26: Train mse = 0.5691 | Val mse = 0.5467 Epoch 27: Train mse = 0.5426 | Val mse = 0.6746 Epoch 28: Train mse = 0.5370 | Val mse = 0.5707 Epoch 29: Train mse = 0.5656 | Val mse = 0.5968 Epoch 30: Train mse = 0.6146 | Val mse = 0.5025 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.16510060 1 day(s) RMSE : 27.62973955 1 day(s) R2 : -0.01489890 1 day(s) Pearson r : 0.02135212 1 day(s) QLIKE : 0.70782687 full horizon MAE : 4.16510060 full horizon RMSE : 27.62973955 full horizon R2 : -0.01489890 full horizon Pearson r : 0.02135212 full horizon QLIKE : 0.70782687 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GE/Simple_KAN_H1.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=1.74756, max=12.9921 === GE | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238557 Min value: -0.033788504883313815 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.05628901565786 Min value: -2.663641411446664 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4221295497976101 Min value: -0.03368162544886536 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.0759413314511908 Min value: -2.3763793738908188 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4422974929765818 Min value: -0.03368162544886536 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.658989486293715 Min value: -2.1577982868808543 Epoch 1: Train mse = 0.9991 | Val mse = 0.9131 Epoch 2: Train mse = 0.9932 | Val mse = 0.9112 Epoch 3: Train mse = 0.9935 | Val mse = 0.8607 Epoch 4: Train mse = 0.9855 | Val mse = 0.8800 Epoch 5: Train mse = 0.9839 | Val mse = 0.8868 Epoch 6: Train mse = 0.9778 | Val mse = 0.8464 Epoch 7: Train mse = 0.9520 | Val mse = 0.9441 Epoch 8: Train mse = 0.8467 | Val mse = 0.9627 Epoch 9: Train mse = 0.8783 | Val mse = 1.6082 Epoch 10: Train mse = 0.8841 | Val mse = 0.5860 Epoch 11: Train mse = 0.8600 | Val mse = 0.5924 Epoch 12: Train mse = 0.8855 | Val mse = 0.7008 Epoch 13: Train mse = 0.8417 | Val mse = 0.8750 Epoch 14: Train mse = 0.7904 | Val mse = 0.7858 Epoch 15: Train mse = 0.7210 | Val mse = 0.6099 Epoch 16: Train mse = 0.7098 | Val mse = 0.6118 Epoch 17: Train mse = 0.6581 | Val mse = 0.6129 Epoch 18: Train mse = 0.6419 | Val mse = 0.6728 Epoch 19: Train mse = 0.6272 | Val mse = 0.7941 Epoch 20: Train mse = 0.7148 | Val mse = 0.7506 Epoch 21: Train mse = 0.6846 | Val mse = 1.0326 Epoch 22: Train mse = 0.7003 | Val mse = 0.4515 Epoch 23: Train mse = 0.6698 | Val mse = 0.7895 Epoch 24: Train mse = 0.6367 | Val mse = 0.5284 Epoch 25: Train mse = 0.6275 | Val mse = 0.5942 Epoch 26: Train mse = 0.6096 | Val mse = 0.7522 Epoch 27: Train mse = 0.6231 | Val mse = 0.6312 Epoch 28: Train mse = 0.5960 | Val mse = 0.5194 Epoch 29: Train mse = 0.6094 | Val mse = 0.6909 Epoch 30: Train mse = 0.5935 | Val mse = 0.6980 Epoch 31: Train mse = 0.5913 | Val mse = 0.5902 Epoch 32: Train mse = 0.5780 | Val mse = 0.5866 Early stopping triggered at epoch 32. Parameters used in the single-fit model: input_dim: 60 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.05822975 1 day(s) RMSE : 27.58341600 1 day(s) R2 : -0.01149862 1 day(s) Pearson r : 0.02931681 1 day(s) QLIKE : 0.69370861 3 day(s) MAE : 4.12038763 3 day(s) RMSE : 27.59488814 3 day(s) R2 : -0.01239844 3 day(s) Pearson r : 0.01810067 3 day(s) QLIKE : 0.70444985 5 day(s) MAE : 4.16206894 5 day(s) RMSE : 27.60343495 5 day(s) R2 : -0.01308368 5 day(s) Pearson r : 0.00933897 5 day(s) QLIKE : 0.71453781 full horizon MAE : 4.16206894 full horizon RMSE : 27.60343495 full horizon R2 : -0.01308368 full horizon Pearson r : 0.00933897 full horizon QLIKE : 0.71453781 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GE/Simple_KAN_H5.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=1.95213, max=11.6411 === GE | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238557 Min value: -0.033788504883313815 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.080595466809852 Min value: -2.674432668646892 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4221295497976101 Min value: -0.03368162544886536 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.079343715915996 Min value: -2.3863103666656302 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4422974929765818 Min value: -0.03368162544886536 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.670127332275317 Min value: -2.167074694264489 Epoch 1: Train mse = 0.9973 | Val mse = 0.9485 Epoch 2: Train mse = 0.9903 | Val mse = 0.9421 Epoch 3: Train mse = 0.9872 | Val mse = 0.8897 Epoch 4: Train mse = 0.9816 | Val mse = 0.9019 Epoch 5: Train mse = 0.9753 | Val mse = 0.9017 Epoch 6: Train mse = 0.9513 | Val mse = 0.8946 Epoch 7: Train mse = 0.8740 | Val mse = 0.8438 Epoch 8: Train mse = 0.8453 | Val mse = 0.5438 Epoch 9: Train mse = 0.7968 | Val mse = 0.7913 Epoch 10: Train mse = 0.7902 | Val mse = 0.6465 Epoch 11: Train mse = 0.7752 | Val mse = 0.8781 Epoch 12: Train mse = 0.7376 | Val mse = 0.7444 Epoch 13: Train mse = 0.7169 | Val mse = 0.6393 Epoch 14: Train mse = 0.6872 | Val mse = 0.5695 Epoch 15: Train mse = 0.7079 | Val mse = 0.6222 Epoch 16: Train mse = 0.6718 | Val mse = 0.9295 Epoch 17: Train mse = 0.6733 | Val mse = 0.9263 Epoch 18: Train mse = 0.6870 | Val mse = 0.6816 Epoch 19: Train mse = 0.6533 | Val mse = 0.5498 Epoch 20: Train mse = 0.6374 | Val mse = 0.6488 Epoch 21: Train mse = 0.6399 | Val mse = 0.7327 Epoch 22: Train mse = 0.6536 | Val mse = 0.8391 Epoch 23: Train mse = 0.6309 | Val mse = 0.6833 Epoch 24: Train mse = 0.6133 | Val mse = 0.5995 Epoch 25: Train mse = 0.6098 | Val mse = 0.5958 Epoch 26: Train mse = 0.6611 | Val mse = 0.5477 Epoch 27: Train mse = 0.7278 | Val mse = 0.9080 Epoch 28: Train mse = 0.6887 | Val mse = 0.5293 Epoch 29: Train mse = 0.6600 | Val mse = 0.7193 Epoch 30: Train mse = 0.6405 | Val mse = 0.7425 Epoch 31: Train mse = 0.6301 | Val mse = 0.5127 Epoch 32: Train mse = 0.6296 | Val mse = 0.6902 Epoch 33: Train mse = 0.6112 | Val mse = 0.7864 Epoch 34: Train mse = 0.6073 | Val mse = 0.6058 Epoch 35: Train mse = 0.5996 | Val mse = 0.5828 Epoch 36: Train mse = 0.6023 | Val mse = 0.5594 Epoch 37: Train mse = 0.5960 | Val mse = 0.5534 Epoch 38: Train mse = 0.5994 | Val mse = 0.5489 Epoch 39: Train mse = 0.6066 | Val mse = 0.5423 Epoch 40: Train mse = 0.6001 | Val mse = 0.5931 Epoch 41: Train mse = 0.6030 | Val mse = 0.6706 Early stopping triggered at epoch 41. Parameters used in the single-fit model: input_dim: 60 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.15799679 1 day(s) RMSE : 27.59039340 1 day(s) R2 : -0.01201042 1 day(s) Pearson r : 0.03725842 1 day(s) QLIKE : 0.70512701 3 day(s) MAE : 4.24159162 3 day(s) RMSE : 27.60785856 3 day(s) R2 : -0.01335037 3 day(s) Pearson r : 0.02322336 3 day(s) QLIKE : 0.71928524 5 day(s) MAE : 4.29887239 5 day(s) RMSE : 27.62561625 5 day(s) R2 : -0.01471250 5 day(s) Pearson r : 0.01391111 5 day(s) QLIKE : 0.73467487 10 day(s) MAE : 4.39848848 10 day(s) RMSE : 27.64341170 10 day(s) R2 : -0.01587111 10 day(s) Pearson r : 0.00412119 10 day(s) QLIKE : 0.75186128 full horizon MAE : 4.39848848 full horizon RMSE : 27.64341170 full horizon R2 : -0.01587111 full horizon Pearson r : 0.00412119 full horizon QLIKE : 0.75186128 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GE/Simple_KAN_H10.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=1.42643, max=17.0831 === GE | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238557 Min value: -0.033788504883313815 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.120224031597557 Min value: -2.692943196140936 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4221295497976101 Min value: -0.03368162544886536 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.084344850046585 Min value: -2.4033958725510236 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 0.4422974929765818 Min value: -0.03368162544886536 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.6879422007515705 Min value: -2.183075884328406 Epoch 1: Train mse = 0.9950 | Val mse = 1.0139 Epoch 2: Train mse = 0.9952 | Val mse = 0.9464 Epoch 3: Train mse = 0.9918 | Val mse = 0.9112 Epoch 4: Train mse = 0.9861 | Val mse = 0.9667 Epoch 5: Train mse = 0.9735 | Val mse = 0.8121 Epoch 6: Train mse = 0.9345 | Val mse = 0.9924 Epoch 7: Train mse = 0.8471 | Val mse = 0.9095 Epoch 8: Train mse = 0.8702 | Val mse = 1.3946 Epoch 9: Train mse = 0.8522 | Val mse = 0.5739 Epoch 10: Train mse = 0.8601 | Val mse = 0.7986 Epoch 11: Train mse = 0.8437 | Val mse = 1.0733 Epoch 12: Train mse = 0.8185 | Val mse = 0.6806 Epoch 13: Train mse = 0.7771 | Val mse = 0.9016 Epoch 14: Train mse = 0.7637 | Val mse = 0.5687 Epoch 15: Train mse = 0.7594 | Val mse = 0.9464 Epoch 16: Train mse = 0.7462 | Val mse = 0.7413 Epoch 17: Train mse = 0.7382 | Val mse = 0.5942 Epoch 18: Train mse = 0.7269 | Val mse = 1.0064 Epoch 19: Train mse = 0.7261 | Val mse = 0.9687 Epoch 20: Train mse = 0.7208 | Val mse = 0.6575 Epoch 21: Train mse = 0.6918 | Val mse = 0.6793 Epoch 22: Train mse = 0.6810 | Val mse = 0.8098 Epoch 23: Train mse = 0.6721 | Val mse = 0.6808 Epoch 24: Train mse = 0.6711 | Val mse = 0.5892 Epoch 25: Train mse = 0.7236 | Val mse = 0.5938 Epoch 26: Train mse = 0.8284 | Val mse = 1.1122 Epoch 27: Train mse = 0.7701 | Val mse = 0.5025 Epoch 28: Train mse = 0.7350 | Val mse = 0.8262 Epoch 29: Train mse = 0.7082 | Val mse = 0.5815 Epoch 30: Train mse = 0.6952 | Val mse = 0.7964 Epoch 31: Train mse = 0.6723 | Val mse = 0.6490 Epoch 32: Train mse = 0.6598 | Val mse = 0.6421 Epoch 33: Train mse = 0.6540 | Val mse = 0.6857 Epoch 34: Train mse = 0.6584 | Val mse = 0.6056 Epoch 35: Train mse = 0.6546 | Val mse = 0.6256 Epoch 36: Train mse = 0.6551 | Val mse = 0.5959 Epoch 37: Train mse = 0.6400 | Val mse = 0.6252 Early stopping triggered at epoch 37. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.04923403 1 day(s) RMSE : 27.56547182 1 day(s) R2 : -0.01018301 1 day(s) Pearson r : 0.03514451 1 day(s) QLIKE : 0.67410237 3 day(s) MAE : 4.10956383 3 day(s) RMSE : 27.57387046 3 day(s) R2 : -0.01085683 3 day(s) Pearson r : 0.02069674 3 day(s) QLIKE : 0.68865757 5 day(s) MAE : 4.13914545 5 day(s) RMSE : 27.58162556 5 day(s) R2 : -0.01148344 5 day(s) Pearson r : 0.01110960 5 day(s) QLIKE : 0.69827925 10 day(s) MAE : 4.21873974 10 day(s) RMSE : 27.58894738 10 day(s) R2 : -0.01187202 10 day(s) Pearson r : 0.00180227 10 day(s) QLIKE : 0.70785386 20 day(s) MAE : 4.29949877 20 day(s) RMSE : 27.59103744 20 day(s) R2 : -0.01197179 20 day(s) Pearson r : -0.00881711 20 day(s) QLIKE : 0.71098707 full horizon MAE : 4.29949877 full horizon RMSE : 27.59103744 full horizon R2 : -0.01197179 full horizon Pearson r : -0.00881711 full horizon QLIKE : 0.71098707 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GE/Simple_KAN_H20.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=2.02259, max=12.4522 === BAC | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179488 Min value: -0.42151023550938554 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.10774278654742 Min value: -3.152671373228803 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166767 Min value: -0.36472825885137616 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.2542888447212466 Min value: -1.29240155253667 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787256 Min value: -0.38915813676977 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.207108669727663 Min value: -1.9461535420618146 Epoch 1: Train mse = 0.8978 | Val mse = 0.4862 Epoch 2: Train mse = 0.6703 | Val mse = 0.5709 Epoch 3: Train mse = 0.5363 | Val mse = 0.5123 Epoch 4: Train mse = 0.4982 | Val mse = 0.4936 Epoch 5: Train mse = 0.4467 | Val mse = 0.5356 Epoch 6: Train mse = 0.4204 | Val mse = 0.5009 Epoch 7: Train mse = 0.4047 | Val mse = 0.4902 Epoch 8: Train mse = 0.3820 | Val mse = 0.4955 Epoch 9: Train mse = 0.3728 | Val mse = 0.4614 Epoch 10: Train mse = 0.3750 | Val mse = 0.4587 Epoch 11: Train mse = 0.3576 | Val mse = 0.5482 Epoch 12: Train mse = 0.3616 | Val mse = 0.4538 Epoch 13: Train mse = 0.3497 | Val mse = 0.4734 Epoch 14: Train mse = 0.3317 | Val mse = 0.4995 Epoch 15: Train mse = 0.3171 | Val mse = 0.5033 Epoch 16: Train mse = 0.3102 | Val mse = 0.5365 Epoch 17: Train mse = 0.2975 | Val mse = 0.5563 Epoch 18: Train mse = 0.2860 | Val mse = 0.5437 Epoch 19: Train mse = 0.2777 | Val mse = 0.5784 Epoch 20: Train mse = 0.2746 | Val mse = 0.5386 Epoch 21: Train mse = 0.2567 | Val mse = 0.5776 Epoch 22: Train mse = 0.2494 | Val mse = 0.5938 Epoch 23: Train mse = 0.2427 | Val mse = 0.5651 Epoch 24: Train mse = 0.2156 | Val mse = 0.5514 Epoch 25: Train mse = 0.2145 | Val mse = 0.5837 Epoch 26: Train mse = 0.2208 | Val mse = 0.5175 Epoch 27: Train mse = 0.1894 | Val mse = 0.6090 Epoch 28: Train mse = 0.1766 | Val mse = 0.5857 Epoch 29: Train mse = 0.1776 | Val mse = 0.6595 Epoch 30: Train mse = 0.1796 | Val mse = 0.6054 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.32248551 1 day(s) RMSE : 7.40503532 1 day(s) R2 : 0.09002190 1 day(s) Pearson r : 0.38391358 1 day(s) QLIKE : 0.27749442 full horizon MAE : 2.32248551 full horizon RMSE : 7.40503532 full horizon R2 : 0.09002190 full horizon Pearson r : 0.38391358 full horizon QLIKE : 0.27749442 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BAC/Simple_KAN_H1.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=0.80017, max=56.1842 === BAC | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179488 Min value: -0.42151023550938554 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.106232115580275 Min value: -3.150569092336922 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166767 Min value: -0.36472825885137616 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.253588840112375 Min value: -1.2911129191419919 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787256 Min value: -0.38915813676977 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.205991919409497 Min value: -1.9445789696790647 Epoch 1: Train mse = 0.9229 | Val mse = 0.4848 Epoch 2: Train mse = 0.7946 | Val mse = 0.5313 Epoch 3: Train mse = 0.6608 | Val mse = 0.5518 Epoch 4: Train mse = 0.6019 | Val mse = 0.5543 Epoch 5: Train mse = 0.5634 | Val mse = 0.5792 Epoch 6: Train mse = 0.5244 | Val mse = 0.5126 Epoch 7: Train mse = 0.5315 | Val mse = 0.4872 Epoch 8: Train mse = 0.5102 | Val mse = 0.5598 Epoch 9: Train mse = 0.4969 | Val mse = 0.5416 Epoch 10: Train mse = 0.4916 | Val mse = 0.5363 Epoch 11: Train mse = 0.4810 | Val mse = 0.5590 Epoch 12: Train mse = 0.4741 | Val mse = 0.4990 Epoch 13: Train mse = 0.4657 | Val mse = 0.5009 Epoch 14: Train mse = 0.4567 | Val mse = 0.5197 Epoch 15: Train mse = 0.4521 | Val mse = 0.5224 Epoch 16: Train mse = 0.4457 | Val mse = 0.4972 Epoch 17: Train mse = 0.4383 | Val mse = 0.4982 Epoch 18: Train mse = 0.4282 | Val mse = 0.5100 Epoch 19: Train mse = 0.4204 | Val mse = 0.5303 Epoch 20: Train mse = 0.4127 | Val mse = 0.5008 Epoch 21: Train mse = 0.4018 | Val mse = 0.5166 Epoch 22: Train mse = 0.3990 | Val mse = 0.5108 Epoch 23: Train mse = 0.3883 | Val mse = 0.5325 Epoch 24: Train mse = 0.3789 | Val mse = 0.4969 Epoch 25: Train mse = 0.3847 | Val mse = 0.5318 Epoch 26: Train mse = 0.3898 | Val mse = 0.4996 Epoch 27: Train mse = 0.3729 | Val mse = 0.5484 Epoch 28: Train mse = 0.3580 | Val mse = 0.5838 Epoch 29: Train mse = 0.3552 | Val mse = 0.5467 Epoch 30: Train mse = 0.3385 | Val mse = 0.5054 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.21178126 1 day(s) RMSE : 7.74989853 1 day(s) R2 : 0.00329026 1 day(s) Pearson r : 0.27091580 1 day(s) QLIKE : 0.39295049 3 day(s) MAE : 2.21331913 3 day(s) RMSE : 7.78556445 3 day(s) R2 : -0.00526251 3 day(s) Pearson r : 0.16470286 3 day(s) QLIKE : 0.40670693 5 day(s) MAE : 2.22979695 5 day(s) RMSE : 7.80706778 5 day(s) R2 : -0.01051084 5 day(s) Pearson r : 0.10295889 5 day(s) QLIKE : 0.41657109 full horizon MAE : 2.22979695 full horizon RMSE : 7.80706778 full horizon R2 : -0.01051084 full horizon Pearson r : 0.10295889 full horizon QLIKE : 0.41657109 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BAC/Simple_KAN_H5.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=2.27473, max=7.08089 === BAC | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179488 Min value: -0.42151023550938554 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.1031963312484585 Min value: -3.149203897636293 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166767 Min value: -0.36472825885137616 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.251540537979127 Min value: -1.2907388380002232 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787256 Min value: -0.38915813676977 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.203435974440135 Min value: -1.9438565829024124 Epoch 1: Train mse = 0.9343 | Val mse = 0.4859 Epoch 2: Train mse = 0.8449 | Val mse = 0.5617 Epoch 3: Train mse = 0.7141 | Val mse = 0.5591 Epoch 4: Train mse = 0.6655 | Val mse = 0.5290 Epoch 5: Train mse = 0.6293 | Val mse = 0.6302 Epoch 6: Train mse = 0.6036 | Val mse = 0.5290 Epoch 7: Train mse = 0.5909 | Val mse = 0.5108 Epoch 8: Train mse = 0.5759 | Val mse = 0.5317 Epoch 9: Train mse = 0.5658 | Val mse = 0.5797 Epoch 10: Train mse = 0.5533 | Val mse = 0.5438 Epoch 11: Train mse = 0.5481 | Val mse = 0.5641 Epoch 12: Train mse = 0.5416 | Val mse = 0.5363 Epoch 13: Train mse = 0.5307 | Val mse = 0.5050 Epoch 14: Train mse = 0.5283 | Val mse = 0.5586 Epoch 15: Train mse = 0.5221 | Val mse = 0.5655 Epoch 16: Train mse = 0.5168 | Val mse = 0.5329 Epoch 17: Train mse = 0.5098 | Val mse = 0.5281 Epoch 18: Train mse = 0.4979 | Val mse = 0.5347 Epoch 19: Train mse = 0.4855 | Val mse = 0.5369 Epoch 20: Train mse = 0.4798 | Val mse = 0.5124 Epoch 21: Train mse = 0.4741 | Val mse = 0.5054 Epoch 22: Train mse = 0.4647 | Val mse = 0.4886 Epoch 23: Train mse = 0.4540 | Val mse = 0.5257 Epoch 24: Train mse = 0.4482 | Val mse = 0.5107 Epoch 25: Train mse = 0.4507 | Val mse = 0.5584 Epoch 26: Train mse = 0.4668 | Val mse = 0.5286 Epoch 27: Train mse = 0.4434 | Val mse = 0.6255 Epoch 28: Train mse = 0.4282 | Val mse = 0.5851 Epoch 29: Train mse = 0.4151 | Val mse = 0.5653 Epoch 30: Train mse = 0.4042 | Val mse = 0.5387 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.22964787 1 day(s) RMSE : 7.75387118 1 day(s) R2 : 0.00226816 1 day(s) Pearson r : 0.26805620 1 day(s) QLIKE : 0.39586678 3 day(s) MAE : 2.22744967 3 day(s) RMSE : 7.78856867 3 day(s) R2 : -0.00603846 3 day(s) Pearson r : 0.14812849 3 day(s) QLIKE : 0.40925825 5 day(s) MAE : 2.23816100 5 day(s) RMSE : 7.80838423 5 day(s) R2 : -0.01085166 5 day(s) Pearson r : 0.09417730 5 day(s) QLIKE : 0.41786883 10 day(s) MAE : 2.24588891 10 day(s) RMSE : 7.83285846 10 day(s) R2 : -0.01710181 10 day(s) Pearson r : 0.03276458 10 day(s) QLIKE : 0.42578014 full horizon MAE : 2.24588891 full horizon RMSE : 7.83285846 full horizon R2 : -0.01710181 full horizon Pearson r : 0.03276458 full horizon QLIKE : 0.42578014 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BAC/Simple_KAN_H10.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=2.23526, max=6.88173 === BAC | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179488 Min value: -0.42151023550938554 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.103268362353564 Min value: -3.1487890186062457 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166767 Min value: -0.36472825885137616 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.251689496563368 Min value: -1.290401169341898 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787256 Min value: -0.38915813676977 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.203545386302683 Min value: -1.9434917803124587 Epoch 1: Train mse = 0.9506 | Val mse = 0.4977 Epoch 2: Train mse = 0.8777 | Val mse = 0.5899 Epoch 3: Train mse = 0.7766 | Val mse = 0.5897 Epoch 4: Train mse = 0.7361 | Val mse = 0.5351 Epoch 5: Train mse = 0.7135 | Val mse = 0.5881 Epoch 6: Train mse = 0.6741 | Val mse = 0.5655 Epoch 7: Train mse = 0.6729 | Val mse = 0.5181 Epoch 8: Train mse = 0.6633 | Val mse = 0.5430 Epoch 9: Train mse = 0.6487 | Val mse = 0.5723 Epoch 10: Train mse = 0.6414 | Val mse = 0.5718 Epoch 11: Train mse = 0.6280 | Val mse = 0.5786 Epoch 12: Train mse = 0.6239 | Val mse = 0.5517 Epoch 13: Train mse = 0.6088 | Val mse = 0.5575 Epoch 14: Train mse = 0.6045 | Val mse = 0.5769 Epoch 15: Train mse = 0.5963 | Val mse = 0.5609 Epoch 16: Train mse = 0.5878 | Val mse = 0.5353 Epoch 17: Train mse = 0.5850 | Val mse = 0.5131 Epoch 18: Train mse = 0.5714 | Val mse = 0.5057 Epoch 19: Train mse = 0.5608 | Val mse = 0.5275 Epoch 20: Train mse = 0.5567 | Val mse = 0.4912 Epoch 21: Train mse = 0.5361 | Val mse = 0.5721 Epoch 22: Train mse = 0.5389 | Val mse = 0.5015 Epoch 23: Train mse = 0.5166 | Val mse = 0.5074 Epoch 24: Train mse = 0.4981 | Val mse = 0.5205 Epoch 25: Train mse = 0.4874 | Val mse = 0.5096 Epoch 26: Train mse = 0.4730 | Val mse = 0.5303 Epoch 27: Train mse = 0.4652 | Val mse = 0.5051 Epoch 28: Train mse = 0.4509 | Val mse = 0.5176 Epoch 29: Train mse = 0.4414 | Val mse = 0.4946 Epoch 30: Train mse = 0.4315 | Val mse = 0.5135 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.31864956 1 day(s) RMSE : 7.58831995 1 day(s) R2 : 0.04441805 1 day(s) Pearson r : 0.35499284 1 day(s) QLIKE : 0.33021818 3 day(s) MAE : 2.57927904 3 day(s) RMSE : 8.67198819 3 day(s) R2 : -0.24720152 3 day(s) Pearson r : 0.24534090 3 day(s) QLIKE : 0.39520204 5 day(s) MAE : 2.87147396 5 day(s) RMSE : 9.94869126 5 day(s) R2 : -0.64095618 5 day(s) Pearson r : 0.14366147 5 day(s) QLIKE : 0.43984295 10 day(s) MAE : 3.26391040 10 day(s) RMSE : 11.98434117 10 day(s) R2 : -1.38096053 10 day(s) Pearson r : 0.05481245 10 day(s) QLIKE : 0.48681341 20 day(s) MAE : 3.15080940 20 day(s) RMSE : 10.81349373 20 day(s) R2 : -0.93909450 20 day(s) Pearson r : 0.02708136 20 day(s) QLIKE : 0.48864557 full horizon MAE : 3.15080940 full horizon RMSE : 10.81349373 full horizon R2 : -0.93909450 full horizon Pearson r : 0.02708136 full horizon QLIKE : 0.48864557 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BAC/Simple_KAN_H20.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=1.56557, max=151.614 === C | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.8091406863669 Min value: -0.37248441284063266 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.116803264609633 Min value: -2.5790852911299234 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.018765610501212 Min value: -0.35593837785433546 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.6474166417153135 Min value: -1.663048077620065 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.615585669042774 Min value: -0.3593653801213122 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.113246907180982 Min value: -2.058904827000899 Epoch 1: Train mse = 0.8934 | Val mse = 0.4770 Epoch 2: Train mse = 0.7021 | Val mse = 0.4134 Epoch 3: Train mse = 0.5647 | Val mse = 0.4388 Epoch 4: Train mse = 0.4812 | Val mse = 0.3935 Epoch 5: Train mse = 0.4676 | Val mse = 0.4134 Epoch 6: Train mse = 0.4242 | Val mse = 0.4182 Epoch 7: Train mse = 0.4053 | Val mse = 0.3837 Epoch 8: Train mse = 0.3800 | Val mse = 0.3785 Epoch 9: Train mse = 0.3758 | Val mse = 0.4042 Epoch 10: Train mse = 0.3614 | Val mse = 0.4045 Epoch 11: Train mse = 0.3495 | Val mse = 0.4181 Epoch 12: Train mse = 0.3449 | Val mse = 0.4031 Epoch 13: Train mse = 0.3367 | Val mse = 0.3805 Epoch 14: Train mse = 0.3307 | Val mse = 0.3992 Epoch 15: Train mse = 0.3288 | Val mse = 0.4106 Epoch 16: Train mse = 0.3132 | Val mse = 0.3886 Epoch 17: Train mse = 0.3093 | Val mse = 0.3946 Epoch 18: Train mse = 0.3016 | Val mse = 0.3994 Epoch 19: Train mse = 0.2950 | Val mse = 0.3972 Epoch 20: Train mse = 0.2873 | Val mse = 0.4031 Epoch 21: Train mse = 0.2761 | Val mse = 0.3991 Epoch 22: Train mse = 0.2696 | Val mse = 0.4205 Epoch 23: Train mse = 0.2997 | Val mse = 0.4034 Epoch 24: Train mse = 0.2667 | Val mse = 0.4115 Epoch 25: Train mse = 0.2584 | Val mse = 0.4457 Epoch 26: Train mse = 0.2516 | Val mse = 0.4219 Epoch 27: Train mse = 0.2286 | Val mse = 0.4387 Epoch 28: Train mse = 0.2222 | Val mse = 0.4481 Epoch 29: Train mse = 0.2098 | Val mse = 0.4753 Epoch 30: Train mse = 0.1959 | Val mse = 0.4796 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.69971305 1 day(s) RMSE : 8.42544081 1 day(s) R2 : 0.20324468 1 day(s) Pearson r : 0.46558478 1 day(s) QLIKE : 0.28112754 full horizon MAE : 2.69971305 full horizon RMSE : 8.42544081 full horizon R2 : 0.20324468 full horizon Pearson r : 0.46558478 full horizon QLIKE : 0.28112754 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/C/Simple_KAN_H1.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=1.53587, max=39.3556 === C | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.8091406863669 Min value: -0.37248441284063266 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.113808938949674 Min value: -2.5779301635708127 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.018765610501212 Min value: -0.35593837785433546 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.6457537548096046 Min value: -1.6623868571045473 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.615585669042774 Min value: -0.3593653801213122 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.110793676954993 Min value: -2.05803016926483 Epoch 1: Train mse = 0.9145 | Val mse = 0.4511 Epoch 2: Train mse = 0.7911 | Val mse = 0.4429 Epoch 3: Train mse = 0.6296 | Val mse = 0.4850 Epoch 4: Train mse = 0.5652 | Val mse = 0.4610 Epoch 5: Train mse = 0.5333 | Val mse = 0.4873 Epoch 6: Train mse = 0.4942 | Val mse = 0.4365 Epoch 7: Train mse = 0.4924 | Val mse = 0.4056 Epoch 8: Train mse = 0.4796 | Val mse = 0.4272 Epoch 9: Train mse = 0.4636 | Val mse = 0.4503 Epoch 10: Train mse = 0.4537 | Val mse = 0.4408 Epoch 11: Train mse = 0.4469 | Val mse = 0.4622 Epoch 12: Train mse = 0.4418 | Val mse = 0.4640 Epoch 13: Train mse = 0.4339 | Val mse = 0.4271 Epoch 14: Train mse = 0.4291 | Val mse = 0.4318 Epoch 15: Train mse = 0.4237 | Val mse = 0.4523 Epoch 16: Train mse = 0.4182 | Val mse = 0.4423 Epoch 17: Train mse = 0.4132 | Val mse = 0.4389 Epoch 18: Train mse = 0.4071 | Val mse = 0.4372 Epoch 19: Train mse = 0.4021 | Val mse = 0.4270 Epoch 20: Train mse = 0.3983 | Val mse = 0.4397 Epoch 21: Train mse = 0.3996 | Val mse = 0.4337 Epoch 22: Train mse = 0.3856 | Val mse = 0.4485 Epoch 23: Train mse = 0.3824 | Val mse = 0.4418 Epoch 24: Train mse = 0.3710 | Val mse = 0.4582 Epoch 25: Train mse = 0.3753 | Val mse = 0.4567 Epoch 26: Train mse = 0.3766 | Val mse = 0.4451 Epoch 27: Train mse = 0.3697 | Val mse = 0.4480 Epoch 28: Train mse = 0.3598 | Val mse = 0.5210 Epoch 29: Train mse = 0.3576 | Val mse = 0.4584 Epoch 30: Train mse = 0.3352 | Val mse = 0.4600 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.63411755 1 day(s) RMSE : 8.64160279 1 day(s) R2 : 0.16183734 1 day(s) Pearson r : 0.46474565 1 day(s) QLIKE : 0.29384145 3 day(s) MAE : 2.76023733 3 day(s) RMSE : 8.92380650 3 day(s) R2 : 0.11055911 3 day(s) Pearson r : 0.36610962 3 day(s) QLIKE : 0.33343703 5 day(s) MAE : 2.86213265 5 day(s) RMSE : 9.19555339 5 day(s) R2 : 0.05857048 5 day(s) Pearson r : 0.27734426 5 day(s) QLIKE : 0.36911791 full horizon MAE : 2.86213265 full horizon RMSE : 9.19555339 full horizon R2 : 0.05857048 full horizon Pearson r : 0.27734426 full horizon QLIKE : 0.36911791 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/C/Simple_KAN_H5.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=1.8951, max=33.1391 === C | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.8091406863669 Min value: -0.37248441284063266 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.111052537752696 Min value: -2.577782485226596 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.018765610501212 Min value: -0.35593837785433546 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.6439291882044227 Min value: -1.662584849687275 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.615585669042774 Min value: -0.3593653801213122 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.1084159724432565 Min value: -2.0580787834517045 Epoch 1: Train mse = 0.9255 | Val mse = 0.4429 Epoch 2: Train mse = 0.8107 | Val mse = 0.4726 Epoch 3: Train mse = 0.6696 | Val mse = 0.5076 Epoch 4: Train mse = 0.6173 | Val mse = 0.4688 Epoch 5: Train mse = 0.5826 | Val mse = 0.5283 Epoch 6: Train mse = 0.5585 | Val mse = 0.4594 Epoch 7: Train mse = 0.5401 | Val mse = 0.4350 Epoch 8: Train mse = 0.5290 | Val mse = 0.4529 Epoch 9: Train mse = 0.5192 | Val mse = 0.4883 Epoch 10: Train mse = 0.5077 | Val mse = 0.4624 Epoch 11: Train mse = 0.4996 | Val mse = 0.4940 Epoch 12: Train mse = 0.4966 | Val mse = 0.4758 Epoch 13: Train mse = 0.4857 | Val mse = 0.4617 Epoch 14: Train mse = 0.4825 | Val mse = 0.4810 Epoch 15: Train mse = 0.4748 | Val mse = 0.5032 Epoch 16: Train mse = 0.4687 | Val mse = 0.4697 Epoch 17: Train mse = 0.4664 | Val mse = 0.4634 Epoch 18: Train mse = 0.4594 | Val mse = 0.4631 Epoch 19: Train mse = 0.4548 | Val mse = 0.4696 Epoch 20: Train mse = 0.4469 | Val mse = 0.4658 Epoch 21: Train mse = 0.4468 | Val mse = 0.4658 Epoch 22: Train mse = 0.4363 | Val mse = 0.4737 Epoch 23: Train mse = 0.4282 | Val mse = 0.4809 Epoch 24: Train mse = 0.4204 | Val mse = 0.5014 Epoch 25: Train mse = 0.4296 | Val mse = 0.4796 Epoch 26: Train mse = 0.4359 | Val mse = 0.5006 Epoch 27: Train mse = 0.4317 | Val mse = 0.5318 Epoch 28: Train mse = 0.4155 | Val mse = 0.5049 Epoch 29: Train mse = 0.4037 | Val mse = 0.4711 Epoch 30: Train mse = 0.3969 | Val mse = 0.4988 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.61040396 1 day(s) RMSE : 8.60602532 1 day(s) R2 : 0.16872457 1 day(s) Pearson r : 0.45305860 1 day(s) QLIKE : 0.29901361 3 day(s) MAE : 2.74730117 3 day(s) RMSE : 8.93349626 3 day(s) R2 : 0.10862649 3 day(s) Pearson r : 0.36409745 3 day(s) QLIKE : 0.34102152 5 day(s) MAE : 2.85207451 5 day(s) RMSE : 9.21651563 5 day(s) R2 : 0.05427341 5 day(s) Pearson r : 0.28042244 5 day(s) QLIKE : 0.37606638 10 day(s) MAE : 3.01836111 10 day(s) RMSE : 9.55594375 10 day(s) R2 : -0.01429516 10 day(s) Pearson r : 0.16180151 10 day(s) QLIKE : 0.42127070 full horizon MAE : 3.01836111 full horizon RMSE : 9.55594375 full horizon R2 : -0.01429516 full horizon Pearson r : 0.16180151 full horizon QLIKE : 0.42127070 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/C/Simple_KAN_H10.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=1.66803, max=39.8535 === C | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.8091406863669 Min value: -0.37248441284063266 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.111456003225553 Min value: -2.5798794252140986 Checking X_price_val: Shape: (161, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.018765610501212 Min value: -0.35593837785433546 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.643530346347736 Min value: -1.664384167842813 Checking X_price_test: Shape: (404, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.615585669042774 Min value: -0.3593653801213122 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.1084933809861415 Min value: -2.0600067160447444 Epoch 1: Train mse = 0.9430 | Val mse = 0.4421 Epoch 2: Train mse = 0.8323 | Val mse = 0.4982 Epoch 3: Train mse = 0.7310 | Val mse = 0.5381 Epoch 4: Train mse = 0.6875 | Val mse = 0.4780 Epoch 5: Train mse = 0.6672 | Val mse = 0.5002 Epoch 6: Train mse = 0.6252 | Val mse = 0.5065 Epoch 7: Train mse = 0.6250 | Val mse = 0.4756 Epoch 8: Train mse = 0.6208 | Val mse = 0.4754 Epoch 9: Train mse = 0.6014 | Val mse = 0.5105 Epoch 10: Train mse = 0.5933 | Val mse = 0.5182 Epoch 11: Train mse = 0.5836 | Val mse = 0.5187 Epoch 12: Train mse = 0.5837 | Val mse = 0.5041 Epoch 13: Train mse = 0.5667 | Val mse = 0.5189 Epoch 14: Train mse = 0.5694 | Val mse = 0.5423 Epoch 15: Train mse = 0.5603 | Val mse = 0.5298 Epoch 16: Train mse = 0.5554 | Val mse = 0.5260 Epoch 17: Train mse = 0.5499 | Val mse = 0.5366 Epoch 18: Train mse = 0.5516 | Val mse = 0.5128 Epoch 19: Train mse = 0.5412 | Val mse = 0.5055 Epoch 20: Train mse = 0.5353 | Val mse = 0.5497 Epoch 21: Train mse = 0.5370 | Val mse = 0.5317 Epoch 22: Train mse = 0.5241 | Val mse = 0.5167 Epoch 23: Train mse = 0.5202 | Val mse = 0.5362 Epoch 24: Train mse = 0.5095 | Val mse = 0.5607 Epoch 25: Train mse = 0.5063 | Val mse = 0.5295 Epoch 26: Train mse = 0.5099 | Val mse = 0.5136 Epoch 27: Train mse = 0.5000 | Val mse = 0.5654 Epoch 28: Train mse = 0.4967 | Val mse = 0.4970 Epoch 29: Train mse = 0.4977 | Val mse = 0.5396 Epoch 30: Train mse = 0.4881 | Val mse = 0.5073 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.77421305 1 day(s) RMSE : 9.49832408 1 day(s) R2 : -0.01259009 1 day(s) Pearson r : 0.32541167 1 day(s) QLIKE : 0.40150704 3 day(s) MAE : 2.78338282 3 day(s) RMSE : 9.56612407 3 day(s) R2 : -0.02208925 3 day(s) Pearson r : 0.19829641 3 day(s) QLIKE : 0.40910354 5 day(s) MAE : 2.81320025 5 day(s) RMSE : 9.60148133 5 day(s) R2 : -0.02638089 5 day(s) Pearson r : 0.15886653 5 day(s) QLIKE : 0.41772220 10 day(s) MAE : 2.84345267 10 day(s) RMSE : 9.64550828 10 day(s) R2 : -0.03339753 10 day(s) Pearson r : 0.08866893 10 day(s) QLIKE : 0.42674628 20 day(s) MAE : 2.86892442 20 day(s) RMSE : 9.66627907 20 day(s) R2 : -0.03703086 20 day(s) Pearson r : 0.04622948 20 day(s) QLIKE : 0.42970293 full horizon MAE : 2.86892442 full horizon RMSE : 9.66627907 full horizon R2 : -0.03703086 full horizon Pearson r : 0.04622948 full horizon QLIKE : 0.42970293 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/C/Simple_KAN_H20.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=2.48092, max=6.95947 === BTCUSDT | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2399 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514245807 Min value: -0.39529821705307827 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.551349524903821 Min value: -3.6053341677678 Checking X_price_val: Shape: (192, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.1802886452741648 Min value: -0.3973706893292718 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.344819091237707 Min value: -4.24132665249135 Checking X_price_test: Shape: (480, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.92735513679459 Min value: -0.3866725144992877 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.58025865739623 Min value: -2.56748600221835 Epoch 1: Train mse = 0.9235 | Val mse = 1.2028 Epoch 2: Train mse = 0.6479 | Val mse = 0.9519 Epoch 3: Train mse = 0.5070 | Val mse = 0.8800 Epoch 4: Train mse = 0.4900 | Val mse = 0.8065 Epoch 5: Train mse = 0.4271 | Val mse = 0.8932 Epoch 6: Train mse = 0.4173 | Val mse = 0.7495 Epoch 7: Train mse = 0.3613 | Val mse = 0.7307 Epoch 8: Train mse = 0.3369 | Val mse = 0.7459 Epoch 9: Train mse = 0.3381 | Val mse = 0.7291 Epoch 10: Train mse = 0.3129 | Val mse = 0.7202 Epoch 11: Train mse = 0.3046 | Val mse = 0.7151 Epoch 12: Train mse = 0.2799 | Val mse = 0.7070 Epoch 13: Train mse = 0.2641 | Val mse = 0.7117 Epoch 14: Train mse = 0.2419 | Val mse = 0.7241 Epoch 15: Train mse = 0.2147 | Val mse = 0.7431 Epoch 16: Train mse = 0.2038 | Val mse = 0.7817 Epoch 17: Train mse = 0.1931 | Val mse = 0.8258 Epoch 18: Train mse = 0.2053 | Val mse = 0.7863 Epoch 19: Train mse = 0.1851 | Val mse = 0.7572 Epoch 20: Train mse = 0.1786 | Val mse = 0.7703 Epoch 21: Train mse = 0.1487 | Val mse = 0.8596 Epoch 22: Train mse = 0.1359 | Val mse = 0.8523 Epoch 23: Train mse = 0.1080 | Val mse = 0.9304 Epoch 24: Train mse = 0.0964 | Val mse = 0.9180 Epoch 25: Train mse = 0.0865 | Val mse = 0.9767 Epoch 26: Train mse = 0.0717 | Val mse = 0.9699 Epoch 27: Train mse = 0.0667 | Val mse = 0.9687 Epoch 28: Train mse = 0.0649 | Val mse = 0.9666 Epoch 29: Train mse = 0.0665 | Val mse = 1.0709 Epoch 30: Train mse = 0.0489 | Val mse = 1.1359 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 3.93152107 1 day(s) RMSE : 8.65436862 1 day(s) R2 : 0.11238883 1 day(s) Pearson r : 0.41053959 1 day(s) QLIKE : 0.24344526 full horizon MAE : 3.93152107 full horizon RMSE : 8.65436862 full horizon R2 : 0.11238883 full horizon Pearson r : 0.41053959 full horizon QLIKE : 0.24344526 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BTCUSDT/Simple_KAN_H1.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=0.778153, max=68.0627 === BTCUSDT | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2399 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514245807 Min value: -0.39529821705307827 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.554196812284146 Min value: -3.6087233634991427 Checking X_price_val: Shape: (192, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.1802886452741648 Min value: -0.3973706893292718 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3459792971639226 Min value: -4.245202118935102 Checking X_price_test: Shape: (480, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.92735513679459 Min value: -0.3866725144992877 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.58159887703065 Min value: -2.5700816741597805 Epoch 1: Train mse = 0.9471 | Val mse = 1.4447 Epoch 2: Train mse = 0.7659 | Val mse = 1.1271 Epoch 3: Train mse = 0.6611 | Val mse = 1.0491 Epoch 4: Train mse = 0.6029 | Val mse = 0.9739 Epoch 5: Train mse = 0.5672 | Val mse = 1.0189 Epoch 6: Train mse = 0.5551 | Val mse = 0.9822 Epoch 7: Train mse = 0.5193 | Val mse = 0.9328 Epoch 8: Train mse = 0.4999 | Val mse = 0.9773 Epoch 9: Train mse = 0.4910 | Val mse = 0.9698 Epoch 10: Train mse = 0.4829 | Val mse = 1.0214 Epoch 11: Train mse = 0.4653 | Val mse = 1.0571 Epoch 12: Train mse = 0.4379 | Val mse = 1.1345 Epoch 13: Train mse = 0.4166 | Val mse = 1.0971 Epoch 14: Train mse = 0.3973 | Val mse = 1.0834 Epoch 15: Train mse = 0.3717 | Val mse = 1.0841 Epoch 16: Train mse = 0.3666 | Val mse = 1.0910 Epoch 17: Train mse = 0.3524 | Val mse = 1.0773 Epoch 18: Train mse = 0.3468 | Val mse = 1.0857 Epoch 19: Train mse = 0.3241 | Val mse = 1.0912 Epoch 20: Train mse = 0.2962 | Val mse = 1.2733 Epoch 21: Train mse = 0.2741 | Val mse = 1.1980 Epoch 22: Train mse = 0.2535 | Val mse = 1.3270 Epoch 23: Train mse = 0.2422 | Val mse = 1.0998 Epoch 24: Train mse = 0.2295 | Val mse = 1.2154 Epoch 25: Train mse = 0.2095 | Val mse = 1.2052 Epoch 26: Train mse = 0.1952 | Val mse = 1.1485 Epoch 27: Train mse = 0.1905 | Val mse = 1.1731 Epoch 28: Train mse = 0.1788 | Val mse = 1.3124 Epoch 29: Train mse = 0.1771 | Val mse = 1.2596 Epoch 30: Train mse = 0.1661 | Val mse = 1.3182 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.17566045 1 day(s) RMSE : 8.82504886 1 day(s) R2 : 0.07703290 1 day(s) Pearson r : 0.33279890 1 day(s) QLIKE : 0.30199962 3 day(s) MAE : 4.30681087 3 day(s) RMSE : 9.00230834 3 day(s) R2 : 0.03971627 3 day(s) Pearson r : 0.28154969 3 day(s) QLIKE : 0.32797879 5 day(s) MAE : 4.44853271 5 day(s) RMSE : 9.15721920 5 day(s) R2 : 0.00607141 5 day(s) Pearson r : 0.23119251 5 day(s) QLIKE : 0.34783235 full horizon MAE : 4.44853271 full horizon RMSE : 9.15721920 full horizon R2 : 0.00607141 full horizon Pearson r : 0.23119251 full horizon QLIKE : 0.34783235 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BTCUSDT/Simple_KAN_H5.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=2.33678, max=41.7041 === BTCUSDT | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2399 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514245807 Min value: -0.39529821705307827 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.556915178048552 Min value: -3.6122923012225283 Checking X_price_val: Shape: (192, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.1802886452741648 Min value: -0.3973706893292718 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.346996833643509 Min value: -4.249261289934421 Checking X_price_test: Shape: (480, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.92735513679459 Min value: -0.3866725144992877 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.582797894129229 Min value: -2.572850621760107 Epoch 1: Train mse = 0.9606 | Val mse = 1.4663 Epoch 2: Train mse = 0.8049 | Val mse = 1.2462 Epoch 3: Train mse = 0.7338 | Val mse = 1.0203 Epoch 4: Train mse = 0.6791 | Val mse = 0.9961 Epoch 5: Train mse = 0.6559 | Val mse = 1.0150 Epoch 6: Train mse = 0.6332 | Val mse = 1.0387 Epoch 7: Train mse = 0.6097 | Val mse = 1.0696 Epoch 8: Train mse = 0.5961 | Val mse = 1.0964 Epoch 9: Train mse = 0.5758 | Val mse = 1.0641 Epoch 10: Train mse = 0.5552 | Val mse = 1.1807 Epoch 11: Train mse = 0.5402 | Val mse = 1.2505 Epoch 12: Train mse = 0.5132 | Val mse = 1.2964 Epoch 13: Train mse = 0.4946 | Val mse = 1.2988 Epoch 14: Train mse = 0.4649 | Val mse = 1.4752 Epoch 15: Train mse = 0.4431 | Val mse = 1.3908 Epoch 16: Train mse = 0.4260 | Val mse = 1.4352 Epoch 17: Train mse = 0.4048 | Val mse = 1.3286 Epoch 18: Train mse = 0.4080 | Val mse = 1.4703 Epoch 19: Train mse = 0.3786 | Val mse = 1.2405 Epoch 20: Train mse = 0.3638 | Val mse = 1.6585 Epoch 21: Train mse = 0.3406 | Val mse = 1.3534 Epoch 22: Train mse = 0.3240 | Val mse = 1.7585 Epoch 23: Train mse = 0.3124 | Val mse = 1.3704 Epoch 24: Train mse = 0.2972 | Val mse = 1.3470 Epoch 25: Train mse = 0.2824 | Val mse = 1.5780 Epoch 26: Train mse = 0.2673 | Val mse = 1.5686 Epoch 27: Train mse = 0.2560 | Val mse = 1.6141 Epoch 28: Train mse = 0.2466 | Val mse = 1.2822 Epoch 29: Train mse = 0.2441 | Val mse = 1.4795 Epoch 30: Train mse = 0.2285 | Val mse = 1.4773 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.20146813 1 day(s) RMSE : 9.00430609 1 day(s) R2 : 0.03915689 1 day(s) Pearson r : 0.30098692 1 day(s) QLIKE : 0.31415881 3 day(s) MAE : 4.33727568 3 day(s) RMSE : 9.17077969 3 day(s) R2 : 0.00343800 3 day(s) Pearson r : 0.23962553 3 day(s) QLIKE : 0.34502595 5 day(s) MAE : 4.49111390 5 day(s) RMSE : 9.30045268 5 day(s) R2 : -0.02526502 5 day(s) Pearson r : 0.18662915 5 day(s) QLIKE : 0.36325883 10 day(s) MAE : 4.64775187 10 day(s) RMSE : 9.41256698 10 day(s) R2 : -0.04987986 10 day(s) Pearson r : 0.12390179 10 day(s) QLIKE : 0.38363134 full horizon MAE : 4.64775187 full horizon RMSE : 9.41256698 full horizon R2 : -0.04987986 full horizon Pearson r : 0.12390179 full horizon QLIKE : 0.38363134 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BTCUSDT/Simple_KAN_H10.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=2.7579, max=36.6398 === BTCUSDT | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2399 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514245807 Min value: -0.39529821705307827 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.564043013270673 Min value: -3.62021477149946 Checking X_price_val: Shape: (192, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.1802886452741648 Min value: -0.3973706893292718 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3500532890780983 Min value: -4.258357261790038 Checking X_price_test: Shape: (480, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.92735513679459 Min value: -0.3866725144992877 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.5862887708961586 Min value: -2.578858106420128 Epoch 1: Train mse = 0.9760 | Val mse = 1.5198 Epoch 2: Train mse = 0.8611 | Val mse = 1.3277 Epoch 3: Train mse = 0.8050 | Val mse = 1.0892 Epoch 4: Train mse = 0.7589 | Val mse = 1.1437 Epoch 5: Train mse = 0.7512 | Val mse = 1.0430 Epoch 6: Train mse = 0.7280 | Val mse = 1.0495 Epoch 7: Train mse = 0.7134 | Val mse = 1.0538 Epoch 8: Train mse = 0.7029 | Val mse = 1.1117 Epoch 9: Train mse = 0.6700 | Val mse = 1.0547 Epoch 10: Train mse = 0.6550 | Val mse = 1.1146 Epoch 11: Train mse = 0.6326 | Val mse = 1.2623 Epoch 12: Train mse = 0.6027 | Val mse = 1.3657 Epoch 13: Train mse = 0.5736 | Val mse = 1.2920 Epoch 14: Train mse = 0.5476 | Val mse = 1.6908 Epoch 15: Train mse = 0.5267 | Val mse = 1.5492 Epoch 16: Train mse = 0.5006 | Val mse = 1.5376 Epoch 17: Train mse = 0.4723 | Val mse = 1.5060 Epoch 18: Train mse = 0.4732 | Val mse = 1.7684 Epoch 19: Train mse = 0.4547 | Val mse = 1.3398 Epoch 20: Train mse = 0.4454 | Val mse = 1.5203 Epoch 21: Train mse = 0.4259 | Val mse = 1.5122 Epoch 22: Train mse = 0.4093 | Val mse = 1.8656 Epoch 23: Train mse = 0.3950 | Val mse = 1.3993 Epoch 24: Train mse = 0.3848 | Val mse = 1.5856 Epoch 25: Train mse = 0.3700 | Val mse = 1.7324 Epoch 26: Train mse = 0.3593 | Val mse = 1.5301 Epoch 27: Train mse = 0.3503 | Val mse = 1.5422 Epoch 28: Train mse = 0.3338 | Val mse = 1.7733 Epoch 29: Train mse = 0.3236 | Val mse = 1.4796 Epoch 30: Train mse = 0.3132 | Val mse = 1.6644 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.28724831 1 day(s) RMSE : 8.99237870 1 day(s) R2 : 0.04170073 1 day(s) Pearson r : 0.28503098 1 day(s) QLIKE : 0.32748216 3 day(s) MAE : 4.43561079 3 day(s) RMSE : 9.14311266 3 day(s) R2 : 0.00944192 3 day(s) Pearson r : 0.23069162 3 day(s) QLIKE : 0.35529200 5 day(s) MAE : 4.55708916 5 day(s) RMSE : 9.27004665 5 day(s) R2 : -0.01857216 5 day(s) Pearson r : 0.18132558 5 day(s) QLIKE : 0.36918408 10 day(s) MAE : 4.70943971 10 day(s) RMSE : 9.39026995 10 day(s) R2 : -0.04491172 10 day(s) Pearson r : 0.11929866 10 day(s) QLIKE : 0.38873036 20 day(s) MAE : 4.84299542 20 day(s) RMSE : 9.45338177 20 day(s) R2 : -0.05937868 20 day(s) Pearson r : 0.05865985 20 day(s) QLIKE : 0.40306223 full horizon MAE : 4.84299542 full horizon RMSE : 9.45338177 full horizon R2 : -0.05937868 full horizon Pearson r : 0.05865985 full horizon QLIKE : 0.40306223 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BTCUSDT/Simple_KAN_H20.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=2.31328, max=38.209 === EURUSD | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525025 Min value: -0.7148707238463011 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9963965101732293 Min value: -3.718602223017603 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.408921165171427 Min value: -0.7103978753601863 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.319059579642504 Min value: -2.975928211620611 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144182 Min value: -0.7173043382357188 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3834322546316056 Min value: -5.349969967608034 Epoch 1: Train mse = 0.8552 | Val mse = 0.5148 Epoch 2: Train mse = 0.4654 | Val mse = 0.5382 Epoch 3: Train mse = 0.3364 | Val mse = 0.4245 Epoch 4: Train mse = 0.3098 | Val mse = 0.3963 Epoch 5: Train mse = 0.2670 | Val mse = 0.3614 Epoch 6: Train mse = 0.2696 | Val mse = 0.3310 Epoch 7: Train mse = 0.2246 | Val mse = 0.3302 Epoch 8: Train mse = 0.2088 | Val mse = 0.3298 Epoch 9: Train mse = 0.1922 | Val mse = 0.3256 Epoch 10: Train mse = 0.1692 | Val mse = 0.3253 Epoch 11: Train mse = 0.1390 | Val mse = 0.3361 Epoch 12: Train mse = 0.1196 | Val mse = 0.3468 Epoch 13: Train mse = 0.1091 | Val mse = 0.3398 Epoch 14: Train mse = 0.1056 | Val mse = 0.3715 Epoch 15: Train mse = 0.0949 | Val mse = 0.3587 Epoch 16: Train mse = 0.0845 | Val mse = 0.3576 Epoch 17: Train mse = 0.0738 | Val mse = 0.3568 Epoch 18: Train mse = 0.0680 | Val mse = 0.3751 Epoch 19: Train mse = 0.0606 | Val mse = 0.3882 Epoch 20: Train mse = 0.0543 | Val mse = 0.3840 Epoch 21: Train mse = 0.0454 | Val mse = 0.3846 Epoch 22: Train mse = 0.0406 | Val mse = 0.3861 Epoch 23: Train mse = 0.0383 | Val mse = 0.3884 Epoch 24: Train mse = 0.0332 | Val mse = 0.3752 Epoch 25: Train mse = 0.0279 | Val mse = 0.3594 Epoch 26: Train mse = 0.0250 | Val mse = 0.3621 Epoch 27: Train mse = 0.0291 | Val mse = 0.3533 Epoch 28: Train mse = 0.0263 | Val mse = 0.3678 Epoch 29: Train mse = 0.0236 | Val mse = 0.3498 Epoch 30: Train mse = 0.0172 | Val mse = 0.3707 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.08409319 1 day(s) RMSE : 0.17190700 1 day(s) R2 : 0.35254521 1 day(s) Pearson r : 0.61482931 1 day(s) QLIKE : 0.41026098 full horizon MAE : 0.08409319 full horizon RMSE : 0.17190700 full horizon R2 : 0.35254521 full horizon Pearson r : 0.61482931 full horizon QLIKE : 0.41026098 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/EURUSD/Simple_KAN_H1.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00386759, max=0.856328 === EURUSD | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525025 Min value: -0.7148707238463011 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9970127449111077 Min value: -3.720451582171488 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.408921165171427 Min value: -0.7103978753601863 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.319427111757353 Min value: -2.9775048778703237 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144182 Min value: -0.7173043382357188 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3838234229198263 Min value: -5.352418327671095 Epoch 1: Train mse = 0.9217 | Val mse = 0.8865 Epoch 2: Train mse = 0.7172 | Val mse = 0.6306 Epoch 3: Train mse = 0.5584 | Val mse = 0.5639 Epoch 4: Train mse = 0.4251 | Val mse = 0.4026 Epoch 5: Train mse = 0.3270 | Val mse = 0.3714 Epoch 6: Train mse = 0.2945 | Val mse = 0.3421 Epoch 7: Train mse = 0.2594 | Val mse = 0.3323 Epoch 8: Train mse = 0.2442 | Val mse = 0.3153 Epoch 9: Train mse = 0.2180 | Val mse = 0.3116 Epoch 10: Train mse = 0.2085 | Val mse = 0.3116 Epoch 11: Train mse = 0.1974 | Val mse = 0.3127 Epoch 12: Train mse = 0.1815 | Val mse = 0.3200 Epoch 13: Train mse = 0.1683 | Val mse = 0.3293 Epoch 14: Train mse = 0.1589 | Val mse = 0.3403 Epoch 15: Train mse = 0.1473 | Val mse = 0.3391 Epoch 16: Train mse = 0.1354 | Val mse = 0.3399 Epoch 17: Train mse = 0.1252 | Val mse = 0.3520 Epoch 18: Train mse = 0.1165 | Val mse = 0.3392 Epoch 19: Train mse = 0.1083 | Val mse = 0.3560 Epoch 20: Train mse = 0.1021 | Val mse = 0.3540 Epoch 21: Train mse = 0.0951 | Val mse = 0.3571 Epoch 22: Train mse = 0.0890 | Val mse = 0.3592 Epoch 23: Train mse = 0.0828 | Val mse = 0.3492 Epoch 24: Train mse = 0.0805 | Val mse = 0.3615 Epoch 25: Train mse = 0.0722 | Val mse = 0.3712 Epoch 26: Train mse = 0.0671 | Val mse = 0.3714 Epoch 27: Train mse = 0.0602 | Val mse = 0.3804 Epoch 28: Train mse = 0.0548 | Val mse = 0.3728 Epoch 29: Train mse = 0.0522 | Val mse = 0.3714 Epoch 30: Train mse = 0.0488 | Val mse = 0.3764 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.08703391 1 day(s) RMSE : 0.16893710 1 day(s) R2 : 0.37472306 1 day(s) Pearson r : 0.62124690 1 day(s) QLIKE : 0.37428720 3 day(s) MAE : 0.08887696 3 day(s) RMSE : 0.17914464 3 day(s) R2 : 0.29145470 3 day(s) Pearson r : 0.55767703 3 day(s) QLIKE : 0.39093673 5 day(s) MAE : 0.09015884 5 day(s) RMSE : 0.18061165 5 day(s) R2 : 0.27161871 5 day(s) Pearson r : 0.53882474 5 day(s) QLIKE : 0.42595533 full horizon MAE : 0.09015884 full horizon RMSE : 0.18061165 full horizon R2 : 0.27161871 full horizon Pearson r : 0.53882474 full horizon QLIKE : 0.42595533 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/EURUSD/Simple_KAN_H5.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00315231, max=1.03881 === EURUSD | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525025 Min value: -0.7148707238463011 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.997326788566986 Min value: -3.719996871136433 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.408921165171427 Min value: -0.7103978753601863 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.319755344427202 Min value: -2.977065724545838 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144182 Min value: -0.7173043382357188 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.384150307095678 Min value: -5.351929442359286 Epoch 1: Train mse = 0.9301 | Val mse = 0.9332 Epoch 2: Train mse = 0.7702 | Val mse = 0.7102 Epoch 3: Train mse = 0.6160 | Val mse = 0.6323 Epoch 4: Train mse = 0.4921 | Val mse = 0.4819 Epoch 5: Train mse = 0.3856 | Val mse = 0.3959 Epoch 6: Train mse = 0.3316 | Val mse = 0.3693 Epoch 7: Train mse = 0.2890 | Val mse = 0.3461 Epoch 8: Train mse = 0.2733 | Val mse = 0.3035 Epoch 9: Train mse = 0.2455 | Val mse = 0.3194 Epoch 10: Train mse = 0.2372 | Val mse = 0.3167 Epoch 11: Train mse = 0.2316 | Val mse = 0.2999 Epoch 12: Train mse = 0.2178 | Val mse = 0.3152 Epoch 13: Train mse = 0.2050 | Val mse = 0.3142 Epoch 14: Train mse = 0.2016 | Val mse = 0.3234 Epoch 15: Train mse = 0.1880 | Val mse = 0.3236 Epoch 16: Train mse = 0.1796 | Val mse = 0.3334 Epoch 17: Train mse = 0.1725 | Val mse = 0.3344 Epoch 18: Train mse = 0.1686 | Val mse = 0.3447 Epoch 19: Train mse = 0.1642 | Val mse = 0.3279 Epoch 20: Train mse = 0.1547 | Val mse = 0.3394 Epoch 21: Train mse = 0.1495 | Val mse = 0.3366 Epoch 22: Train mse = 0.1457 | Val mse = 0.3446 Epoch 23: Train mse = 0.1377 | Val mse = 0.3410 Epoch 24: Train mse = 0.1359 | Val mse = 0.3438 Epoch 25: Train mse = 0.1269 | Val mse = 0.3437 Epoch 26: Train mse = 0.1203 | Val mse = 0.3566 Epoch 27: Train mse = 0.1157 | Val mse = 0.3580 Epoch 28: Train mse = 0.1066 | Val mse = 0.3642 Epoch 29: Train mse = 0.1052 | Val mse = 0.3533 Epoch 30: Train mse = 0.1000 | Val mse = 0.3636 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.08865974 1 day(s) RMSE : 0.16836132 1 day(s) R2 : 0.37897802 1 day(s) Pearson r : 0.62025950 1 day(s) QLIKE : 0.40133784 3 day(s) MAE : 0.09022202 3 day(s) RMSE : 0.17521972 3 day(s) R2 : 0.32216196 3 day(s) Pearson r : 0.57517456 3 day(s) QLIKE : 0.40000221 5 day(s) MAE : 0.09250928 5 day(s) RMSE : 0.17898423 5 day(s) R2 : 0.28468583 5 day(s) Pearson r : 0.54721257 5 day(s) QLIKE : 0.40472232 10 day(s) MAE : 0.09469696 10 day(s) RMSE : 0.18440835 10 day(s) R2 : 0.23342555 10 day(s) Pearson r : 0.50536975 10 day(s) QLIKE : 0.42423480 full horizon MAE : 0.09469696 full horizon RMSE : 0.18440835 full horizon R2 : 0.23342555 full horizon Pearson r : 0.50536975 full horizon QLIKE : 0.42423480 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/EURUSD/Simple_KAN_H10.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00389452, max=1.1875 === EURUSD | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525025 Min value: -0.7148707238463011 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.998957169490066 Min value: -3.7200073694899434 Checking X_price_val: Shape: (302, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.408921165171427 Min value: -0.7103978753601863 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3212202110774225 Min value: -2.9768947428361368 Checking X_price_test: Shape: (757, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144182 Min value: -0.7173043382357188 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737162 Min value: -5.3523385822659755 Epoch 1: Train mse = 0.9342 | Val mse = 0.9623 Epoch 2: Train mse = 0.7896 | Val mse = 0.7242 Epoch 3: Train mse = 0.6343 | Val mse = 0.6767 Epoch 4: Train mse = 0.5217 | Val mse = 0.5373 Epoch 5: Train mse = 0.3928 | Val mse = 0.3925 Epoch 6: Train mse = 0.3340 | Val mse = 0.3764 Epoch 7: Train mse = 0.3021 | Val mse = 0.3731 Epoch 8: Train mse = 0.2841 | Val mse = 0.3346 Epoch 9: Train mse = 0.2695 | Val mse = 0.3404 Epoch 10: Train mse = 0.2575 | Val mse = 0.3406 Epoch 11: Train mse = 0.2519 | Val mse = 0.3225 Epoch 12: Train mse = 0.2409 | Val mse = 0.3390 Epoch 13: Train mse = 0.2347 | Val mse = 0.3422 Epoch 14: Train mse = 0.2285 | Val mse = 0.3377 Epoch 15: Train mse = 0.2205 | Val mse = 0.3523 Epoch 16: Train mse = 0.2127 | Val mse = 0.3539 Epoch 17: Train mse = 0.2055 | Val mse = 0.3581 Epoch 18: Train mse = 0.1981 | Val mse = 0.3716 Epoch 19: Train mse = 0.1956 | Val mse = 0.3619 Epoch 20: Train mse = 0.1877 | Val mse = 0.3722 Epoch 21: Train mse = 0.1859 | Val mse = 0.3583 Epoch 22: Train mse = 0.1820 | Val mse = 0.3789 Epoch 23: Train mse = 0.1758 | Val mse = 0.3608 Epoch 24: Train mse = 0.1723 | Val mse = 0.3715 Epoch 25: Train mse = 0.1665 | Val mse = 0.3618 Epoch 26: Train mse = 0.1609 | Val mse = 0.3760 Epoch 27: Train mse = 0.1563 | Val mse = 0.3737 Epoch 28: Train mse = 0.1514 | Val mse = 0.3742 Epoch 29: Train mse = 0.1472 | Val mse = 0.3741 Epoch 30: Train mse = 0.1443 | Val mse = 0.3910 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.08938343 1 day(s) RMSE : 0.17470632 1 day(s) R2 : 0.33128730 1 day(s) Pearson r : 0.58734444 1 day(s) QLIKE : 0.46710309 3 day(s) MAE : 0.09216679 3 day(s) RMSE : 0.17772054 3 day(s) R2 : 0.30267502 3 day(s) Pearson r : 0.56072037 3 day(s) QLIKE : 0.44199299 5 day(s) MAE : 0.09358763 5 day(s) RMSE : 0.18024687 5 day(s) R2 : 0.27455790 5 day(s) Pearson r : 0.53969217 5 day(s) QLIKE : 0.43391566 10 day(s) MAE : 0.09692819 10 day(s) RMSE : 0.18635014 10 day(s) R2 : 0.21719668 10 day(s) Pearson r : 0.49693950 10 day(s) QLIKE : 0.45666437 20 day(s) MAE : 0.09988040 20 day(s) RMSE : 0.19000796 20 day(s) R2 : 0.17656573 20 day(s) Pearson r : 0.46223998 20 day(s) QLIKE : 0.48809595 full horizon MAE : 0.09988040 full horizon RMSE : 0.19000796 full horizon R2 : 0.17656573 full horizon Pearson r : 0.46223998 full horizon QLIKE : 0.48809595 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/EURUSD/Simple_KAN_H20.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00302536, max=1.01372 === GOLD | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 5534 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 17.63876776611196 Min value: -0.5790835075307484 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.28323490000094 Min value: -5.250298730157779 Checking X_price_val: Shape: (443, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722398346 Min value: -0.5755730380699846 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.7902797110229713 Min value: -2.119473437475616 Checking X_price_test: Shape: (1107, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698947 Min value: -0.5768892216495461 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5405778347636028 Min value: -2.3606736150182797 Epoch 1: Train mse = 0.9003 | Val mse = 0.5067 Epoch 2: Train mse = 0.5157 | Val mse = 0.3988 Epoch 3: Train mse = 0.3481 | Val mse = 0.3219 Epoch 4: Train mse = 0.3139 | Val mse = 0.2837 Epoch 5: Train mse = 0.2627 | Val mse = 0.2630 Epoch 6: Train mse = 0.2207 | Val mse = 0.2463 Epoch 7: Train mse = 0.1951 | Val mse = 0.2418 Epoch 8: Train mse = 0.1655 | Val mse = 0.2336 Epoch 9: Train mse = 0.1417 | Val mse = 0.2298 Epoch 10: Train mse = 0.1338 | Val mse = 0.2390 Epoch 11: Train mse = 0.1215 | Val mse = 0.2550 Epoch 12: Train mse = 0.1052 | Val mse = 0.2570 Epoch 13: Train mse = 0.0946 | Val mse = 0.2691 Epoch 14: Train mse = 0.0929 | Val mse = 0.2453 Epoch 15: Train mse = 0.0841 | Val mse = 0.2566 Epoch 16: Train mse = 0.0766 | Val mse = 0.2505 Epoch 17: Train mse = 0.0725 | Val mse = 0.2647 Epoch 18: Train mse = 0.0684 | Val mse = 0.2845 Epoch 19: Train mse = 0.0663 | Val mse = 0.2710 Epoch 20: Train mse = 0.0605 | Val mse = 0.2708 Epoch 21: Train mse = 0.0547 | Val mse = 0.2763 Epoch 22: Train mse = 0.0491 | Val mse = 0.2658 Epoch 23: Train mse = 0.0447 | Val mse = 0.2856 Epoch 24: Train mse = 0.0427 | Val mse = 0.2816 Epoch 25: Train mse = 0.0426 | Val mse = 0.2789 Epoch 26: Train mse = 0.0385 | Val mse = 0.2961 Epoch 27: Train mse = 0.0334 | Val mse = 0.2860 Epoch 28: Train mse = 0.0280 | Val mse = 0.2848 Epoch 29: Train mse = 0.0294 | Val mse = 0.2751 Epoch 30: Train mse = 0.0263 | Val mse = 0.2811 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.28862430 1 day(s) RMSE : 0.54422790 1 day(s) R2 : 0.31029859 1 day(s) Pearson r : 0.59421240 1 day(s) QLIKE : 0.54432552 full horizon MAE : 0.28862430 full horizon RMSE : 0.54422790 full horizon R2 : 0.31029859 full horizon Pearson r : 0.59421240 full horizon QLIKE : 0.54432552 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GOLD/Simple_KAN_H1.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.00269326, max=3.6102 === GOLD | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 5534 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 17.63876776611196 Min value: -0.5790835075307484 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.283677745863046 Min value: -5.250623802304629 Checking X_price_val: Shape: (443, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722398346 Min value: -0.5755730380699846 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.7906723083316343 Min value: -2.1194793742414912 Checking X_price_test: Shape: (1107, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698947 Min value: -0.5768892216495461 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5409449791338357 Min value: -2.3607041381163065 Epoch 1: Train mse = 0.9183 | Val mse = 0.5057 Epoch 2: Train mse = 0.6644 | Val mse = 0.4680 Epoch 3: Train mse = 0.4690 | Val mse = 0.3810 Epoch 4: Train mse = 0.3578 | Val mse = 0.3274 Epoch 5: Train mse = 0.3037 | Val mse = 0.3009 Epoch 6: Train mse = 0.2609 | Val mse = 0.2754 Epoch 7: Train mse = 0.2235 | Val mse = 0.2622 Epoch 8: Train mse = 0.1986 | Val mse = 0.2449 Epoch 9: Train mse = 0.1740 | Val mse = 0.2406 Epoch 10: Train mse = 0.1587 | Val mse = 0.2373 Epoch 11: Train mse = 0.1468 | Val mse = 0.2426 Epoch 12: Train mse = 0.1366 | Val mse = 0.2438 Epoch 13: Train mse = 0.1281 | Val mse = 0.2502 Epoch 14: Train mse = 0.1202 | Val mse = 0.2451 Epoch 15: Train mse = 0.1167 | Val mse = 0.2429 Epoch 16: Train mse = 0.1066 | Val mse = 0.2535 Epoch 17: Train mse = 0.1052 | Val mse = 0.2522 Epoch 18: Train mse = 0.1017 | Val mse = 0.2533 Epoch 19: Train mse = 0.0947 | Val mse = 0.2494 Epoch 20: Train mse = 0.0881 | Val mse = 0.2554 Epoch 21: Train mse = 0.0834 | Val mse = 0.2649 Epoch 22: Train mse = 0.0792 | Val mse = 0.2652 Epoch 23: Train mse = 0.0738 | Val mse = 0.2642 Epoch 24: Train mse = 0.0701 | Val mse = 0.2608 Epoch 25: Train mse = 0.0672 | Val mse = 0.2765 Epoch 26: Train mse = 0.0642 | Val mse = 0.2732 Epoch 27: Train mse = 0.0603 | Val mse = 0.2734 Epoch 28: Train mse = 0.0571 | Val mse = 0.2774 Epoch 29: Train mse = 0.0557 | Val mse = 0.2753 Epoch 30: Train mse = 0.0537 | Val mse = 0.2841 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.29802935 1 day(s) RMSE : 0.54727011 1 day(s) R2 : 0.30256622 1 day(s) Pearson r : 0.55357295 1 day(s) QLIKE : 0.40674870 3 day(s) MAE : 0.30071062 3 day(s) RMSE : 0.56464566 3 day(s) R2 : 0.25819679 3 day(s) Pearson r : 0.51637904 3 day(s) QLIKE : 0.42083903 5 day(s) MAE : 0.30684668 5 day(s) RMSE : 0.57142210 5 day(s) R2 : 0.24244625 5 day(s) Pearson r : 0.50406822 5 day(s) QLIKE : 0.44368664 full horizon MAE : 0.30684668 full horizon RMSE : 0.57142210 full horizon R2 : 0.24244625 full horizon Pearson r : 0.50406822 full horizon QLIKE : 0.44368664 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GOLD/Simple_KAN_H5.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.00508248, max=3.43164 === GOLD | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 5534 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 17.63876776611196 Min value: -0.5790835075307484 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.2851543622719985 Min value: -5.254244390683224 Checking X_price_val: Shape: (443, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722398346 Min value: -0.5755730380699846 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.7918153902171272 Min value: -2.1209816396053798 Checking X_price_test: Shape: (1107, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698947 Min value: -0.5768892216495461 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5419191122017333 Min value: -2.3623696000304566 Epoch 1: Train mse = 0.9430 | Val mse = 0.4915 Epoch 2: Train mse = 0.7320 | Val mse = 0.4995 Epoch 3: Train mse = 0.5617 | Val mse = 0.4144 Epoch 4: Train mse = 0.4362 | Val mse = 0.3696 Epoch 5: Train mse = 0.3519 | Val mse = 0.3385 Epoch 6: Train mse = 0.2948 | Val mse = 0.2905 Epoch 7: Train mse = 0.2489 | Val mse = 0.2730 Epoch 8: Train mse = 0.2193 | Val mse = 0.2574 Epoch 9: Train mse = 0.1941 | Val mse = 0.2487 Epoch 10: Train mse = 0.1821 | Val mse = 0.2503 Epoch 11: Train mse = 0.1682 | Val mse = 0.2505 Epoch 12: Train mse = 0.1564 | Val mse = 0.2472 Epoch 13: Train mse = 0.1485 | Val mse = 0.2533 Epoch 14: Train mse = 0.1414 | Val mse = 0.2554 Epoch 15: Train mse = 0.1352 | Val mse = 0.2591 Epoch 16: Train mse = 0.1282 | Val mse = 0.2561 Epoch 17: Train mse = 0.1233 | Val mse = 0.2606 Epoch 18: Train mse = 0.1198 | Val mse = 0.2713 Epoch 19: Train mse = 0.1150 | Val mse = 0.2715 Epoch 20: Train mse = 0.1106 | Val mse = 0.2754 Epoch 21: Train mse = 0.1059 | Val mse = 0.2792 Epoch 22: Train mse = 0.0998 | Val mse = 0.2789 Epoch 23: Train mse = 0.0936 | Val mse = 0.2796 Epoch 24: Train mse = 0.0887 | Val mse = 0.2860 Epoch 25: Train mse = 0.0854 | Val mse = 0.2958 Epoch 26: Train mse = 0.0812 | Val mse = 0.3025 Epoch 27: Train mse = 0.0764 | Val mse = 0.3002 Epoch 28: Train mse = 0.0741 | Val mse = 0.3031 Epoch 29: Train mse = 0.0713 | Val mse = 0.3111 Epoch 30: Train mse = 0.0665 | Val mse = 0.3135 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.27871042 1 day(s) RMSE : 0.53236059 1 day(s) R2 : 0.34004958 1 day(s) Pearson r : 0.60390574 1 day(s) QLIKE : 0.43434120 3 day(s) MAE : 0.29394684 3 day(s) RMSE : 0.56120094 3 day(s) R2 : 0.26722020 3 day(s) Pearson r : 0.54722980 3 day(s) QLIKE : 0.45101764 5 day(s) MAE : 0.30252208 5 day(s) RMSE : 0.57649303 5 day(s) R2 : 0.22894118 5 day(s) Pearson r : 0.51819016 5 day(s) QLIKE : 0.44561320 10 day(s) MAE : 0.31602897 10 day(s) RMSE : 0.58808690 10 day(s) R2 : 0.20656595 10 day(s) Pearson r : 0.49991847 10 day(s) QLIKE : 0.49276984 full horizon MAE : 0.31602897 full horizon RMSE : 0.58808690 full horizon R2 : 0.20656595 full horizon Pearson r : 0.49991847 full horizon QLIKE : 0.49276984 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GOLD/Simple_KAN_H10.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.00757664, max=4.8144 === GOLD | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 5534 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 17.63876776611196 Min value: -0.5790835075307484 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.2870228653696634 Min value: -5.257736685381005 Checking X_price_val: Shape: (443, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722398346 Min value: -0.5755730380699846 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.793333110638287 Min value: -2.1222460659281195 Checking X_price_test: Shape: (1107, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698947 Min value: -0.5768892216495461 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5432591469134391 Min value: -2.3638056623268993 Epoch 1: Train mse = 0.9612 | Val mse = 0.4869 Epoch 2: Train mse = 0.8090 | Val mse = 0.5645 Epoch 3: Train mse = 0.6416 | Val mse = 0.4724 Epoch 4: Train mse = 0.5089 | Val mse = 0.4341 Epoch 5: Train mse = 0.4128 | Val mse = 0.4095 Epoch 6: Train mse = 0.3559 | Val mse = 0.3674 Epoch 7: Train mse = 0.3138 | Val mse = 0.3326 Epoch 8: Train mse = 0.2749 | Val mse = 0.3020 Epoch 9: Train mse = 0.2421 | Val mse = 0.3026 Epoch 10: Train mse = 0.2226 | Val mse = 0.2818 Epoch 11: Train mse = 0.2078 | Val mse = 0.2840 Epoch 12: Train mse = 0.1951 | Val mse = 0.2674 Epoch 13: Train mse = 0.1882 | Val mse = 0.2712 Epoch 14: Train mse = 0.1833 | Val mse = 0.2687 Epoch 15: Train mse = 0.1776 | Val mse = 0.2744 Epoch 16: Train mse = 0.1699 | Val mse = 0.2679 Epoch 17: Train mse = 0.1610 | Val mse = 0.2710 Epoch 18: Train mse = 0.1554 | Val mse = 0.2759 Epoch 19: Train mse = 0.1507 | Val mse = 0.2876 Epoch 20: Train mse = 0.1442 | Val mse = 0.2852 Epoch 21: Train mse = 0.1392 | Val mse = 0.2923 Epoch 22: Train mse = 0.1333 | Val mse = 0.2979 Epoch 23: Train mse = 0.1302 | Val mse = 0.3003 Epoch 24: Train mse = 0.1267 | Val mse = 0.3022 Epoch 25: Train mse = 0.1219 | Val mse = 0.3045 Epoch 26: Train mse = 0.1155 | Val mse = 0.3006 Epoch 27: Train mse = 0.1097 | Val mse = 0.3173 Epoch 28: Train mse = 0.1064 | Val mse = 0.3146 Epoch 29: Train mse = 0.1007 | Val mse = 0.3254 Epoch 30: Train mse = 0.0952 | Val mse = 0.3210 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.30308997 1 day(s) RMSE : 0.55048019 1 day(s) R2 : 0.29436047 1 day(s) Pearson r : 0.55948752 1 day(s) QLIKE : 0.39813374 3 day(s) MAE : 0.30720722 3 day(s) RMSE : 0.57525939 3 day(s) R2 : 0.23004711 3 day(s) Pearson r : 0.51383475 3 day(s) QLIKE : 0.41860763 5 day(s) MAE : 0.31314457 5 day(s) RMSE : 0.58427711 5 day(s) R2 : 0.20797822 5 day(s) Pearson r : 0.49581011 5 day(s) QLIKE : 0.44071589 10 day(s) MAE : 0.32000525 10 day(s) RMSE : 0.58960252 10 day(s) R2 : 0.20247101 10 day(s) Pearson r : 0.49431692 10 day(s) QLIKE : 0.49043000 20 day(s) MAE : 0.34287990 20 day(s) RMSE : 0.62801041 20 day(s) R2 : 0.10853622 20 day(s) Pearson r : 0.41496603 20 day(s) QLIKE : 0.58224203 full horizon MAE : 0.34287990 full horizon RMSE : 0.62801041 full horizon R2 : 0.10853622 full horizon Pearson r : 0.41496603 full horizon QLIKE : 0.58224203 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GOLD/Simple_KAN_H20.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.00448748, max=5.82162 === SP500 | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3466 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790344 Min value: -0.29186160079947243 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.598103080699805 Min value: -18.186962127218408 Checking X_price_val: Shape: (277, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.189039765537116 Min value: -0.288808876061582 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3850009093345803 Min value: -2.4181466917899535 Checking X_price_test: Shape: (694, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.65876861006644 Min value: -0.2909517549064528 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.3795163284544953 Min value: -3.2441546953028633 Epoch 1: Train mse = 0.9028 | Val mse = 0.7569 Epoch 2: Train mse = 0.7421 | Val mse = 0.6542 Epoch 3: Train mse = 0.6373 | Val mse = 0.5390 Epoch 4: Train mse = 0.6001 | Val mse = 0.4889 Epoch 5: Train mse = 0.5443 | Val mse = 0.4986 Epoch 6: Train mse = 0.5010 | Val mse = 0.4980 Epoch 7: Train mse = 0.4769 | Val mse = 0.5330 Epoch 8: Train mse = 0.4733 | Val mse = 0.5262 Epoch 9: Train mse = 0.4407 | Val mse = 0.5143 Epoch 10: Train mse = 0.4151 | Val mse = 0.5187 Epoch 11: Train mse = 0.4077 | Val mse = 0.5298 Epoch 12: Train mse = 0.3843 | Val mse = 0.5385 Epoch 13: Train mse = 0.3805 | Val mse = 0.5749 Epoch 14: Train mse = 0.3657 | Val mse = 0.5454 Epoch 15: Train mse = 0.4134 | Val mse = 0.5752 Epoch 16: Train mse = 0.4123 | Val mse = 0.5858 Epoch 17: Train mse = 0.3465 | Val mse = 0.5700 Epoch 18: Train mse = 0.2979 | Val mse = 0.5424 Epoch 19: Train mse = 0.2660 | Val mse = 0.5721 Epoch 20: Train mse = 0.2397 | Val mse = 0.5583 Epoch 21: Train mse = 0.2381 | Val mse = 0.5652 Epoch 22: Train mse = 0.2430 | Val mse = 0.5410 Epoch 23: Train mse = 0.2359 | Val mse = 0.5277 Epoch 24: Train mse = 0.2249 | Val mse = 0.5372 Epoch 25: Train mse = 0.2129 | Val mse = 0.5387 Epoch 26: Train mse = 0.2054 | Val mse = 0.5633 Epoch 27: Train mse = 0.1962 | Val mse = 0.5768 Epoch 28: Train mse = 0.1983 | Val mse = 0.5622 Epoch 29: Train mse = 0.1929 | Val mse = 0.5863 Epoch 30: Train mse = 0.1881 | Val mse = 0.6227 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 60 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.49997044 1 day(s) RMSE : 2.39624140 1 day(s) R2 : 0.28389833 1 day(s) Pearson r : 0.65403000 1 day(s) QLIKE : 0.47191816 full horizon MAE : 0.49997044 full horizon RMSE : 2.39624140 full horizon R2 : 0.28389833 full horizon Pearson r : 0.65403000 full horizon QLIKE : 0.47191816 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/SP500/Simple_KAN_H1.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.0677789, max=9.42283 === SP500 | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3466 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790344 Min value: -0.29186160079947243 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.596907486751884 Min value: -18.180099645819357 Checking X_price_val: Shape: (277, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.189039765537116 Min value: -0.288808876061582 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.384254029613913 Min value: -2.417116935778614 Checking X_price_test: Shape: (694, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.65876861006644 Min value: -0.2909517549064528 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.3784015875360525 Min value: -3.2428194072829677 Epoch 1: Train mse = 0.9275 | Val mse = 0.8104 Epoch 2: Train mse = 0.7860 | Val mse = 0.8305 Epoch 3: Train mse = 0.7296 | Val mse = 0.7517 Epoch 4: Train mse = 0.7032 | Val mse = 0.7608 Epoch 5: Train mse = 0.6804 | Val mse = 0.7547 Epoch 6: Train mse = 0.6658 | Val mse = 0.7476 Epoch 7: Train mse = 0.6611 | Val mse = 0.7553 Epoch 8: Train mse = 0.6527 | Val mse = 0.7444 Epoch 9: Train mse = 0.6414 | Val mse = 0.7281 Epoch 10: Train mse = 0.6351 | Val mse = 0.7071 Epoch 11: Train mse = 0.6478 | Val mse = 0.6999 Epoch 12: Train mse = 0.6288 | Val mse = 0.6614 Epoch 13: Train mse = 0.6081 | Val mse = 0.6869 Epoch 14: Train mse = 0.5900 | Val mse = 0.6212 Epoch 15: Train mse = 0.5756 | Val mse = 0.6174 Epoch 16: Train mse = 0.5444 | Val mse = 0.6253 Epoch 17: Train mse = 0.5201 | Val mse = 0.6052 Epoch 18: Train mse = 0.4971 | Val mse = 0.5834 Epoch 19: Train mse = 0.4806 | Val mse = 0.5831 Epoch 20: Train mse = 0.4619 | Val mse = 0.6205 Epoch 21: Train mse = 0.4375 | Val mse = 0.6027 Epoch 22: Train mse = 0.4350 | Val mse = 0.5769 Epoch 23: Train mse = 0.4211 | Val mse = 0.6526 Epoch 24: Train mse = 0.4002 | Val mse = 0.6037 Epoch 25: Train mse = 0.3838 | Val mse = 0.6065 Epoch 26: Train mse = 0.3653 | Val mse = 0.5997 Epoch 27: Train mse = 0.3476 | Val mse = 0.6191 Epoch 28: Train mse = 0.3336 | Val mse = 0.6450 Epoch 29: Train mse = 0.3235 | Val mse = 0.6324 Epoch 30: Train mse = 0.3149 | Val mse = 0.6589 Epoch 31: Train mse = 0.2883 | Val mse = 0.6607 Epoch 32: Train mse = 0.2779 | Val mse = 0.6588 Early stopping triggered at epoch 32. Parameters used in the single-fit model: input_dim: 60 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.59308450 1 day(s) RMSE : 2.54715518 1 day(s) R2 : 0.19085871 1 day(s) Pearson r : 0.54198579 1 day(s) QLIKE : 0.62028127 3 day(s) MAE : 0.70351322 3 day(s) RMSE : 3.19267388 3 day(s) R2 : -0.27116428 3 day(s) Pearson r : 0.38393386 3 day(s) QLIKE : 0.69833720 5 day(s) MAE : 0.79043202 5 day(s) RMSE : 3.74713413 5 day(s) R2 : -0.75091943 5 day(s) Pearson r : 0.25139463 5 day(s) QLIKE : 0.73104298 full horizon MAE : 0.79043202 full horizon RMSE : 3.74713413 full horizon R2 : -0.75091943 full horizon Pearson r : 0.25139463 full horizon QLIKE : 0.73104298 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/SP500/Simple_KAN_H5.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.00246211, max=54.9897 === SP500 | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3466 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790344 Min value: -0.29186160079947243 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.596947126941468 Min value: -18.18335453470548 Checking X_price_val: Shape: (277, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.189039765537116 Min value: -0.288808876061582 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.384110213828625 Min value: -2.417987125769155 Checking X_price_test: Shape: (694, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.65876861006644 Min value: -0.2909517549064528 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.378408171115341 Min value: -3.2438145134653293 Epoch 1: Train mse = 0.9644 | Val mse = 0.8654 Epoch 2: Train mse = 0.8324 | Val mse = 0.8471 Epoch 3: Train mse = 0.7910 | Val mse = 0.7863 Epoch 4: Train mse = 0.7592 | Val mse = 0.7752 Epoch 5: Train mse = 0.7406 | Val mse = 0.7864 Epoch 6: Train mse = 0.7287 | Val mse = 0.7928 Epoch 7: Train mse = 0.7231 | Val mse = 0.7784 Epoch 8: Train mse = 0.7132 | Val mse = 0.7812 Epoch 9: Train mse = 0.7013 | Val mse = 0.7879 Epoch 10: Train mse = 0.7033 | Val mse = 0.7859 Epoch 11: Train mse = 0.7029 | Val mse = 0.7777 Epoch 12: Train mse = 0.6865 | Val mse = 0.7323 Epoch 13: Train mse = 0.6692 | Val mse = 0.7267 Epoch 14: Train mse = 0.6533 | Val mse = 0.7102 Epoch 15: Train mse = 0.6331 | Val mse = 0.7083 Epoch 16: Train mse = 0.6132 | Val mse = 0.6556 Epoch 17: Train mse = 0.6075 | Val mse = 0.6600 Epoch 18: Train mse = 0.5807 | Val mse = 0.6430 Epoch 19: Train mse = 0.5612 | Val mse = 0.6377 Epoch 20: Train mse = 0.5479 | Val mse = 0.6887 Epoch 21: Train mse = 0.5225 | Val mse = 0.6564 Epoch 22: Train mse = 0.5055 | Val mse = 0.6540 Epoch 23: Train mse = 0.4834 | Val mse = 0.6244 Epoch 24: Train mse = 0.4704 | Val mse = 0.6312 Epoch 25: Train mse = 0.4568 | Val mse = 0.6734 Epoch 26: Train mse = 0.4495 | Val mse = 0.7020 Epoch 27: Train mse = 0.4324 | Val mse = 0.6269 Epoch 28: Train mse = 0.4251 | Val mse = 0.6670 Epoch 29: Train mse = 0.4114 | Val mse = 0.6535 Epoch 30: Train mse = 0.3968 | Val mse = 0.6588 Epoch 31: Train mse = 0.4028 | Val mse = 0.6520 Epoch 32: Train mse = 0.3854 | Val mse = 0.6864 Epoch 33: Train mse = 0.3669 | Val mse = 0.6805 Early stopping triggered at epoch 33. Parameters used in the single-fit model: input_dim: 60 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.59881581 1 day(s) RMSE : 2.55019538 1 day(s) R2 : 0.18892603 1 day(s) Pearson r : 0.53417732 1 day(s) QLIKE : 0.51713623 3 day(s) MAE : 0.69887411 3 day(s) RMSE : 3.16712780 3 day(s) R2 : -0.25090330 3 day(s) Pearson r : 0.35630985 3 day(s) QLIKE : 0.58972365 5 day(s) MAE : 0.79071023 5 day(s) RMSE : 3.54935411 5 day(s) R2 : -0.57096440 5 day(s) Pearson r : 0.24718662 5 day(s) QLIKE : 0.68991371 10 day(s) MAE : 0.85558021 10 day(s) RMSE : 3.64931849 10 day(s) R2 : -0.66047526 10 day(s) Pearson r : 0.15839601 10 day(s) QLIKE : 0.88979748 full horizon MAE : 0.85558021 full horizon RMSE : 3.64931849 full horizon R2 : -0.66047526 full horizon Pearson r : 0.15839601 full horizon QLIKE : 0.88979748 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/SP500/Simple_KAN_H10.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.00811509, max=49.7571 === SP500 | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3466 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790344 Min value: -0.29186160079947243 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.5963112311058607 Min value: -18.187442810157716 Checking X_price_val: Shape: (277, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.189039765537116 Min value: -0.288808876061582 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3832820721048678 Min value: -2.446834413184202 Checking X_price_test: Shape: (694, 60, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.65876861006644 Min value: -0.2909517549064528 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.377737634830384 Min value: -3.2455347334033506 Epoch 1: Train mse = 0.9694 | Val mse = 0.9072 Epoch 2: Train mse = 0.8552 | Val mse = 0.9263 Epoch 3: Train mse = 0.8230 | Val mse = 0.8346 Epoch 4: Train mse = 0.8026 | Val mse = 0.8243 Epoch 5: Train mse = 0.7895 | Val mse = 0.8448 Epoch 6: Train mse = 0.7813 | Val mse = 0.8299 Epoch 7: Train mse = 0.7773 | Val mse = 0.8334 Epoch 8: Train mse = 0.7694 | Val mse = 0.8360 Epoch 9: Train mse = 0.7635 | Val mse = 0.8568 Epoch 10: Train mse = 0.7668 | Val mse = 0.8179 Epoch 11: Train mse = 0.7515 | Val mse = 0.8446 Epoch 12: Train mse = 0.7401 | Val mse = 0.7498 Epoch 13: Train mse = 0.7210 | Val mse = 0.7302 Epoch 14: Train mse = 0.7018 | Val mse = 0.7275 Epoch 15: Train mse = 0.6797 | Val mse = 0.7290 Epoch 16: Train mse = 0.6672 | Val mse = 0.6941 Epoch 17: Train mse = 0.6546 | Val mse = 0.6990 Epoch 18: Train mse = 0.6259 | Val mse = 0.6984 Epoch 19: Train mse = 0.6014 | Val mse = 0.8137 Epoch 20: Train mse = 0.5918 | Val mse = 0.7111 Epoch 21: Train mse = 0.5718 | Val mse = 0.6760 Epoch 22: Train mse = 0.5657 | Val mse = 0.7061 Epoch 23: Train mse = 0.5478 | Val mse = 0.7094 Epoch 24: Train mse = 0.5286 | Val mse = 0.7596 Epoch 25: Train mse = 0.5159 | Val mse = 0.7015 Epoch 26: Train mse = 0.5029 | Val mse = 0.6649 Epoch 27: Train mse = 0.4890 | Val mse = 0.7361 Epoch 28: Train mse = 0.4748 | Val mse = 0.6893 Epoch 29: Train mse = 0.4643 | Val mse = 0.7022 Epoch 30: Train mse = 0.4655 | Val mse = 0.6840 Epoch 31: Train mse = 0.4507 | Val mse = 0.7071 Epoch 32: Train mse = 0.4484 | Val mse = 0.7751 Epoch 33: Train mse = 0.4335 | Val mse = 0.7130 Epoch 34: Train mse = 0.4331 | Val mse = 0.7393 Epoch 35: Train mse = 0.4259 | Val mse = 0.7036 Epoch 36: Train mse = 0.4153 | Val mse = 0.7646 Early stopping triggered at epoch 36. Parameters used in the single-fit model: input_dim: 60 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.61014199 1 day(s) RMSE : 2.60891120 1 day(s) R2 : 0.15114766 1 day(s) Pearson r : 0.48627095 1 day(s) QLIKE : 0.57154295 3 day(s) MAE : 0.72311987 3 day(s) RMSE : 3.35081269 3 day(s) R2 : -0.40020900 3 day(s) Pearson r : 0.34887806 3 day(s) QLIKE : 0.63669379 5 day(s) MAE : 0.78506448 5 day(s) RMSE : 3.62777461 5 day(s) R2 : -0.64115001 5 day(s) Pearson r : 0.24777164 5 day(s) QLIKE : 0.70695204 10 day(s) MAE : 0.88173555 10 day(s) RMSE : 3.87672806 10 day(s) R2 : -0.87387039 10 day(s) Pearson r : 0.15186299 10 day(s) QLIKE : 0.84807828 20 day(s) MAE : 0.90759089 20 day(s) RMSE : 3.75387333 20 day(s) R2 : -0.75677676 20 day(s) Pearson r : 0.10791116 20 day(s) QLIKE : 0.87219332 full horizon MAE : 0.90759089 full horizon RMSE : 3.75387333 full horizon R2 : -0.75677676 full horizon Pearson r : 0.10791116 full horizon QLIKE : 0.87219332 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/SP500/Simple_KAN_H20.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.00822392, max=86.9713
In [ ]:
import os, pickle
from pathlib import Path
import numpy as np
load_path = Path(root_folder) / objects_relative_path / "structured_data_dict_10.pkl"
if not load_path.exists():
raise FileNotFoundError(f"Pickle not found at: {load_path}")
with open(load_path, "rb") as f:
structured_data_dict_10 = pickle.load(f)
print("Data dictionary 10 loaded successfully.")
tickers = ["AAPL", "MSFT", "GE", "BAC", "C", "BTCUSDT", "EURUSD", "GOLD", "SP500"]
X10_price_map, X10_time_map, y10_map = {}, {}, {}
missing = []
for t in tickers:
if t not in structured_data_dict_10:
missing.append(t)
continue
entry = structured_data_dict_10[t]
Xp10 = entry.get("X_other")
Xt10 = entry.get("X_time")
Y10 = entry.get("y")
if Xp10 is None or Y10 is None:
print(f"[WARN] {t}: missing"
f"{' X_other' if Xp10 is None else ''}"
f"{' and y' if (Xp10 is None and Y10 is None) else (' y' if Y10 is None else '')} - skipping.")
continue
try:
Xp10 = np.asarray(Xp10, dtype=float)
Y10 = np.asarray(Y10, dtype=float)
Xt10 = None if Xt10 is None else np.asarray(Xt10, dtype=float)
except Exception as e:
print(f"[WARN] {t}: array conversion failed: {e} - skipping.")
continue
if Xp10.ndim != 3 or Y10.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_other={Xp10.shape}, y={Y10.shape} - skipping.")
continue
if Xp10.shape[0] != Y10.shape[0]:
print(f"[WARN] {t}: X_other and y n_samples mismatch: {Xp10.shape[0]} vs {Y10.shape[0]} - skipping.")
continue
if Xt10 is not None:
if Xt10.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_time={Xt10.shape} - setting to None.")
Xt10 = None
elif Xt10.shape[0] != Xp10.shape[0]:
print(f"[WARN] {t}: X_time n_samples mismatch: {Xt10.shape[0]} vs {Xp10.shape[0]} - setting to None.")
Xt10 = None
X10_price_map[t] = Xp10
X10_time_map[t] = Xt10
y10_map[t] = Y10
if missing:
print(f"[INFO] Missing tickers in data dict (skipped): {missing}")
print(f"[INFO] Loaded tickers: {list(X10_price_map.keys())}")
y = y10_map.get("AAPL")
print("AAPL y shape:", None if y is None else y.shape)
Data dictionary 10 loaded successfully. [INFO] Loaded tickers: ['AAPL', 'MSFT', 'GE', 'BAC', 'C', 'BTCUSDT', 'EURUSD', 'GOLD', 'SP500'] AAPL y shape: (2018, 60, 2)
In [151]:
final_tickers = ["AAPL", "MSFT", "GE", "BAC", "C", "BTCUSDT", "EURUSD", "GOLD", "SP500"]
final_models = ["ITransformer"]
final_horizons = [1, 5, 10, 20]
MULTI_TASK_MODELS = {"ITransformer"}
_ = run_all_models_for_all(
tickers=final_tickers,
horizons=final_horizons,
model_list=final_models,
X_price_map=X10_price_map,
X_time_map=X10_time_map,
y_map=y10_map,
base_save_dir=FINAL_BASE_SAVE_DIR,
fixed_params=FIXED_PARAMS_MERGED,
common_params=COMMON,
model_io=MODEL_IO,
multitask_models=MULTI_TASK_MODELS,
param_grids=None,
results_store=final_results_store,
overwrite=True
)
=== AAPL | H=1 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714034 Min value: -33.2571324878827 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.133266415235102 Min value: -2.354189421011418 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.093969981528533 Min value: -1.2108152104959184 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9069843280619727 Min value: -1.6295009124362907 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.5044175194514073 Min value: -2.3858091292041257 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.8950185841626324 Min value: -2.1199314093218837 Epoch 1/50 Train 28.9482 Val 0.7880 0.6s Epoch 2/50 Train 28.4201 Val 0.7867 0.2s Epoch 3/50 Train 27.9709 Val 0.7854 0.2s Epoch 4/50 Train 27.5625 Val 0.7842 0.2s Epoch 5/50 Train 27.1872 Val 0.7832 0.2s Epoch 6/50 Train 26.8406 Val 0.7821 0.2s Epoch 7/50 Train 26.5196 Val 0.7812 0.2s Epoch 8/50 Train 26.2216 Val 0.7803 0.2s Epoch 9/50 Train 25.9447 Val 0.7795 0.2s Epoch 10/50 Train 25.6870 Val 0.7788 0.2s Epoch 11/50 Train 25.4469 Val 0.7781 0.2s Epoch 12/50 Train 25.2229 Val 0.7775 0.2s Epoch 13/50 Train 25.0138 Val 0.7769 0.2s Epoch 14/50 Train 24.8183 Val 0.7764 0.2s Epoch 15/50 Train 24.6353 Val 0.7759 0.2s Epoch 16/50 Train 24.4639 Val 0.7754 0.2s Epoch 17/50 Train 24.3031 Val 0.7750 0.2s Epoch 18/50 Train 24.1523 Val 0.7746 0.2s Epoch 19/50 Train 24.0105 Val 0.7742 0.2s Epoch 20/50 Train 23.8773 Val 0.7739 0.2s Epoch 21/50 Train 23.7518 Val 0.7736 0.2s Epoch 22/50 Train 23.6337 Val 0.7733 0.2s Epoch 23/50 Train 23.5223 Val 0.7730 0.2s Epoch 24/50 Train 23.4172 Val 0.7728 0.2s Epoch 25/50 Train 23.3179 Val 0.7725 0.2s Epoch 26/50 Train 23.2240 Val 0.7723 0.2s Epoch 27/50 Train 23.1353 Val 0.7721 0.2s Epoch 28/50 Train 23.0512 Val 0.7720 0.2s Epoch 29/50 Train 22.9715 Val 0.7718 0.3s Epoch 30/50 Train 22.8960 Val 0.7716 0.2s Epoch 31/50 Train 22.8243 Val 0.7715 0.2s Epoch 32/50 Train 22.7561 Val 0.7714 0.2s Epoch 33/50 Train 22.6914 Val 0.7713 0.2s Epoch 34/50 Train 22.6297 Val 0.7712 0.2s Epoch 35/50 Train 22.5710 Val 0.7711 0.2s Epoch 36/50 Train 22.5151 Val 0.7710 0.2s Epoch 37/50 Train 22.4617 Val 0.7709 0.2s Epoch 38/50 Train 22.4108 Val 0.7708 0.2s Epoch 39/50 Train 22.3622 Val 0.7707 0.2s Epoch 40/50 Train 22.3156 Val 0.7707 0.2s Epoch 41/50 Train 22.2711 Val 0.7706 0.2s Epoch 42/50 Train 22.2285 Val 0.7706 0.2s Epoch 43/50 Train 22.1876 Val 0.7705 0.2s Epoch 44/50 Train 22.1484 Val 0.7705 0.2s Epoch 45/50 Train 22.1108 Val 0.7705 0.2s Epoch 46/50 Train 22.0747 Val 0.7704 0.2s Epoch 47/50 Train 22.0399 Val 0.7704 0.2s Epoch 48/50 Train 22.0065 Val 0.7704 0.2s Epoch 49/50 Train 21.9743 Val 0.7704 0.2s Epoch 50/50 Train 21.9433 Val 0.7703 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 1 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.44080942 1 day(s) RMSE : 8.71817512 1 day(s) R2 : -0.01756961 1 day(s) Pearson r : 0.23056800 1 day(s) QLIKE : 0.53330540 full horizon MAE : 2.44080942 full horizon RMSE : 8.71817512 full horizon R2 : -0.01756961 full horizon Pearson r : 0.23056800 full horizon QLIKE : 0.53330540 --- Task 2 --- 1 day(s) MAE : 0.58801941 1 day(s) RMSE : 1.24304818 1 day(s) R2 : -0.28827194 1 day(s) Pearson r : 0.00701819 1 day(s) QLIKE : 13.90310447 full horizon MAE : 0.58801941 full horizon RMSE : 1.24304818 full horizon R2 : -0.28827194 full horizon Pearson r : 0.00701819 full horizon QLIKE : 13.90310447 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/AAPL/ITransformer_H1.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=2.40867, max=2.4793 === AAPL | H=5 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714034 Min value: -33.2571324878827 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.140231129276028 Min value: -2.3583145800692082 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.093969981528533 Min value: -1.2108152104959184 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9079382526637185 Min value: -1.632926464574084 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.5044175194514073 Min value: -2.3858091292041257 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.8969263469922275 Min value: -2.123830418076332 Epoch 1/50 Train 29.5854 Val 0.8073 0.5s Epoch 2/50 Train 29.2640 Val 0.8061 0.2s Epoch 3/50 Train 28.9742 Val 0.8050 0.2s Epoch 4/50 Train 28.7001 Val 0.8040 0.2s Epoch 5/50 Train 28.4402 Val 0.8030 0.2s Epoch 6/50 Train 28.1934 Val 0.8020 0.2s Epoch 7/50 Train 27.9586 Val 0.8011 0.2s Epoch 8/50 Train 27.7347 Val 0.8002 0.2s Epoch 9/50 Train 27.5210 Val 0.7994 0.2s Epoch 10/50 Train 27.3166 Val 0.7985 0.2s Epoch 11/50 Train 27.1210 Val 0.7977 0.2s Epoch 12/50 Train 26.9335 Val 0.7970 0.2s Epoch 13/50 Train 26.7537 Val 0.7962 0.2s Epoch 14/50 Train 26.5811 Val 0.7955 0.2s Epoch 15/50 Train 26.4154 Val 0.7948 0.2s Epoch 16/50 Train 26.2561 Val 0.7941 0.2s Epoch 17/50 Train 26.1031 Val 0.7935 0.2s Epoch 18/50 Train 25.9560 Val 0.7929 0.2s Epoch 19/50 Train 25.8145 Val 0.7923 0.2s Epoch 20/50 Train 25.6784 Val 0.7917 0.2s Epoch 21/50 Train 25.5474 Val 0.7911 0.2s Epoch 22/50 Train 25.4214 Val 0.7906 0.2s Epoch 23/50 Train 25.3000 Val 0.7900 0.2s Epoch 24/50 Train 25.1832 Val 0.7895 0.2s Epoch 25/50 Train 25.0707 Val 0.7890 0.2s Epoch 26/50 Train 24.9624 Val 0.7885 0.2s Epoch 27/50 Train 24.8581 Val 0.7881 0.2s Epoch 28/50 Train 24.7576 Val 0.7876 0.3s Epoch 29/50 Train 24.6608 Val 0.7872 0.2s Epoch 30/50 Train 24.5674 Val 0.7868 0.2s Epoch 31/50 Train 24.4775 Val 0.7864 0.3s Epoch 32/50 Train 24.3908 Val 0.7860 0.2s Epoch 33/50 Train 24.3072 Val 0.7856 0.2s Epoch 34/50 Train 24.2266 Val 0.7852 0.2s Epoch 35/50 Train 24.1489 Val 0.7848 0.2s Epoch 36/50 Train 24.0739 Val 0.7845 0.2s Epoch 37/50 Train 24.0016 Val 0.7842 0.2s Epoch 38/50 Train 23.9318 Val 0.7838 0.2s Epoch 39/50 Train 23.8645 Val 0.7835 0.2s Epoch 40/50 Train 23.7995 Val 0.7832 0.2s Epoch 41/50 Train 23.7368 Val 0.7829 0.4s Epoch 42/50 Train 23.6762 Val 0.7826 0.3s Epoch 43/50 Train 23.6177 Val 0.7823 0.3s Epoch 44/50 Train 23.5611 Val 0.7821 0.3s Epoch 45/50 Train 23.5065 Val 0.7818 0.3s Epoch 46/50 Train 23.4538 Val 0.7816 0.3s Epoch 47/50 Train 23.4027 Val 0.7813 0.3s Epoch 48/50 Train 23.3534 Val 0.7811 0.3s Epoch 49/50 Train 23.3057 Val 0.7808 0.3s Epoch 50/50 Train 23.2596 Val 0.7806 0.3s Parameters used in the single-fit model: input_len: 60 output_len: 5 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.44210174 1 day(s) RMSE : 8.71798493 1 day(s) R2 : -0.01752521 1 day(s) Pearson r : 0.18403635 1 day(s) QLIKE : 0.53368233 3 day(s) MAE : 2.52920014 3 day(s) RMSE : 8.97471969 3 day(s) R2 : -0.01920720 3 day(s) Pearson r : 0.25052337 3 day(s) QLIKE : 0.55066899 5 day(s) MAE : 2.55297706 5 day(s) RMSE : 9.02791015 5 day(s) R2 : -0.01994404 5 day(s) Pearson r : 0.19791944 5 day(s) QLIKE : 0.55502788 full horizon MAE : 2.55297706 full horizon RMSE : 9.02791015 full horizon R2 : -0.01994404 full horizon Pearson r : 0.19791944 full horizon QLIKE : 0.55502788 --- Task 2 --- 1 day(s) MAE : 0.58801728 1 day(s) RMSE : 1.24304568 1 day(s) R2 : -0.28826676 1 day(s) Pearson r : 0.01739471 1 day(s) QLIKE : 14.35501510 3 day(s) MAE : 0.59012472 3 day(s) RMSE : 1.24572638 3 day(s) R2 : -0.28932746 3 day(s) Pearson r : 0.02783502 3 day(s) QLIKE : 13.83295252 5 day(s) MAE : 0.59116921 5 day(s) RMSE : 1.24664024 5 day(s) R2 : -0.29010270 5 day(s) Pearson r : 0.00347599 5 day(s) QLIKE : 13.72215779 full horizon MAE : 0.59116921 full horizon RMSE : 1.24664024 full horizon R2 : -0.29010270 full horizon Pearson r : 0.00347599 full horizon QLIKE : 13.72215779 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/AAPL/ITransformer_H5.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=2.39799, max=2.54057 === AAPL | H=10 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714034 Min value: -33.2571324878827 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.149858145997081 Min value: -2.3634631558377763 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.093969981528533 Min value: -1.2108152104959184 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.909556794039411 Min value: -1.6371429190585962 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.5044175194514073 Min value: -2.3858091292041257 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.8998157347365185 Min value: -2.1286776824868854 Epoch 1/50 Train 29.9010 Val 0.8068 0.6s Epoch 2/50 Train 29.6512 Val 0.8060 0.2s Epoch 3/50 Train 29.4229 Val 0.8052 0.2s Epoch 4/50 Train 29.2040 Val 0.8045 0.3s Epoch 5/50 Train 28.9939 Val 0.8038 0.2s Epoch 6/50 Train 28.7920 Val 0.8031 0.2s Epoch 7/50 Train 28.5978 Val 0.8024 0.2s Epoch 8/50 Train 28.4107 Val 0.8018 0.2s Epoch 9/50 Train 28.2302 Val 0.8012 0.2s Epoch 10/50 Train 28.0558 Val 0.8006 0.2s Epoch 11/50 Train 27.8873 Val 0.8000 0.2s Epoch 12/50 Train 27.7242 Val 0.7994 0.2s Epoch 13/50 Train 27.5662 Val 0.7988 0.2s Epoch 14/50 Train 27.4131 Val 0.7983 0.2s Epoch 15/50 Train 27.2646 Val 0.7978 0.2s Epoch 16/50 Train 27.1205 Val 0.7973 0.2s Epoch 17/50 Train 26.9806 Val 0.7968 0.2s Epoch 18/50 Train 26.8447 Val 0.7963 0.2s Epoch 19/50 Train 26.7126 Val 0.7958 0.2s Epoch 20/50 Train 26.5843 Val 0.7953 0.2s Epoch 21/50 Train 26.4594 Val 0.7948 0.2s Epoch 22/50 Train 26.3380 Val 0.7944 0.2s Epoch 23/50 Train 26.2198 Val 0.7940 0.2s Epoch 24/50 Train 26.1049 Val 0.7935 0.2s Epoch 25/50 Train 25.9930 Val 0.7931 0.3s Epoch 26/50 Train 25.8841 Val 0.7927 0.2s Epoch 27/50 Train 25.7780 Val 0.7923 0.3s Epoch 28/50 Train 25.6748 Val 0.7919 0.2s Epoch 29/50 Train 25.5743 Val 0.7915 0.3s Epoch 30/50 Train 25.4765 Val 0.7912 0.3s Epoch 31/50 Train 25.3813 Val 0.7908 0.3s Epoch 32/50 Train 25.2886 Val 0.7904 0.3s Epoch 33/50 Train 25.1983 Val 0.7901 0.3s Epoch 34/50 Train 25.1104 Val 0.7898 0.3s Epoch 35/50 Train 25.0249 Val 0.7894 0.3s Epoch 36/50 Train 24.9417 Val 0.7891 0.3s Epoch 37/50 Train 24.8607 Val 0.7888 0.3s Epoch 38/50 Train 24.7819 Val 0.7885 0.3s Epoch 39/50 Train 24.7052 Val 0.7882 0.2s Epoch 40/50 Train 24.6306 Val 0.7879 0.2s Epoch 41/50 Train 24.5581 Val 0.7876 0.3s Epoch 42/50 Train 24.4876 Val 0.7873 0.2s Epoch 43/50 Train 24.4190 Val 0.7870 0.2s Epoch 44/50 Train 24.3524 Val 0.7868 0.2s Epoch 45/50 Train 24.2877 Val 0.7865 0.2s Epoch 46/50 Train 24.2248 Val 0.7862 0.2s Epoch 47/50 Train 24.1637 Val 0.7860 0.2s Epoch 48/50 Train 24.1044 Val 0.7858 0.2s Epoch 49/50 Train 24.0469 Val 0.7855 0.2s Epoch 50/50 Train 23.9910 Val 0.7853 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 10 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.44358512 1 day(s) RMSE : 8.71717953 1 day(s) R2 : -0.01733721 1 day(s) Pearson r : 0.18702392 1 day(s) QLIKE : 0.53358636 3 day(s) MAE : 2.53067193 3 day(s) RMSE : 8.97408416 3 day(s) R2 : -0.01906286 3 day(s) Pearson r : 0.23870684 3 day(s) QLIKE : 0.55074560 5 day(s) MAE : 2.55446230 5 day(s) RMSE : 9.02730955 5 day(s) R2 : -0.01980834 5 day(s) Pearson r : 0.18352002 5 day(s) QLIKE : 0.55513935 10 day(s) MAE : 2.58105813 10 day(s) RMSE : 9.06859530 10 day(s) R2 : -0.02069214 10 day(s) Pearson r : 0.14998041 10 day(s) QLIKE : 0.55811205 full horizon MAE : 2.58105813 full horizon RMSE : 9.06859530 full horizon R2 : -0.02069214 full horizon Pearson r : 0.14998041 full horizon QLIKE : 0.55811205 --- Task 2 --- 1 day(s) MAE : 0.58801784 1 day(s) RMSE : 1.24304395 1 day(s) R2 : -0.28826318 1 day(s) Pearson r : 0.02206237 1 day(s) QLIKE : 14.47466948 3 day(s) MAE : 0.59012405 3 day(s) RMSE : 1.24572343 3 day(s) R2 : -0.28932136 3 day(s) Pearson r : 0.05276251 3 day(s) QLIKE : 13.92031835 5 day(s) MAE : 0.59116784 5 day(s) RMSE : 1.24663820 5 day(s) R2 : -0.29009848 5 day(s) Pearson r : 0.01634935 5 day(s) QLIKE : 13.76947273 10 day(s) MAE : 0.59762079 10 day(s) RMSE : 1.25432471 10 day(s) R2 : -0.29365152 10 day(s) Pearson r : 0.01606620 10 day(s) QLIKE : 13.96813981 full horizon MAE : 0.59762079 full horizon RMSE : 1.25432471 full horizon R2 : -0.29365152 full horizon Pearson r : 0.01606620 full horizon QLIKE : 13.96813981 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/AAPL/ITransformer_H10.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=2.40231, max=2.55167 === AAPL | H=20 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714034 Min value: -33.2571324878827 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.174045403839706 Min value: -2.3775104768031508 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.093969981528533 Min value: -1.2108152104959184 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.913020640489958 Min value: -1.6487782037743999 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.5044175194514073 Min value: -2.3858091292041257 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.906568131251448 Min value: -2.1419453045757613 Epoch 1/50 Train 30.4838 Val 0.8062 0.4s Epoch 2/50 Train 30.2814 Val 0.8056 0.2s Epoch 3/50 Train 30.0939 Val 0.8049 0.2s Epoch 4/50 Train 29.9122 Val 0.8043 0.2s Epoch 5/50 Train 29.7358 Val 0.8038 0.2s Epoch 6/50 Train 29.5644 Val 0.8032 0.2s Epoch 7/50 Train 29.3977 Val 0.8027 0.2s Epoch 8/50 Train 29.2353 Val 0.8021 0.2s Epoch 9/50 Train 29.0770 Val 0.8016 0.2s Epoch 10/50 Train 28.9225 Val 0.8011 0.2s Epoch 11/50 Train 28.7715 Val 0.8006 0.2s Epoch 12/50 Train 28.6237 Val 0.8001 0.4s Epoch 13/50 Train 28.4790 Val 0.7997 0.3s Epoch 14/50 Train 28.3371 Val 0.7992 0.3s Epoch 15/50 Train 28.1978 Val 0.7987 0.3s Epoch 16/50 Train 28.0610 Val 0.7983 0.3s Epoch 17/50 Train 27.9265 Val 0.7978 0.3s Epoch 18/50 Train 27.7941 Val 0.7974 0.3s Epoch 19/50 Train 27.6637 Val 0.7970 0.3s Epoch 20/50 Train 27.5353 Val 0.7965 0.4s Epoch 21/50 Train 27.4086 Val 0.7961 0.2s Epoch 22/50 Train 27.2836 Val 0.7957 0.2s Epoch 23/50 Train 27.1602 Val 0.7953 0.2s Epoch 24/50 Train 27.0383 Val 0.7949 0.3s Epoch 25/50 Train 26.9179 Val 0.7945 0.3s Epoch 26/50 Train 26.7989 Val 0.7941 0.3s Epoch 27/50 Train 26.6813 Val 0.7937 0.3s Epoch 28/50 Train 26.5651 Val 0.7933 0.2s Epoch 29/50 Train 26.4502 Val 0.7929 0.3s Epoch 30/50 Train 26.3368 Val 0.7925 0.3s Epoch 31/50 Train 26.2247 Val 0.7921 0.3s Epoch 32/50 Train 26.1140 Val 0.7917 0.4s Epoch 33/50 Train 26.0048 Val 0.7914 0.3s Epoch 34/50 Train 25.8971 Val 0.7910 0.3s Epoch 35/50 Train 25.7909 Val 0.7907 0.2s Epoch 36/50 Train 25.6864 Val 0.7903 0.2s Epoch 37/50 Train 25.5836 Val 0.7899 0.2s Epoch 38/50 Train 25.4826 Val 0.7896 0.2s Epoch 39/50 Train 25.3835 Val 0.7893 0.2s Epoch 40/50 Train 25.2864 Val 0.7889 0.2s Epoch 41/50 Train 25.1913 Val 0.7886 0.2s Epoch 42/50 Train 25.0984 Val 0.7883 0.2s Epoch 43/50 Train 25.0077 Val 0.7880 0.2s Epoch 44/50 Train 24.9193 Val 0.7877 0.3s Epoch 45/50 Train 24.8333 Val 0.7874 0.3s Epoch 46/50 Train 24.7497 Val 0.7871 0.3s Epoch 47/50 Train 24.6686 Val 0.7868 0.3s Epoch 48/50 Train 24.5900 Val 0.7865 0.3s Epoch 49/50 Train 24.5140 Val 0.7863 0.2s Epoch 50/50 Train 24.4406 Val 0.7860 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 20 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.44816797 1 day(s) RMSE : 8.71510547 1 day(s) R2 : -0.01685317 1 day(s) Pearson r : 0.19098873 1 day(s) QLIKE : 0.53354865 3 day(s) MAE : 2.53511345 3 day(s) RMSE : 8.97212464 3 day(s) R2 : -0.01861788 3 day(s) Pearson r : 0.22948939 3 day(s) QLIKE : 0.55087659 5 day(s) MAE : 2.55890649 5 day(s) RMSE : 9.02527036 5 day(s) R2 : -0.01934766 5 day(s) Pearson r : 0.17730911 5 day(s) QLIKE : 0.55522359 10 day(s) MAE : 2.58530029 10 day(s) RMSE : 9.06650799 10 day(s) R2 : -0.02022233 10 day(s) Pearson r : 0.14582351 10 day(s) QLIKE : 0.55817178 20 day(s) MAE : 2.61233843 20 day(s) RMSE : 9.09177679 20 day(s) R2 : -0.02155314 20 day(s) Pearson r : 0.10634949 20 day(s) QLIKE : 0.55863232 full horizon MAE : 2.61233843 full horizon RMSE : 9.09177679 full horizon R2 : -0.02155314 full horizon Pearson r : 0.10634949 full horizon QLIKE : 0.55863232 --- Task 2 --- 1 day(s) MAE : 0.58801968 1 day(s) RMSE : 1.24304510 1 day(s) R2 : -0.28826556 1 day(s) Pearson r : 0.01640296 1 day(s) QLIKE : 14.45751552 3 day(s) MAE : 0.59012454 3 day(s) RMSE : 1.24572290 3 day(s) R2 : -0.28932026 3 day(s) Pearson r : 0.06044757 3 day(s) QLIKE : 13.89842196 5 day(s) MAE : 0.59116803 5 day(s) RMSE : 1.24663787 5 day(s) R2 : -0.29009778 5 day(s) Pearson r : 0.02699362 5 day(s) QLIKE : 13.72607222 10 day(s) MAE : 0.59762076 10 day(s) RMSE : 1.25432436 10 day(s) R2 : -0.29365080 10 day(s) Pearson r : 0.02647000 10 day(s) QLIKE : 13.87239803 20 day(s) MAE : 0.60268958 20 day(s) RMSE : 1.25998970 20 day(s) R2 : -0.29666513 20 day(s) Pearson r : 0.00759941 20 day(s) QLIKE : 13.91005691 full horizon MAE : 0.60268958 full horizon RMSE : 1.25998970 full horizon R2 : -0.29666513 full horizon Pearson r : 0.00759941 full horizon QLIKE : 13.91005691 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/AAPL/ITransformer_H20.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=2.41599, max=2.5724 === MSFT | H=1 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.550928529745745 Min value: -8.748202192355542 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.218119847017174 Min value: -2.8051449830585735 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.2612343368490615 Min value: -4.427436593443818 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.8947596773346853 Min value: -1.3150487901568413 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.030086059790657 Min value: -3.528134593538675 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.930619914036179 Min value: -2.3299350512435675 Epoch 1/50 Train 25.5171 Val 0.8516 0.2s Epoch 2/50 Train 24.8413 Val 0.8399 0.2s Epoch 3/50 Train 24.2874 Val 0.8300 0.2s Epoch 4/50 Train 23.8052 Val 0.8215 0.2s Epoch 5/50 Train 23.3828 Val 0.8142 0.2s Epoch 6/50 Train 23.0105 Val 0.8079 0.2s Epoch 7/50 Train 22.6803 Val 0.8024 0.2s Epoch 8/50 Train 22.3860 Val 0.7977 0.2s Epoch 9/50 Train 22.1221 Val 0.7936 0.2s Epoch 10/50 Train 21.8846 Val 0.7900 0.2s Epoch 11/50 Train 21.6697 Val 0.7868 0.2s Epoch 12/50 Train 21.4744 Val 0.7841 0.2s Epoch 13/50 Train 21.2964 Val 0.7817 0.2s Epoch 14/50 Train 21.1334 Val 0.7796 0.2s Epoch 15/50 Train 20.9836 Val 0.7778 0.3s Epoch 16/50 Train 20.8455 Val 0.7762 0.2s Epoch 17/50 Train 20.7177 Val 0.7749 0.2s Epoch 18/50 Train 20.5992 Val 0.7737 0.2s Epoch 19/50 Train 20.4889 Val 0.7727 0.2s Epoch 20/50 Train 20.3859 Val 0.7718 0.2s Epoch 21/50 Train 20.2895 Val 0.7710 0.2s Epoch 22/50 Train 20.1991 Val 0.7703 0.2s Epoch 23/50 Train 20.1139 Val 0.7698 0.2s Epoch 24/50 Train 20.0336 Val 0.7693 0.2s Epoch 25/50 Train 19.9576 Val 0.7689 0.2s Epoch 26/50 Train 19.8856 Val 0.7685 0.3s Epoch 27/50 Train 19.8172 Val 0.7682 0.3s Epoch 28/50 Train 19.7521 Val 0.7680 0.2s Epoch 29/50 Train 19.6899 Val 0.7678 0.3s Epoch 30/50 Train 19.6304 Val 0.7676 0.3s Epoch 31/50 Train 19.5735 Val 0.7675 0.3s Epoch 32/50 Train 19.5188 Val 0.7674 0.3s Epoch 33/50 Train 19.4663 Val 0.7673 0.2s Epoch 34/50 Train 19.4156 Val 0.7673 0.3s Epoch 35/50 Train 19.3668 Val 0.7673 0.3s Epoch 36/50 Train 19.3196 Val 0.7672 0.3s Epoch 37/50 Train 19.2739 Val 0.7672 0.3s Epoch 38/50 Train 19.2297 Val 0.7673 0.2s Epoch 39/50 Train 19.1867 Val 0.7673 0.2s Epoch 40/50 Train 19.1449 Val 0.7673 0.2s Epoch 41/50 Train 19.1043 Val 0.7674 0.2s Epoch 42/50 Train 19.0647 Val 0.7674 0.2s Epoch 43/50 Train 19.0261 Val 0.7675 0.2s Epoch 44/50 Train 18.9884 Val 0.7675 0.2s Epoch 45/50 Train 18.9515 Val 0.7676 0.2s Epoch 46/50 Train 18.9154 Val 0.7677 0.2s Early stopping triggered. Parameters used in the single-fit model: input_len: 60 output_len: 1 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 1.93130813 1 day(s) RMSE : 6.64769993 1 day(s) R2 : 0.01636499 1 day(s) Pearson r : 0.22558978 1 day(s) QLIKE : 0.41528547 full horizon MAE : 1.93130813 full horizon RMSE : 6.64769993 full horizon R2 : 0.01636499 full horizon Pearson r : 0.22558978 full horizon QLIKE : 0.41528547 --- Task 2 --- 1 day(s) MAE : 0.53391318 1 day(s) RMSE : 0.99900463 1 day(s) R2 : -0.39964879 1 day(s) Pearson r : 0.00627441 1 day(s) QLIKE : 16.38492426 full horizon MAE : 0.53391318 full horizon RMSE : 0.99900463 full horizon R2 : -0.39964879 full horizon Pearson r : 0.00627441 full horizon QLIKE : 16.38492426 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/MSFT/ITransformer_H1.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=0.963975, max=7.47938 === MSFT | H=5 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.550928529745745 Min value: -8.748202192355542 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.218387359181361 Min value: -2.808479041395319 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.2612343368490615 Min value: -4.427436593443818 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.894348562811063 Min value: -1.3176187186164212 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.030086059790657 Min value: -3.528134593538675 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.930739994585198 Min value: -2.3330254192007547 Epoch 1/50 Train 27.9544 Val 0.9425 0.2s Epoch 2/50 Train 27.4853 Val 0.9333 0.2s Epoch 3/50 Train 27.0721 Val 0.9248 0.2s Epoch 4/50 Train 26.6884 Val 0.9170 0.2s Epoch 5/50 Train 26.3314 Val 0.9097 0.2s Epoch 6/50 Train 25.9987 Val 0.9029 0.2s Epoch 7/50 Train 25.6879 Val 0.8965 0.2s Epoch 8/50 Train 25.3972 Val 0.8905 0.3s Epoch 9/50 Train 25.1247 Val 0.8849 0.2s Epoch 10/50 Train 24.8690 Val 0.8797 0.3s Epoch 11/50 Train 24.6287 Val 0.8747 0.2s Epoch 12/50 Train 24.4026 Val 0.8701 0.2s Epoch 13/50 Train 24.1896 Val 0.8657 0.2s Epoch 14/50 Train 23.9887 Val 0.8615 0.2s Epoch 15/50 Train 23.7990 Val 0.8576 0.2s Epoch 16/50 Train 23.6198 Val 0.8540 0.2s Epoch 17/50 Train 23.4503 Val 0.8505 0.2s Epoch 18/50 Train 23.2898 Val 0.8472 0.2s Epoch 19/50 Train 23.1378 Val 0.8441 0.2s Epoch 20/50 Train 22.9936 Val 0.8412 0.2s Epoch 21/50 Train 22.8568 Val 0.8384 0.2s Epoch 22/50 Train 22.7268 Val 0.8358 0.2s Epoch 23/50 Train 22.6032 Val 0.8333 0.2s Epoch 24/50 Train 22.4857 Val 0.8309 0.3s Epoch 25/50 Train 22.3738 Val 0.8287 0.3s Epoch 26/50 Train 22.2671 Val 0.8266 0.3s Epoch 27/50 Train 22.1654 Val 0.8246 0.3s Epoch 28/50 Train 22.0684 Val 0.8227 0.4s Epoch 29/50 Train 21.9757 Val 0.8208 0.3s Epoch 30/50 Train 21.8871 Val 0.8191 0.3s Epoch 31/50 Train 21.8023 Val 0.8175 0.3s Epoch 32/50 Train 21.7212 Val 0.8159 0.3s Epoch 33/50 Train 21.6434 Val 0.8144 0.3s Epoch 34/50 Train 21.5689 Val 0.8130 0.3s Epoch 35/50 Train 21.4974 Val 0.8117 0.3s Epoch 36/50 Train 21.4287 Val 0.8104 0.4s Epoch 37/50 Train 21.3627 Val 0.8092 0.2s Epoch 38/50 Train 21.2993 Val 0.8080 0.2s Epoch 39/50 Train 21.2382 Val 0.8069 0.2s Epoch 40/50 Train 21.1794 Val 0.8058 0.2s Epoch 41/50 Train 21.1228 Val 0.8048 0.2s Epoch 42/50 Train 21.0681 Val 0.8038 0.2s Epoch 43/50 Train 21.0154 Val 0.8029 0.3s Epoch 44/50 Train 20.9644 Val 0.8020 0.2s Epoch 45/50 Train 20.9152 Val 0.8011 0.3s Epoch 46/50 Train 20.8676 Val 0.8003 0.2s Epoch 47/50 Train 20.8215 Val 0.7995 0.2s Epoch 48/50 Train 20.7769 Val 0.7988 0.2s Epoch 49/50 Train 20.7337 Val 0.7980 0.2s Epoch 50/50 Train 20.6918 Val 0.7974 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 5 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 1.97627461 1 day(s) RMSE : 6.68501075 1 day(s) R2 : 0.00529253 1 day(s) Pearson r : 0.18699502 1 day(s) QLIKE : 0.43123132 3 day(s) MAE : 1.99440687 3 day(s) RMSE : 6.74965851 3 day(s) R2 : -0.01410704 3 day(s) Pearson r : 0.10656162 3 day(s) QLIKE : 0.45415782 5 day(s) MAE : 2.03107874 5 day(s) RMSE : 6.77996215 5 day(s) R2 : -0.02311867 5 day(s) Pearson r : 0.06330995 5 day(s) QLIKE : 0.46746330 full horizon MAE : 2.03107874 full horizon RMSE : 6.77996215 full horizon R2 : -0.02311867 full horizon Pearson r : 0.06330995 full horizon QLIKE : 0.46746330 --- Task 2 --- 1 day(s) MAE : 0.53384152 1 day(s) RMSE : 0.99891076 1 day(s) R2 : -0.39938579 1 day(s) Pearson r : 0.03816504 1 day(s) QLIKE : 16.97207801 3 day(s) MAE : 0.53222354 3 day(s) RMSE : 0.99802797 3 day(s) R2 : -0.39719198 3 day(s) Pearson r : 0.01506260 3 day(s) QLIKE : 16.78885061 5 day(s) MAE : 0.53116226 5 day(s) RMSE : 0.99730565 5 day(s) R2 : -0.39581962 5 day(s) Pearson r : 0.01332828 5 day(s) QLIKE : 16.81870497 full horizon MAE : 0.53116226 full horizon RMSE : 0.99730565 full horizon R2 : -0.39581962 full horizon Pearson r : 0.01332828 full horizon QLIKE : 16.81870497 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/MSFT/ITransformer_H5.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=0.904334, max=8.09818 === MSFT | H=10 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.550928529745745 Min value: -8.748202192355542 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.220877759176237 Min value: -2.814364260545607 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.2612343368490615 Min value: -4.427436593443818 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.895260784986747 Min value: -1.3217269183172993 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.030086059790657 Min value: -3.528134593538675 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.9328875355250386 Min value: -2.3383439250774902 Epoch 1/50 Train 28.0299 Val 0.9400 0.3s Epoch 2/50 Train 27.6940 Val 0.9329 0.3s Epoch 3/50 Train 27.3928 Val 0.9263 0.2s Epoch 4/50 Train 27.1079 Val 0.9200 0.2s Epoch 5/50 Train 26.8382 Val 0.9141 0.2s Epoch 6/50 Train 26.5826 Val 0.9084 0.2s Epoch 7/50 Train 26.3402 Val 0.9031 0.2s Epoch 8/50 Train 26.1100 Val 0.8980 0.2s Epoch 9/50 Train 25.8913 Val 0.8932 0.2s Epoch 10/50 Train 25.6832 Val 0.8886 0.3s Epoch 11/50 Train 25.4852 Val 0.8842 0.2s Epoch 12/50 Train 25.2965 Val 0.8800 0.2s Epoch 13/50 Train 25.1166 Val 0.8760 0.2s Epoch 14/50 Train 24.9450 Val 0.8722 0.2s Epoch 15/50 Train 24.7811 Val 0.8686 0.3s Epoch 16/50 Train 24.6246 Val 0.8651 0.3s Epoch 17/50 Train 24.4750 Val 0.8617 0.3s Epoch 18/50 Train 24.3318 Val 0.8585 0.3s Epoch 19/50 Train 24.1949 Val 0.8554 0.2s Epoch 20/50 Train 24.0637 Val 0.8524 0.2s Epoch 21/50 Train 23.9381 Val 0.8496 0.2s Epoch 22/50 Train 23.8176 Val 0.8469 0.2s Epoch 23/50 Train 23.7021 Val 0.8443 0.2s Epoch 24/50 Train 23.5913 Val 0.8418 0.3s Epoch 25/50 Train 23.4850 Val 0.8393 0.3s Epoch 26/50 Train 23.3828 Val 0.8370 0.3s Epoch 27/50 Train 23.2847 Val 0.8348 0.3s Epoch 28/50 Train 23.1904 Val 0.8326 0.3s Epoch 29/50 Train 23.0997 Val 0.8306 0.3s Epoch 30/50 Train 23.0124 Val 0.8286 0.3s Epoch 31/50 Train 22.9284 Val 0.8267 0.3s Epoch 32/50 Train 22.8476 Val 0.8248 0.3s Epoch 33/50 Train 22.7697 Val 0.8230 0.2s Epoch 34/50 Train 22.6946 Val 0.8213 0.2s Epoch 35/50 Train 22.6222 Val 0.8197 0.2s Epoch 36/50 Train 22.5524 Val 0.8181 0.2s Epoch 37/50 Train 22.4851 Val 0.8166 0.2s Epoch 38/50 Train 22.4200 Val 0.8151 0.2s Epoch 39/50 Train 22.3572 Val 0.8137 0.2s Epoch 40/50 Train 22.2965 Val 0.8123 0.2s Epoch 41/50 Train 22.2379 Val 0.8109 0.2s Epoch 42/50 Train 22.1811 Val 0.8097 0.2s Epoch 43/50 Train 22.1262 Val 0.8084 0.2s Epoch 44/50 Train 22.0731 Val 0.8072 0.2s Epoch 45/50 Train 22.0216 Val 0.8061 0.3s Epoch 46/50 Train 21.9718 Val 0.8049 0.2s Epoch 47/50 Train 21.9234 Val 0.8039 0.2s Epoch 48/50 Train 21.8766 Val 0.8028 0.3s Epoch 49/50 Train 21.8311 Val 0.8018 0.2s Epoch 50/50 Train 21.7870 Val 0.8008 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 10 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 1.97860969 1 day(s) RMSE : 6.67293078 1 day(s) R2 : 0.00888420 1 day(s) Pearson r : 0.19335122 1 day(s) QLIKE : 0.43269424 3 day(s) MAE : 2.00713042 3 day(s) RMSE : 6.75287876 3 day(s) R2 : -0.01507493 3 day(s) Pearson r : 0.09987256 3 day(s) QLIKE : 0.46023647 5 day(s) MAE : 2.04797289 5 day(s) RMSE : 6.78637125 5 day(s) R2 : -0.02505389 5 day(s) Pearson r : 0.05309725 5 day(s) QLIKE : 0.47401673 10 day(s) MAE : 2.07400893 10 day(s) RMSE : 6.81344559 10 day(s) R2 : -0.03317338 10 day(s) Pearson r : 0.01635198 10 day(s) QLIKE : 0.48986187 full horizon MAE : 2.07400893 full horizon RMSE : 6.81344559 full horizon R2 : -0.03317338 full horizon Pearson r : 0.01635198 full horizon QLIKE : 0.48986187 --- Task 2 --- 1 day(s) MAE : 0.53382213 1 day(s) RMSE : 0.99887603 1 day(s) R2 : -0.39928849 1 day(s) Pearson r : 0.04221130 1 day(s) QLIKE : 18.17372898 3 day(s) MAE : 0.53220952 3 day(s) RMSE : 0.99800514 3 day(s) R2 : -0.39712805 3 day(s) Pearson r : 0.01894964 3 day(s) QLIKE : 17.53100670 5 day(s) MAE : 0.53116278 5 day(s) RMSE : 0.99729538 5 day(s) R2 : -0.39579086 5 day(s) Pearson r : 0.01090968 5 day(s) QLIKE : 17.24045445 10 day(s) MAE : 0.53241592 10 day(s) RMSE : 1.00140148 10 day(s) R2 : -0.39386703 10 day(s) Pearson r : -0.00824376 10 day(s) QLIKE : 17.50695789 full horizon MAE : 0.53241592 full horizon RMSE : 1.00140148 full horizon R2 : -0.39386703 full horizon Pearson r : -0.00824376 full horizon QLIKE : 17.50695789 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/MSFT/ITransformer_H10.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=0.712367, max=9.86555 === MSFT | H=20 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.550928529745745 Min value: -8.748202192355542 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.21818909955111 Min value: -2.8217406454840073 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.2612343368490615 Min value: -4.427436593443818 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.8916888397222356 Min value: -1.3281087285370063 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.030086059790657 Min value: -3.528134593538675 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.9300069821373937 Min value: -2.3454031279530767 Epoch 1/50 Train 29.1267 Val 0.9468 0.3s Epoch 2/50 Train 28.8411 Val 0.9398 0.2s Epoch 3/50 Train 28.5817 Val 0.9332 0.2s Epoch 4/50 Train 28.3338 Val 0.9269 0.3s Epoch 5/50 Train 28.0966 Val 0.9208 0.3s Epoch 6/50 Train 27.8695 Val 0.9151 0.3s Epoch 7/50 Train 27.6520 Val 0.9096 0.3s Epoch 8/50 Train 27.4436 Val 0.9043 0.2s Epoch 9/50 Train 27.2437 Val 0.8993 0.2s Epoch 10/50 Train 27.0519 Val 0.8944 0.2s Epoch 11/50 Train 26.8676 Val 0.8897 0.3s Epoch 12/50 Train 26.6906 Val 0.8852 0.2s Epoch 13/50 Train 26.5203 Val 0.8809 0.2s Epoch 14/50 Train 26.3565 Val 0.8767 0.3s Epoch 15/50 Train 26.1987 Val 0.8727 0.3s Epoch 16/50 Train 26.0468 Val 0.8688 0.2s Epoch 17/50 Train 25.9002 Val 0.8650 0.2s Epoch 18/50 Train 25.7589 Val 0.8614 0.2s Epoch 19/50 Train 25.6225 Val 0.8579 0.2s Epoch 20/50 Train 25.4908 Val 0.8544 0.3s Epoch 21/50 Train 25.3636 Val 0.8511 0.2s Epoch 22/50 Train 25.2406 Val 0.8479 0.3s Epoch 23/50 Train 25.1217 Val 0.8448 0.3s Epoch 24/50 Train 25.0067 Val 0.8417 0.4s Epoch 25/50 Train 24.8954 Val 0.8388 0.3s Epoch 26/50 Train 24.7876 Val 0.8359 0.3s Epoch 27/50 Train 24.6833 Val 0.8331 0.3s Epoch 28/50 Train 24.5822 Val 0.8304 0.4s Epoch 29/50 Train 24.4844 Val 0.8277 0.3s Epoch 30/50 Train 24.3895 Val 0.8251 0.3s Epoch 31/50 Train 24.2976 Val 0.8226 0.3s Epoch 32/50 Train 24.2085 Val 0.8202 0.3s Epoch 33/50 Train 24.1221 Val 0.8178 0.3s Epoch 34/50 Train 24.0384 Val 0.8154 0.2s Epoch 35/50 Train 23.9573 Val 0.8132 0.2s Epoch 36/50 Train 23.8786 Val 0.8109 0.2s Epoch 37/50 Train 23.8023 Val 0.8088 0.3s Epoch 38/50 Train 23.7283 Val 0.8067 0.3s Epoch 39/50 Train 23.6566 Val 0.8046 0.3s Epoch 40/50 Train 23.5871 Val 0.8026 0.3s Epoch 41/50 Train 23.5197 Val 0.8006 0.3s Epoch 42/50 Train 23.4545 Val 0.7987 0.4s Epoch 43/50 Train 23.3912 Val 0.7968 0.3s Epoch 44/50 Train 23.3299 Val 0.7950 0.2s Epoch 45/50 Train 23.2704 Val 0.7933 0.4s Epoch 46/50 Train 23.2129 Val 0.7915 0.3s Epoch 47/50 Train 23.1572 Val 0.7899 0.2s Epoch 48/50 Train 23.1032 Val 0.7882 0.2s Epoch 49/50 Train 23.0509 Val 0.7866 0.2s Epoch 50/50 Train 23.0003 Val 0.7851 0.3s Parameters used in the single-fit model: input_len: 60 output_len: 20 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.00875725 1 day(s) RMSE : 6.71183230 1 day(s) R2 : -0.00270540 1 day(s) Pearson r : 0.14173295 1 day(s) QLIKE : 0.44635972 3 day(s) MAE : 2.02714534 3 day(s) RMSE : 6.77199896 3 day(s) R2 : -0.02083126 3 day(s) Pearson r : 0.06799888 3 day(s) QLIKE : 0.47016016 5 day(s) MAE : 2.06433190 5 day(s) RMSE : 6.79927618 5 day(s) R2 : -0.02895608 5 day(s) Pearson r : 0.02900698 5 day(s) QLIKE : 0.48254484 10 day(s) MAE : 2.08355063 10 day(s) RMSE : 6.82007655 10 day(s) R2 : -0.03518536 10 day(s) Pearson r : 0.00250382 10 day(s) QLIKE : 0.49375671 20 day(s) MAE : 2.13999928 20 day(s) RMSE : 7.03238608 20 day(s) R2 : -0.03712239 20 day(s) Pearson r : 0.00256215 20 day(s) QLIKE : 0.50488663 full horizon MAE : 2.13999928 full horizon RMSE : 7.03238608 full horizon R2 : -0.03712239 full horizon Pearson r : 0.00256215 full horizon QLIKE : 0.50488663 --- Task 2 --- 1 day(s) MAE : 0.53387619 1 day(s) RMSE : 0.99894852 1 day(s) R2 : -0.39949158 1 day(s) Pearson r : 0.03017229 1 day(s) QLIKE : 18.69979925 3 day(s) MAE : 0.53221993 3 day(s) RMSE : 0.99802182 3 day(s) R2 : -0.39717475 3 day(s) Pearson r : 0.01073724 3 day(s) QLIKE : 17.78811653 5 day(s) MAE : 0.53117582 5 day(s) RMSE : 0.99730759 5 day(s) R2 : -0.39582504 5 day(s) Pearson r : -0.00073708 5 day(s) QLIKE : 17.42405422 10 day(s) MAE : 0.53239823 10 day(s) RMSE : 1.00137634 10 day(s) R2 : -0.39379703 10 day(s) Pearson r : -0.00549544 10 day(s) QLIKE : 17.93553742 20 day(s) MAE : 0.54570849 20 day(s) RMSE : 1.03415264 20 day(s) R2 : -0.38559834 20 day(s) Pearson r : -0.00101217 20 day(s) QLIKE : 18.01281534 full horizon MAE : 0.54570849 full horizon RMSE : 1.03415264 full horizon R2 : -0.38559834 full horizon Pearson r : -0.00101217 full horizon QLIKE : 18.01281534 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/MSFT/ITransformer_H20.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=0.656947, max=9.50526 === GE | H=1 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238581 Min value: -3.2166288924657587 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.030029259880507 Min value: -2.6526564326715185 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.008530017154456 Min value: -3.2166288924657587 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.0718644033108666 Min value: -2.3663072843518616 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.6741296103710765 Min value: -4.217627703807063 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.646703896552387 Min value: -2.1484208255749326 Epoch 1/50 Train 29.3338 Val 0.9223 0.2s Epoch 2/50 Train 28.7270 Val 0.9226 0.2s Epoch 3/50 Train 28.2121 Val 0.9229 0.2s Epoch 4/50 Train 27.7526 Val 0.9232 0.2s Epoch 5/50 Train 27.3401 Val 0.9234 0.2s Epoch 6/50 Train 26.9682 Val 0.9236 0.2s Epoch 7/50 Train 26.6317 Val 0.9238 0.3s Epoch 8/50 Train 26.3264 Val 0.9239 0.2s Epoch 9/50 Train 26.0486 Val 0.9241 0.2s Epoch 10/50 Train 25.7954 Val 0.9242 0.2s Epoch 11/50 Train 25.5642 Val 0.9243 0.2s Epoch 12/50 Train 25.3525 Val 0.9245 0.2s Epoch 13/50 Train 25.1585 Val 0.9246 0.2s Epoch 14/50 Train 24.9804 Val 0.9247 0.2s Epoch 15/50 Train 24.8167 Val 0.9248 0.2s Epoch 16/50 Train 24.6659 Val 0.9249 0.2s Epoch 17/50 Train 24.5269 Val 0.9250 0.2s Epoch 18/50 Train 24.3985 Val 0.9251 0.2s Epoch 19/50 Train 24.2799 Val 0.9252 0.2s Epoch 20/50 Train 24.1701 Val 0.9254 0.2s Epoch 21/50 Train 24.0683 Val 0.9255 0.2s Epoch 22/50 Train 23.9739 Val 0.9256 0.2s Epoch 23/50 Train 23.8862 Val 0.9257 0.2s Epoch 24/50 Train 23.8047 Val 0.9257 0.2s Epoch 25/50 Train 23.7289 Val 0.9258 0.2s Epoch 26/50 Train 23.6581 Val 0.9259 0.2s Epoch 27/50 Train 23.5922 Val 0.9260 0.2s Epoch 28/50 Train 23.5306 Val 0.9261 0.2s Epoch 29/50 Train 23.4730 Val 0.9262 0.2s Epoch 30/50 Train 23.4190 Val 0.9262 0.2s Early stopping triggered. Parameters used in the single-fit model: input_len: 60 output_len: 1 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.96505141 1 day(s) RMSE : 27.44607199 1 day(s) R2 : -0.00145075 1 day(s) Pearson r : -0.02881735 1 day(s) QLIKE : 0.68548926 full horizon MAE : 4.96505141 full horizon RMSE : 27.44607199 full horizon R2 : -0.00145075 full horizon Pearson r : -0.02881735 full horizon QLIKE : 0.68548926 --- Task 2 --- 1 day(s) MAE : 0.77053005 1 day(s) RMSE : 1.45653670 1 day(s) R2 : -0.38861111 1 day(s) Pearson r : -0.04783417 1 day(s) QLIKE : 18.02566618 full horizon MAE : 0.77053005 full horizon RMSE : 1.45653670 full horizon R2 : -0.38861111 full horizon Pearson r : -0.04783417 full horizon QLIKE : 18.02566618 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GE/ITransformer_H1.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=4.71213, max=5.16528 === GE | H=5 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238581 Min value: -3.2166288924657587 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.05628901565783 Min value: -2.6636414114466582 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.008530017154456 Min value: -3.2166288924657587 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.0759413314511823 Min value: -2.376379373890814 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.6741296103710765 Min value: -4.217627703807063 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.658989486293699 Min value: -2.1577982868808494 Epoch 1/50 Train 30.2684 Val 0.9250 0.2s Epoch 2/50 Train 29.9248 Val 0.9249 0.2s Epoch 3/50 Train 29.6151 Val 0.9248 0.2s Epoch 4/50 Train 29.3217 Val 0.9247 0.2s Epoch 5/50 Train 29.0430 Val 0.9246 0.2s Epoch 6/50 Train 28.7777 Val 0.9246 0.2s Epoch 7/50 Train 28.5248 Val 0.9245 0.3s Epoch 8/50 Train 28.2837 Val 0.9245 0.2s Epoch 9/50 Train 28.0536 Val 0.9244 0.2s Epoch 10/50 Train 27.8339 Val 0.9244 0.2s Epoch 11/50 Train 27.6241 Val 0.9244 0.2s Epoch 12/50 Train 27.4236 Val 0.9244 0.2s Epoch 13/50 Train 27.2320 Val 0.9244 0.2s Epoch 14/50 Train 27.0488 Val 0.9244 0.2s Epoch 15/50 Train 26.8737 Val 0.9244 0.2s Epoch 16/50 Train 26.7062 Val 0.9244 0.2s Epoch 17/50 Train 26.5459 Val 0.9244 0.3s Epoch 18/50 Train 26.3926 Val 0.9245 0.3s Epoch 19/50 Train 26.2459 Val 0.9245 0.3s Epoch 20/50 Train 26.1055 Val 0.9245 0.3s Epoch 21/50 Train 25.9711 Val 0.9245 0.3s Epoch 22/50 Train 25.8424 Val 0.9246 0.3s Epoch 23/50 Train 25.7191 Val 0.9246 0.3s Epoch 24/50 Train 25.6011 Val 0.9246 0.3s Epoch 25/50 Train 25.4881 Val 0.9247 0.3s Epoch 26/50 Train 25.3798 Val 0.9247 0.2s Epoch 27/50 Train 25.2760 Val 0.9247 0.2s Epoch 28/50 Train 25.1766 Val 0.9248 0.2s Epoch 29/50 Train 25.0813 Val 0.9248 0.2s Epoch 30/50 Train 24.9900 Val 0.9249 0.2s Early stopping triggered. Parameters used in the single-fit model: input_len: 60 output_len: 5 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.97329695 1 day(s) RMSE : 27.44592959 1 day(s) R2 : -0.00144035 1 day(s) Pearson r : -0.03156749 1 day(s) QLIKE : 0.68581265 3 day(s) MAE : 4.96943146 3 day(s) RMSE : 27.44486653 3 day(s) R2 : -0.00142040 3 day(s) Pearson r : -0.01691991 3 day(s) QLIKE : 0.68373671 5 day(s) MAE : 4.96270928 5 day(s) RMSE : 27.44452205 5 day(s) R2 : -0.00145262 5 day(s) Pearson r : -0.01765190 5 day(s) QLIKE : 0.68244365 full horizon MAE : 4.96270928 full horizon RMSE : 27.44452205 full horizon R2 : -0.00145262 full horizon Pearson r : -0.01765190 full horizon QLIKE : 0.68244365 --- Task 2 --- 1 day(s) MAE : 0.77053074 1 day(s) RMSE : 1.45653642 1 day(s) R2 : -0.38861057 1 day(s) Pearson r : -0.04526911 1 day(s) QLIKE : 19.03529012 3 day(s) MAE : 0.77441596 3 day(s) RMSE : 1.45967024 3 day(s) R2 : -0.39172243 3 day(s) Pearson r : 0.12716003 3 day(s) QLIKE : 18.03475461 5 day(s) MAE : 0.77519278 5 day(s) RMSE : 1.46029898 5 day(s) R2 : -0.39235186 5 day(s) Pearson r : 0.06924680 5 day(s) QLIKE : 17.56052773 full horizon MAE : 0.77519278 full horizon RMSE : 1.46029898 full horizon R2 : -0.39235186 full horizon Pearson r : 0.06924680 full horizon QLIKE : 17.56052773 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GE/ITransformer_H5.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=4.67113, max=5.28734 === GE | H=10 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238581 Min value: -3.2166288924657587 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.08059546680979 Min value: -2.6744326686468765 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.008530017154456 Min value: -3.2166288924657587 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.079343715915981 Min value: -2.3863103666656165 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.6741296103710765 Min value: -4.217627703807063 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.670127332275285 Min value: -2.1670746942644765 Epoch 1/50 Train 30.2557 Val 0.9449 0.2s Epoch 2/50 Train 29.9969 Val 0.9446 0.2s Epoch 3/50 Train 29.7608 Val 0.9444 0.2s Epoch 4/50 Train 29.5342 Val 0.9442 0.3s Epoch 5/50 Train 29.3160 Val 0.9440 0.2s Epoch 6/50 Train 29.1055 Val 0.9439 0.2s Epoch 7/50 Train 28.9023 Val 0.9437 0.2s Epoch 8/50 Train 28.7060 Val 0.9435 0.2s Epoch 9/50 Train 28.5162 Val 0.9434 0.2s Epoch 10/50 Train 28.3327 Val 0.9433 0.2s Epoch 11/50 Train 28.1552 Val 0.9431 0.2s Epoch 12/50 Train 27.9835 Val 0.9430 0.2s Epoch 13/50 Train 27.8173 Val 0.9429 0.2s Epoch 14/50 Train 27.6565 Val 0.9428 0.3s Epoch 15/50 Train 27.5010 Val 0.9427 0.3s Epoch 16/50 Train 27.3504 Val 0.9426 0.3s Epoch 17/50 Train 27.2047 Val 0.9425 0.3s Epoch 18/50 Train 27.0636 Val 0.9424 0.3s Epoch 19/50 Train 26.9271 Val 0.9423 0.3s Epoch 20/50 Train 26.7950 Val 0.9423 0.3s Epoch 21/50 Train 26.6671 Val 0.9422 0.3s Epoch 22/50 Train 26.5433 Val 0.9421 0.2s Epoch 23/50 Train 26.4236 Val 0.9421 0.3s Epoch 24/50 Train 26.3077 Val 0.9420 0.3s Epoch 25/50 Train 26.1955 Val 0.9419 0.3s Epoch 26/50 Train 26.0870 Val 0.9419 0.3s Epoch 27/50 Train 25.9819 Val 0.9418 0.3s Epoch 28/50 Train 25.8803 Val 0.9418 0.3s Epoch 29/50 Train 25.7820 Val 0.9417 0.3s Epoch 30/50 Train 25.6869 Val 0.9417 0.3s Epoch 31/50 Train 25.5949 Val 0.9417 0.3s Epoch 32/50 Train 25.5059 Val 0.9416 0.3s Epoch 33/50 Train 25.4198 Val 0.9416 0.4s Epoch 34/50 Train 25.3365 Val 0.9415 0.2s Epoch 35/50 Train 25.2559 Val 0.9415 0.5s Epoch 36/50 Train 25.1780 Val 0.9415 0.2s Epoch 37/50 Train 25.1026 Val 0.9415 0.2s Epoch 38/50 Train 25.0298 Val 0.9414 0.2s Epoch 39/50 Train 24.9593 Val 0.9414 0.2s Epoch 40/50 Train 24.8911 Val 0.9414 0.2s Epoch 41/50 Train 24.8252 Val 0.9413 0.2s Epoch 42/50 Train 24.7614 Val 0.9413 0.2s Epoch 43/50 Train 24.6998 Val 0.9413 0.2s Epoch 44/50 Train 24.6402 Val 0.9413 0.2s Epoch 45/50 Train 24.5825 Val 0.9412 0.2s Epoch 46/50 Train 24.5268 Val 0.9412 0.2s Epoch 47/50 Train 24.4728 Val 0.9412 0.2s Epoch 48/50 Train 24.4207 Val 0.9412 0.2s Epoch 49/50 Train 24.3703 Val 0.9412 0.2s Epoch 50/50 Train 24.3215 Val 0.9411 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 10 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.98166857 1 day(s) RMSE : 27.44533740 1 day(s) R2 : -0.00139714 1 day(s) Pearson r : -0.03193386 1 day(s) QLIKE : 0.68572016 3 day(s) MAE : 4.97740417 3 day(s) RMSE : 27.44430784 3 day(s) R2 : -0.00137963 3 day(s) Pearson r : -0.01658014 3 day(s) QLIKE : 0.68365778 5 day(s) MAE : 4.97070491 5 day(s) RMSE : 27.44398055 5 day(s) R2 : -0.00141310 5 day(s) Pearson r : -0.01790147 5 day(s) QLIKE : 0.68238946 10 day(s) MAE : 4.97193386 10 day(s) RMSE : 27.44708005 10 day(s) R2 : -0.00149232 10 day(s) Pearson r : -0.01026894 10 day(s) QLIKE : 0.67930857 full horizon MAE : 4.97193386 full horizon RMSE : 27.44708005 full horizon R2 : -0.00149232 full horizon Pearson r : -0.01026894 full horizon QLIKE : 0.67930857 --- Task 2 --- 1 day(s) MAE : 0.77053042 1 day(s) RMSE : 1.45653646 1 day(s) R2 : -0.38861065 1 day(s) Pearson r : -0.04314626 1 day(s) QLIKE : 18.48655255 3 day(s) MAE : 0.77441630 3 day(s) RMSE : 1.45967396 3 day(s) R2 : -0.39172953 3 day(s) Pearson r : 0.08890457 3 day(s) QLIKE : 17.45572407 5 day(s) MAE : 0.77519314 5 day(s) RMSE : 1.46030152 5 day(s) R2 : -0.39235672 5 day(s) Pearson r : 0.03635069 5 day(s) QLIKE : 16.99597746 10 day(s) MAE : 0.78128243 10 day(s) RMSE : 1.47245605 10 day(s) R2 : -0.39184915 10 day(s) Pearson r : 0.00734027 10 day(s) QLIKE : 16.47087276 full horizon MAE : 0.78128243 full horizon RMSE : 1.47245605 full horizon R2 : -0.39184915 full horizon Pearson r : 0.00734027 full horizon QLIKE : 16.47087276 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GE/ITransformer_H10.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=4.60292, max=5.26605 === GE | H=20 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238581 Min value: -3.2166288924657587 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.120224031597433 Min value: -2.6929431961408983 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.008530017154456 Min value: -3.2166288924657587 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.084344850046558 Min value: -2.4033958725509903 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.6741296103710765 Min value: -4.217627703807063 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.687942200751508 Min value: -2.1830758843283755 Epoch 1/50 Train 30.8571 Val 0.9679 0.2s Epoch 2/50 Train 30.6482 Val 0.9676 0.2s Epoch 3/50 Train 30.4557 Val 0.9674 0.3s Epoch 4/50 Train 30.2689 Val 0.9671 0.2s Epoch 5/50 Train 30.0873 Val 0.9669 0.2s Epoch 6/50 Train 29.9102 Val 0.9667 0.2s Epoch 7/50 Train 29.7375 Val 0.9665 0.2s Epoch 8/50 Train 29.5689 Val 0.9663 0.2s Epoch 9/50 Train 29.4044 Val 0.9661 0.2s Epoch 10/50 Train 29.2436 Val 0.9659 0.2s Epoch 11/50 Train 29.0866 Val 0.9658 0.2s Epoch 12/50 Train 28.9332 Val 0.9656 0.2s Epoch 13/50 Train 28.7833 Val 0.9655 0.2s Epoch 14/50 Train 28.6368 Val 0.9653 0.2s Epoch 15/50 Train 28.4937 Val 0.9652 0.3s Epoch 16/50 Train 28.3538 Val 0.9650 0.2s Epoch 17/50 Train 28.2171 Val 0.9649 0.2s Epoch 18/50 Train 28.0835 Val 0.9648 0.2s Epoch 19/50 Train 27.9529 Val 0.9646 0.2s Epoch 20/50 Train 27.8253 Val 0.9645 0.2s Epoch 21/50 Train 27.7007 Val 0.9644 0.2s Epoch 22/50 Train 27.5789 Val 0.9643 0.2s Epoch 23/50 Train 27.4598 Val 0.9642 0.2s Epoch 24/50 Train 27.3436 Val 0.9641 0.2s Epoch 25/50 Train 27.2300 Val 0.9640 0.2s Epoch 26/50 Train 27.1190 Val 0.9639 0.3s Epoch 27/50 Train 27.0106 Val 0.9638 0.3s Epoch 28/50 Train 26.9048 Val 0.9638 0.3s Epoch 29/50 Train 26.8014 Val 0.9637 0.3s Epoch 30/50 Train 26.7005 Val 0.9636 0.3s Epoch 31/50 Train 26.6019 Val 0.9635 0.3s Epoch 32/50 Train 26.5057 Val 0.9635 0.3s Epoch 33/50 Train 26.4118 Val 0.9634 0.3s Epoch 34/50 Train 26.3202 Val 0.9633 0.3s Epoch 35/50 Train 26.2308 Val 0.9632 0.3s Epoch 36/50 Train 26.1435 Val 0.9632 0.2s Epoch 37/50 Train 26.0584 Val 0.9631 0.2s Epoch 38/50 Train 25.9754 Val 0.9631 0.2s Epoch 39/50 Train 25.8944 Val 0.9630 0.2s Epoch 40/50 Train 25.8154 Val 0.9630 0.2s Epoch 41/50 Train 25.7383 Val 0.9629 0.2s Epoch 42/50 Train 25.6632 Val 0.9628 0.2s Epoch 43/50 Train 25.5900 Val 0.9628 0.2s Epoch 44/50 Train 25.5187 Val 0.9627 0.2s Epoch 45/50 Train 25.4491 Val 0.9627 0.2s Epoch 46/50 Train 25.3813 Val 0.9626 0.2s Epoch 47/50 Train 25.3153 Val 0.9626 0.2s Epoch 48/50 Train 25.2510 Val 0.9626 0.2s Epoch 49/50 Train 25.1883 Val 0.9625 0.3s Epoch 50/50 Train 25.1272 Val 0.9625 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 20 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.99859675 1 day(s) RMSE : 27.44431009 1 day(s) R2 : -0.00132217 1 day(s) Pearson r : -0.02946415 1 day(s) QLIKE : 0.68569512 3 day(s) MAE : 4.99358464 3 day(s) RMSE : 27.44337156 3 day(s) R2 : -0.00131131 3 day(s) Pearson r : -0.01603747 3 day(s) QLIKE : 0.68367744 5 day(s) MAE : 4.98721778 5 day(s) RMSE : 27.44302088 5 day(s) R2 : -0.00134306 5 day(s) Pearson r : -0.01782981 5 day(s) QLIKE : 0.68243152 10 day(s) MAE : 4.98842296 10 day(s) RMSE : 27.44605412 10 day(s) R2 : -0.00141746 10 day(s) Pearson r : -0.01033855 10 day(s) QLIKE : 0.67933638 20 day(s) MAE : 4.98557932 20 day(s) RMSE : 27.44791431 20 day(s) R2 : -0.00150021 20 day(s) Pearson r : -0.00640148 20 day(s) QLIKE : 0.67500374 full horizon MAE : 4.98557932 full horizon RMSE : 27.44791431 full horizon R2 : -0.00150021 full horizon Pearson r : -0.00640148 full horizon QLIKE : 0.67500374 --- Task 2 --- 1 day(s) MAE : 0.77053142 1 day(s) RMSE : 1.45653612 1 day(s) R2 : -0.38861000 1 day(s) Pearson r : -0.04127292 1 day(s) QLIKE : 18.69435853 3 day(s) MAE : 0.77441637 3 day(s) RMSE : 1.45967253 3 day(s) R2 : -0.39172681 3 day(s) Pearson r : 0.11181015 3 day(s) QLIKE : 17.55257058 5 day(s) MAE : 0.77519196 5 day(s) RMSE : 1.46029984 5 day(s) R2 : -0.39235351 5 day(s) Pearson r : 0.02162081 5 day(s) QLIKE : 17.15144940 10 day(s) MAE : 0.78128147 10 day(s) RMSE : 1.47245447 10 day(s) R2 : -0.39184616 10 day(s) Pearson r : 0.00711622 10 day(s) QLIKE : 16.62448222 20 day(s) MAE : 0.79014844 20 day(s) RMSE : 1.48378844 20 day(s) R2 : -0.39581391 20 day(s) Pearson r : 0.00971681 20 day(s) QLIKE : 16.25652444 full horizon MAE : 0.79014844 full horizon RMSE : 1.48378844 full horizon R2 : -0.39581391 full horizon Pearson r : 0.00971681 full horizon QLIKE : 16.25652444 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GE/ITransformer_H20.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=4.59903, max=5.37702 === BAC | H=1 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179623 Min value: -9.003598937548427 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.107742786547419 Min value: -3.152671373228804 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166819 Min value: -2.988682485704703 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.2542888447212457 Min value: -1.2924015525366714 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787336 Min value: -5.46095162532849 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.207108669727662 Min value: -1.9461535420618155 Epoch 1/50 Train 24.2424 Val 0.8604 0.2s Epoch 2/50 Train 23.6341 Val 0.8520 0.2s Epoch 3/50 Train 23.1402 Val 0.8444 0.2s Epoch 4/50 Train 22.7081 Val 0.8377 0.2s Epoch 5/50 Train 22.3286 Val 0.8316 0.3s Epoch 6/50 Train 21.9937 Val 0.8261 0.2s Epoch 7/50 Train 21.6967 Val 0.8212 0.2s Epoch 8/50 Train 21.4318 Val 0.8167 0.2s Epoch 9/50 Train 21.1946 Val 0.8126 0.2s Epoch 10/50 Train 20.9809 Val 0.8088 0.2s Epoch 11/50 Train 20.7877 Val 0.8054 0.2s Epoch 12/50 Train 20.6122 Val 0.8023 0.2s Epoch 13/50 Train 20.4521 Val 0.7994 0.2s Epoch 14/50 Train 20.3055 Val 0.7968 0.2s Epoch 15/50 Train 20.1707 Val 0.7944 0.2s Epoch 16/50 Train 20.0463 Val 0.7922 0.2s Epoch 17/50 Train 19.9311 Val 0.7901 0.2s Epoch 18/50 Train 19.8241 Val 0.7883 0.2s Epoch 19/50 Train 19.7244 Val 0.7866 0.2s Epoch 20/50 Train 19.6311 Val 0.7850 0.2s Epoch 21/50 Train 19.5437 Val 0.7836 0.3s Epoch 22/50 Train 19.4615 Val 0.7823 0.2s Epoch 23/50 Train 19.3840 Val 0.7811 0.2s Epoch 24/50 Train 19.3107 Val 0.7800 0.2s Epoch 25/50 Train 19.2413 Val 0.7790 0.2s Epoch 26/50 Train 19.1755 Val 0.7781 0.2s Epoch 27/50 Train 19.1128 Val 0.7773 0.2s Epoch 28/50 Train 19.0530 Val 0.7765 0.2s Epoch 29/50 Train 18.9959 Val 0.7759 0.2s Epoch 30/50 Train 18.9412 Val 0.7752 0.2s Epoch 31/50 Train 18.8888 Val 0.7747 0.2s Epoch 32/50 Train 18.8385 Val 0.7742 0.2s Epoch 33/50 Train 18.7900 Val 0.7737 0.2s Epoch 34/50 Train 18.7434 Val 0.7733 0.2s Epoch 35/50 Train 18.6983 Val 0.7729 0.2s Epoch 36/50 Train 18.6548 Val 0.7726 0.2s Epoch 37/50 Train 18.6127 Val 0.7723 0.2s Epoch 38/50 Train 18.5719 Val 0.7720 0.2s Epoch 39/50 Train 18.5323 Val 0.7718 0.2s Epoch 40/50 Train 18.4938 Val 0.7716 0.2s Epoch 41/50 Train 18.4563 Val 0.7714 0.2s Epoch 42/50 Train 18.4199 Val 0.7712 0.2s Epoch 43/50 Train 18.3843 Val 0.7711 0.2s Epoch 44/50 Train 18.3496 Val 0.7710 0.2s Epoch 45/50 Train 18.3157 Val 0.7709 0.2s Epoch 46/50 Train 18.2825 Val 0.7708 0.2s Epoch 47/50 Train 18.2500 Val 0.7707 0.3s Epoch 48/50 Train 18.2181 Val 0.7707 0.4s Epoch 49/50 Train 18.1868 Val 0.7707 0.3s Epoch 50/50 Train 18.1561 Val 0.7706 0.3s Parameters used in the single-fit model: input_len: 60 output_len: 1 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.13593101 1 day(s) RMSE : 7.60659705 1 day(s) R2 : 0.03980931 1 day(s) Pearson r : 0.33805327 1 day(s) QLIKE : 0.34770757 full horizon MAE : 2.13593101 full horizon RMSE : 7.60659705 full horizon R2 : 0.03980931 full horizon Pearson r : 0.33805327 full horizon QLIKE : 0.34770757 --- Task 2 --- 1 day(s) MAE : 0.60429187 1 day(s) RMSE : 1.20424477 1 day(s) R2 : -0.33640829 1 day(s) Pearson r : -0.04884508 1 day(s) QLIKE : 20.81920533 full horizon MAE : 0.60429187 full horizon RMSE : 1.20424477 full horizon R2 : -0.33640829 full horizon Pearson r : -0.04884508 full horizon QLIKE : 20.81920533 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BAC/ITransformer_H1.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=1.96761, max=11.2972 === BAC | H=5 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179623 Min value: -9.003598937548427 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.106232115580254 Min value: -3.1505690923369047 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166819 Min value: -2.988682485704703 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.253588840112363 Min value: -1.2911129191419828 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787336 Min value: -5.46095162532849 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.205991919409481 Min value: -1.9445789696790527 Epoch 1/50 Train 26.9898 Val 0.8816 0.3s Epoch 2/50 Train 26.5718 Val 0.8761 0.3s Epoch 3/50 Train 26.2018 Val 0.8709 0.3s Epoch 4/50 Train 25.8539 Val 0.8661 0.3s Epoch 5/50 Train 25.5271 Val 0.8615 0.3s Epoch 6/50 Train 25.2200 Val 0.8571 0.3s Epoch 7/50 Train 24.9313 Val 0.8530 0.2s Epoch 8/50 Train 24.6597 Val 0.8491 0.2s Epoch 9/50 Train 24.4038 Val 0.8454 0.2s Epoch 10/50 Train 24.1626 Val 0.8419 0.2s Epoch 11/50 Train 23.9350 Val 0.8385 0.2s Epoch 12/50 Train 23.7200 Val 0.8353 0.2s Epoch 13/50 Train 23.5169 Val 0.8323 0.2s Epoch 14/50 Train 23.3247 Val 0.8294 0.2s Epoch 15/50 Train 23.1429 Val 0.8267 0.2s Epoch 16/50 Train 22.9706 Val 0.8241 0.2s Epoch 17/50 Train 22.8074 Val 0.8216 0.2s Epoch 18/50 Train 22.6527 Val 0.8192 0.2s Epoch 19/50 Train 22.5059 Val 0.8170 0.2s Epoch 20/50 Train 22.3665 Val 0.8148 0.2s Epoch 21/50 Train 22.2341 Val 0.8128 0.2s Epoch 22/50 Train 22.1083 Val 0.8108 0.2s Epoch 23/50 Train 21.9886 Val 0.8089 0.2s Epoch 24/50 Train 21.8747 Val 0.8071 0.2s Epoch 25/50 Train 21.7663 Val 0.8054 0.2s Epoch 26/50 Train 21.6631 Val 0.8038 0.2s Epoch 27/50 Train 21.5646 Val 0.8023 0.2s Epoch 28/50 Train 21.4707 Val 0.8008 0.2s Epoch 29/50 Train 21.3811 Val 0.7994 0.2s Epoch 30/50 Train 21.2954 Val 0.7980 0.2s Epoch 31/50 Train 21.2136 Val 0.7967 0.2s Epoch 32/50 Train 21.1353 Val 0.7955 0.2s Epoch 33/50 Train 21.0604 Val 0.7943 0.3s Epoch 34/50 Train 20.9886 Val 0.7931 0.2s Epoch 35/50 Train 20.9198 Val 0.7921 0.2s Epoch 36/50 Train 20.8539 Val 0.7910 0.2s Epoch 37/50 Train 20.7905 Val 0.7900 0.2s Epoch 38/50 Train 20.7297 Val 0.7891 0.2s Epoch 39/50 Train 20.6712 Val 0.7882 0.2s Epoch 40/50 Train 20.6149 Val 0.7873 0.2s Epoch 41/50 Train 20.5607 Val 0.7864 0.2s Epoch 42/50 Train 20.5085 Val 0.7856 0.2s Epoch 43/50 Train 20.4581 Val 0.7848 0.2s Epoch 44/50 Train 20.4095 Val 0.7841 0.2s Epoch 45/50 Train 20.3626 Val 0.7834 0.3s Epoch 46/50 Train 20.3173 Val 0.7827 0.3s Epoch 47/50 Train 20.2734 Val 0.7820 0.3s Epoch 48/50 Train 20.2310 Val 0.7814 0.3s Epoch 49/50 Train 20.1899 Val 0.7808 0.3s Epoch 50/50 Train 20.1500 Val 0.7802 0.3s Parameters used in the single-fit model: input_len: 60 output_len: 5 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.18069961 1 day(s) RMSE : 7.69822169 1 day(s) R2 : 0.01653820 1 day(s) Pearson r : 0.28169557 1 day(s) QLIKE : 0.36807469 3 day(s) MAE : 2.20292642 3 day(s) RMSE : 7.75488436 3 day(s) R2 : 0.00264463 3 day(s) Pearson r : 0.17817359 3 day(s) QLIKE : 0.38956409 5 day(s) MAE : 2.22102442 5 day(s) RMSE : 7.78239280 5 day(s) R2 : -0.00413330 5 day(s) Pearson r : 0.13265113 5 day(s) QLIKE : 0.40056290 full horizon MAE : 2.22102442 full horizon RMSE : 7.78239280 full horizon R2 : -0.00413330 full horizon Pearson r : 0.13265113 full horizon QLIKE : 0.40056290 --- Task 2 --- 1 day(s) MAE : 0.60422084 1 day(s) RMSE : 1.20423731 1 day(s) R2 : -0.33639171 1 day(s) Pearson r : -0.04287388 1 day(s) QLIKE : 20.27797712 3 day(s) MAE : 0.60850169 3 day(s) RMSE : 1.21034222 3 day(s) R2 : -0.33820675 3 day(s) Pearson r : -0.00115862 3 day(s) QLIKE : 19.73329689 5 day(s) MAE : 0.61130957 5 day(s) RMSE : 1.21416267 5 day(s) R2 : -0.33950863 5 day(s) Pearson r : -0.00306330 5 day(s) QLIKE : 19.37207439 full horizon MAE : 0.61130957 full horizon RMSE : 1.21416267 full horizon R2 : -0.33950863 full horizon Pearson r : -0.00306330 full horizon QLIKE : 19.37207439 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BAC/ITransformer_H5.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=1.8675, max=11.4547 === BAC | H=10 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179623 Min value: -9.003598937548427 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.103196331248472 Min value: -3.1492038976362813 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166819 Min value: -2.988682485704703 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.2515405379791398 Min value: -1.2907388380002114 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787336 Min value: -5.46095162532849 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.203435974440148 Min value: -1.943856582902401 Epoch 1/50 Train 26.8887 Val 0.8859 0.3s Epoch 2/50 Train 26.5828 Val 0.8813 0.3s Epoch 3/50 Train 26.3085 Val 0.8770 0.3s Epoch 4/50 Train 26.0472 Val 0.8728 0.2s Epoch 5/50 Train 25.7983 Val 0.8689 0.2s Epoch 6/50 Train 25.5616 Val 0.8651 0.2s Epoch 7/50 Train 25.3363 Val 0.8615 0.3s Epoch 8/50 Train 25.1219 Val 0.8580 0.2s Epoch 9/50 Train 24.9177 Val 0.8547 0.2s Epoch 10/50 Train 24.7232 Val 0.8516 0.2s Epoch 11/50 Train 24.5377 Val 0.8485 0.2s Epoch 12/50 Train 24.3609 Val 0.8456 0.2s Epoch 13/50 Train 24.1921 Val 0.8428 0.2s Epoch 14/50 Train 24.0309 Val 0.8401 0.2s Epoch 15/50 Train 23.8769 Val 0.8375 0.2s Epoch 16/50 Train 23.7297 Val 0.8351 0.2s Epoch 17/50 Train 23.5889 Val 0.8327 0.2s Epoch 18/50 Train 23.4543 Val 0.8304 0.2s Epoch 19/50 Train 23.3253 Val 0.8281 0.2s Epoch 20/50 Train 23.2019 Val 0.8260 0.2s Epoch 21/50 Train 23.0836 Val 0.8239 0.2s Epoch 22/50 Train 22.9702 Val 0.8219 0.3s Epoch 23/50 Train 22.8614 Val 0.8200 0.2s Epoch 24/50 Train 22.7571 Val 0.8182 0.2s Epoch 25/50 Train 22.6570 Val 0.8164 0.3s Epoch 26/50 Train 22.5609 Val 0.8147 0.3s Epoch 27/50 Train 22.4686 Val 0.8130 0.3s Epoch 28/50 Train 22.3800 Val 0.8114 0.3s Epoch 29/50 Train 22.2947 Val 0.8099 0.3s Epoch 30/50 Train 22.2128 Val 0.8084 0.3s Epoch 31/50 Train 22.1340 Val 0.8069 0.3s Epoch 32/50 Train 22.0581 Val 0.8055 0.3s Epoch 33/50 Train 21.9851 Val 0.8041 0.3s Epoch 34/50 Train 21.9148 Val 0.8028 0.4s Epoch 35/50 Train 21.8471 Val 0.8016 0.3s Epoch 36/50 Train 21.7819 Val 0.8003 0.2s Epoch 37/50 Train 21.7189 Val 0.7992 0.2s Epoch 38/50 Train 21.6583 Val 0.7980 0.2s Epoch 39/50 Train 21.5997 Val 0.7969 0.2s Epoch 40/50 Train 21.5432 Val 0.7958 0.2s Epoch 41/50 Train 21.4887 Val 0.7948 0.2s Epoch 42/50 Train 21.4360 Val 0.7938 0.2s Epoch 43/50 Train 21.3851 Val 0.7928 0.2s Epoch 44/50 Train 21.3358 Val 0.7918 0.2s Epoch 45/50 Train 21.2882 Val 0.7909 0.2s Epoch 46/50 Train 21.2421 Val 0.7900 0.2s Epoch 47/50 Train 21.1975 Val 0.7891 0.2s Epoch 48/50 Train 21.1544 Val 0.7883 0.2s Epoch 49/50 Train 21.1125 Val 0.7875 0.2s Epoch 50/50 Train 21.0720 Val 0.7867 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 10 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.18332134 1 day(s) RMSE : 7.70133918 1 day(s) R2 : 0.01574150 1 day(s) Pearson r : 0.27106080 1 day(s) QLIKE : 0.36964120 3 day(s) MAE : 2.20899261 3 day(s) RMSE : 7.76314754 3 day(s) R2 : 0.00051805 3 day(s) Pearson r : 0.16444171 3 day(s) QLIKE : 0.39299769 5 day(s) MAE : 2.22811265 5 day(s) RMSE : 7.78889575 5 day(s) R2 : -0.00581211 5 day(s) Pearson r : 0.12249496 5 day(s) QLIKE : 0.40344094 10 day(s) MAE : 2.24726349 10 day(s) RMSE : 7.81312319 10 day(s) R2 : -0.01198299 10 day(s) Pearson r : 0.07943902 10 day(s) QLIKE : 0.41222476 full horizon MAE : 2.24726349 full horizon RMSE : 7.81312319 full horizon R2 : -0.01198299 full horizon Pearson r : 0.07943902 full horizon QLIKE : 0.41222476 --- Task 2 --- 1 day(s) MAE : 0.60416619 1 day(s) RMSE : 1.20421587 1 day(s) R2 : -0.33634413 1 day(s) Pearson r : -0.02856167 1 day(s) QLIKE : 19.11951693 3 day(s) MAE : 0.60869627 3 day(s) RMSE : 1.21043915 3 day(s) R2 : -0.33842109 3 day(s) Pearson r : 0.00104090 3 day(s) QLIKE : 18.77999789 5 day(s) MAE : 0.61142407 5 day(s) RMSE : 1.21421389 5 day(s) R2 : -0.33962165 5 day(s) Pearson r : 0.00057361 5 day(s) QLIKE : 18.62460078 10 day(s) MAE : 0.61635269 10 day(s) RMSE : 1.22240924 10 day(s) R2 : -0.34024328 10 day(s) Pearson r : 0.04033533 10 day(s) QLIKE : 19.00559009 full horizon MAE : 0.61635269 full horizon RMSE : 1.22240924 full horizon R2 : -0.34024328 full horizon Pearson r : 0.04033533 full horizon QLIKE : 19.00559009 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BAC/ITransformer_H10.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=1.66268, max=11.0126 === BAC | H=20 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179623 Min value: -9.003598937548427 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.103268362353512 Min value: -3.1487890186062453 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166819 Min value: -2.988682485704703 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.2516894965633285 Min value: -1.2904011693419095 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787336 Min value: -5.46095162532849 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.203545386302637 Min value: -1.9434917803124665 Epoch 1/50 Train 27.6664 Val 0.8980 0.2s Epoch 2/50 Train 27.4160 Val 0.8941 0.2s Epoch 3/50 Train 27.1897 Val 0.8903 0.2s Epoch 4/50 Train 26.9716 Val 0.8867 0.2s Epoch 5/50 Train 26.7616 Val 0.8832 0.3s Epoch 6/50 Train 26.5595 Val 0.8798 0.2s Epoch 7/50 Train 26.3651 Val 0.8765 0.3s Epoch 8/50 Train 26.1781 Val 0.8734 0.2s Epoch 9/50 Train 25.9982 Val 0.8703 0.2s Epoch 10/50 Train 25.8250 Val 0.8674 0.2s Epoch 11/50 Train 25.6583 Val 0.8645 0.2s Epoch 12/50 Train 25.4977 Val 0.8618 0.2s Epoch 13/50 Train 25.3430 Val 0.8591 0.2s Epoch 14/50 Train 25.1939 Val 0.8565 0.3s Epoch 15/50 Train 25.0501 Val 0.8540 0.3s Epoch 16/50 Train 24.9114 Val 0.8515 0.2s Epoch 17/50 Train 24.7775 Val 0.8492 0.3s Epoch 18/50 Train 24.6483 Val 0.8469 0.2s Epoch 19/50 Train 24.5234 Val 0.8446 0.2s Epoch 20/50 Train 24.4028 Val 0.8424 0.2s Epoch 21/50 Train 24.2863 Val 0.8403 0.2s Epoch 22/50 Train 24.1736 Val 0.8382 0.2s Epoch 23/50 Train 24.0645 Val 0.8362 0.2s Epoch 24/50 Train 23.9590 Val 0.8343 0.2s Epoch 25/50 Train 23.8569 Val 0.8323 0.2s Epoch 26/50 Train 23.7580 Val 0.8305 0.3s Epoch 27/50 Train 23.6623 Val 0.8286 0.3s Epoch 28/50 Train 23.5695 Val 0.8269 0.3s Epoch 29/50 Train 23.4796 Val 0.8251 0.3s Epoch 30/50 Train 23.3925 Val 0.8234 0.3s Epoch 31/50 Train 23.3080 Val 0.8218 0.3s Epoch 32/50 Train 23.2261 Val 0.8202 0.3s Epoch 33/50 Train 23.1467 Val 0.8186 0.3s Epoch 34/50 Train 23.0697 Val 0.8170 0.3s Epoch 35/50 Train 22.9949 Val 0.8155 0.2s Epoch 36/50 Train 22.9224 Val 0.8140 0.2s Epoch 37/50 Train 22.8520 Val 0.8126 0.2s Epoch 38/50 Train 22.7837 Val 0.8112 0.2s Epoch 39/50 Train 22.7174 Val 0.8098 0.2s Epoch 40/50 Train 22.6531 Val 0.8084 0.2s Epoch 41/50 Train 22.5907 Val 0.8071 0.2s Epoch 42/50 Train 22.5301 Val 0.8058 0.2s Epoch 43/50 Train 22.4712 Val 0.8045 0.2s Epoch 44/50 Train 22.4141 Val 0.8033 0.2s Epoch 45/50 Train 22.3586 Val 0.8021 0.2s Epoch 46/50 Train 22.3048 Val 0.8009 0.2s Epoch 47/50 Train 22.2526 Val 0.7997 0.2s Epoch 48/50 Train 22.2018 Val 0.7986 0.2s Epoch 49/50 Train 22.1526 Val 0.7975 0.2s Epoch 50/50 Train 22.1048 Val 0.7964 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 20 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.19746740 1 day(s) RMSE : 7.72516176 1 day(s) R2 : 0.00964286 1 day(s) Pearson r : 0.24950813 1 day(s) QLIKE : 0.37702621 3 day(s) MAE : 2.21872164 3 day(s) RMSE : 7.77802303 3 day(s) R2 : -0.00331598 3 day(s) Pearson r : 0.13629815 3 day(s) QLIKE : 0.39825874 5 day(s) MAE : 2.23701756 5 day(s) RMSE : 7.79821120 5 day(s) R2 : -0.00821943 5 day(s) Pearson r : 0.10292923 5 day(s) QLIKE : 0.40710205 10 day(s) MAE : 2.25251391 10 day(s) RMSE : 7.81726990 10 day(s) R2 : -0.01305747 10 day(s) Pearson r : 0.07053669 10 day(s) QLIKE : 0.41346666 20 day(s) MAE : 2.26522910 20 day(s) RMSE : 7.83152164 20 day(s) R2 : -0.01708960 20 day(s) Pearson r : 0.03913770 20 day(s) QLIKE : 0.41691088 full horizon MAE : 2.26522910 full horizon RMSE : 7.83152164 full horizon R2 : -0.01708960 full horizon Pearson r : 0.03913770 full horizon QLIKE : 0.41691088 --- Task 2 --- 1 day(s) MAE : 0.60388945 1 day(s) RMSE : 1.20416115 1 day(s) R2 : -0.33622268 1 day(s) Pearson r : -0.02086665 1 day(s) QLIKE : 18.32239190 3 day(s) MAE : 0.60995004 3 day(s) RMSE : 1.21217757 3 day(s) R2 : -0.34226831 3 day(s) Pearson r : 0.00083840 3 day(s) QLIKE : 18.23971774 5 day(s) MAE : 0.61216193 5 day(s) RMSE : 1.21523558 5 day(s) R2 : -0.34187704 5 day(s) Pearson r : 0.00068338 5 day(s) QLIKE : 18.28225203 10 day(s) MAE : 0.61892908 10 day(s) RMSE : 1.23502464 10 day(s) R2 : -0.36804895 10 day(s) Pearson r : 0.04042312 10 day(s) QLIKE : 18.94147984 20 day(s) MAE : 0.62482475 20 day(s) RMSE : 1.23685961 20 day(s) R2 : -0.35874658 20 day(s) Pearson r : 0.02829382 20 day(s) QLIKE : 21.18845942 full horizon MAE : 0.62482475 full horizon RMSE : 1.23685961 full horizon R2 : -0.35874658 full horizon Pearson r : 0.02829382 full horizon QLIKE : 21.18845942 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BAC/ITransformer_H20.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=1.4288, max=12.1328 === C | H=1 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.80914068636715 Min value: -9.273594127665117 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.116803264609635 Min value: -2.5790852911299216 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.833292676987295 Min value: -3.3424547859577265 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.6474166417153167 Min value: -1.6630480776200625 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.61558566904287 Min value: -5.593651881065264 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.113246907180985 Min value: -2.0589048270008967 Epoch 1/50 Train 23.6838 Val 0.7958 0.3s Epoch 2/50 Train 23.1299 Val 0.7895 0.2s Epoch 3/50 Train 22.6711 Val 0.7839 0.2s Epoch 4/50 Train 22.2673 Val 0.7788 0.2s Epoch 5/50 Train 21.9097 Val 0.7743 0.2s Epoch 6/50 Train 21.5910 Val 0.7702 0.2s Epoch 7/50 Train 21.3057 Val 0.7664 0.2s Epoch 8/50 Train 21.0491 Val 0.7630 0.2s Epoch 9/50 Train 20.8173 Val 0.7599 0.2s Epoch 10/50 Train 20.6071 Val 0.7570 0.2s Epoch 11/50 Train 20.4158 Val 0.7544 0.2s Epoch 12/50 Train 20.2412 Val 0.7520 0.2s Epoch 13/50 Train 20.0812 Val 0.7498 0.2s Epoch 14/50 Train 19.9342 Val 0.7478 0.2s Epoch 15/50 Train 19.7987 Val 0.7459 0.3s Epoch 16/50 Train 19.6736 Val 0.7442 0.2s Epoch 17/50 Train 19.5576 Val 0.7426 0.2s Epoch 18/50 Train 19.4500 Val 0.7411 0.2s Epoch 19/50 Train 19.3497 Val 0.7398 0.2s Epoch 20/50 Train 19.2561 Val 0.7385 0.2s Epoch 21/50 Train 19.1686 Val 0.7374 0.2s Epoch 22/50 Train 19.0865 Val 0.7363 0.2s Epoch 23/50 Train 19.0094 Val 0.7353 0.2s Epoch 24/50 Train 18.9368 Val 0.7344 0.2s Epoch 25/50 Train 18.8683 Val 0.7335 0.2s Epoch 26/50 Train 18.8036 Val 0.7327 0.2s Epoch 27/50 Train 18.7422 Val 0.7320 0.2s Epoch 28/50 Train 18.6840 Val 0.7313 0.2s Epoch 29/50 Train 18.6286 Val 0.7307 0.2s Epoch 30/50 Train 18.5759 Val 0.7301 0.2s Epoch 31/50 Train 18.5256 Val 0.7295 0.2s Epoch 32/50 Train 18.4775 Val 0.7290 0.2s Epoch 33/50 Train 18.4314 Val 0.7286 0.2s Epoch 34/50 Train 18.3873 Val 0.7281 0.2s Epoch 35/50 Train 18.3449 Val 0.7277 0.2s Epoch 36/50 Train 18.3040 Val 0.7274 0.2s Epoch 37/50 Train 18.2647 Val 0.7270 0.2s Epoch 38/50 Train 18.2268 Val 0.7267 0.2s Epoch 39/50 Train 18.1901 Val 0.7264 0.2s Epoch 40/50 Train 18.1546 Val 0.7261 0.2s Epoch 41/50 Train 18.1203 Val 0.7259 0.2s Epoch 42/50 Train 18.0869 Val 0.7256 0.2s Epoch 43/50 Train 18.0545 Val 0.7254 0.2s Epoch 44/50 Train 18.0229 Val 0.7252 0.2s Epoch 45/50 Train 17.9922 Val 0.7251 0.2s Epoch 46/50 Train 17.9622 Val 0.7249 0.2s Epoch 47/50 Train 17.9329 Val 0.7247 0.2s Epoch 48/50 Train 17.9043 Val 0.7246 0.2s Epoch 49/50 Train 17.8763 Val 0.7245 0.2s Epoch 50/50 Train 17.8488 Val 0.7244 0.4s Parameters used in the single-fit model: input_len: 60 output_len: 1 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.64588919 1 day(s) RMSE : 9.25764049 1 day(s) R2 : 0.03807696 1 day(s) Pearson r : 0.37444665 1 day(s) QLIKE : 0.34633521 full horizon MAE : 2.64588919 full horizon RMSE : 9.25764049 full horizon R2 : 0.03807696 full horizon Pearson r : 0.37444665 full horizon QLIKE : 0.34633521 --- Task 2 --- 1 day(s) MAE : 0.70233428 1 day(s) RMSE : 1.31995792 1 day(s) R2 : -0.39480781 1 day(s) Pearson r : -0.03998046 1 day(s) QLIKE : 18.45444374 full horizon MAE : 0.70233428 full horizon RMSE : 1.31995792 full horizon R2 : -0.39480781 full horizon Pearson r : -0.03998046 full horizon QLIKE : 18.45444374 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/C/ITransformer_H1.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=2.13532, max=14.9598 === C | H=5 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.80914068636715 Min value: -9.273594127665117 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.113808938949697 Min value: -2.5779301635708336 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.833292676987295 Min value: -3.3424547859577265 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.645753754809614 Min value: -1.6623868571045624 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.61558566904287 Min value: -5.593651881065264 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.110793676955011 Min value: -2.0580301692648475 Epoch 1/50 Train 26.2645 Val 0.8166 0.3s Epoch 2/50 Train 25.8747 Val 0.8125 0.3s Epoch 3/50 Train 25.5260 Val 0.8087 0.3s Epoch 4/50 Train 25.1983 Val 0.8051 0.3s Epoch 5/50 Train 24.8906 Val 0.8017 0.3s Epoch 6/50 Train 24.6014 Val 0.7986 0.3s Epoch 7/50 Train 24.3294 Val 0.7955 0.3s Epoch 8/50 Train 24.0733 Val 0.7927 0.4s Epoch 9/50 Train 23.8317 Val 0.7900 0.2s Epoch 10/50 Train 23.6036 Val 0.7874 0.2s Epoch 11/50 Train 23.3880 Val 0.7850 0.2s Epoch 12/50 Train 23.1840 Val 0.7827 0.2s Epoch 13/50 Train 22.9907 Val 0.7805 0.2s Epoch 14/50 Train 22.8075 Val 0.7784 0.2s Epoch 15/50 Train 22.6337 Val 0.7764 0.2s Epoch 16/50 Train 22.4688 Val 0.7745 0.2s Epoch 17/50 Train 22.3121 Val 0.7726 0.2s Epoch 18/50 Train 22.1631 Val 0.7709 0.2s Epoch 19/50 Train 22.0214 Val 0.7692 0.2s Epoch 20/50 Train 21.8866 Val 0.7677 0.2s Epoch 21/50 Train 21.7582 Val 0.7661 0.2s Epoch 22/50 Train 21.6360 Val 0.7647 0.3s Epoch 23/50 Train 21.5194 Val 0.7633 0.2s Epoch 24/50 Train 21.4083 Val 0.7620 0.2s Epoch 25/50 Train 21.3023 Val 0.7608 0.2s Epoch 26/50 Train 21.2011 Val 0.7596 0.2s Epoch 27/50 Train 21.1045 Val 0.7584 0.3s Epoch 28/50 Train 21.0122 Val 0.7573 0.3s Epoch 29/50 Train 20.9239 Val 0.7563 0.3s Epoch 30/50 Train 20.8395 Val 0.7553 0.2s Epoch 31/50 Train 20.7588 Val 0.7543 0.2s Epoch 32/50 Train 20.6815 Val 0.7534 0.3s Epoch 33/50 Train 20.6074 Val 0.7525 0.3s Epoch 34/50 Train 20.5364 Val 0.7517 0.3s Epoch 35/50 Train 20.4683 Val 0.7508 0.3s Epoch 36/50 Train 20.4030 Val 0.7501 0.2s Epoch 37/50 Train 20.3403 Val 0.7493 0.2s Epoch 38/50 Train 20.2801 Val 0.7486 0.2s Epoch 39/50 Train 20.2222 Val 0.7479 0.2s Epoch 40/50 Train 20.1665 Val 0.7473 0.2s Epoch 41/50 Train 20.1129 Val 0.7467 0.2s Epoch 42/50 Train 20.0613 Val 0.7461 0.2s Epoch 43/50 Train 20.0116 Val 0.7455 0.2s Epoch 44/50 Train 19.9637 Val 0.7449 0.2s Epoch 45/50 Train 19.9175 Val 0.7444 0.2s Epoch 46/50 Train 19.8729 Val 0.7439 0.2s Epoch 47/50 Train 19.8299 Val 0.7434 0.2s Epoch 48/50 Train 19.7883 Val 0.7429 0.2s Epoch 49/50 Train 19.7480 Val 0.7425 0.2s Epoch 50/50 Train 19.7091 Val 0.7421 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 5 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.68962434 1 day(s) RMSE : 9.37915454 1 day(s) R2 : 0.01265918 1 day(s) Pearson r : 0.32807740 1 day(s) QLIKE : 0.36368343 3 day(s) MAE : 2.75327557 3 day(s) RMSE : 9.47084849 3 day(s) R2 : -0.00183127 3 day(s) Pearson r : 0.25618223 3 day(s) QLIKE : 0.38494366 5 day(s) MAE : 2.79075278 5 day(s) RMSE : 9.52713456 5 day(s) R2 : -0.01054736 5 day(s) Pearson r : 0.21004264 5 day(s) QLIKE : 0.39653872 full horizon MAE : 2.79075278 full horizon RMSE : 9.52713456 full horizon R2 : -0.01054736 full horizon Pearson r : 0.21004264 full horizon QLIKE : 0.39653872 --- Task 2 --- 1 day(s) MAE : 0.70232243 1 day(s) RMSE : 1.31993940 1 day(s) R2 : -0.39476866 1 day(s) Pearson r : -0.02457740 1 day(s) QLIKE : 18.82805847 3 day(s) MAE : 0.70673424 3 day(s) RMSE : 1.32455486 3 day(s) R2 : -0.39784783 3 day(s) Pearson r : -0.01318146 3 day(s) QLIKE : 20.49993037 5 day(s) MAE : 0.70936780 5 day(s) RMSE : 1.32667914 5 day(s) R2 : -0.40023425 5 day(s) Pearson r : -0.01194272 5 day(s) QLIKE : 20.00497640 full horizon MAE : 0.70936780 full horizon RMSE : 1.32667914 full horizon R2 : -0.40023425 full horizon Pearson r : -0.01194272 full horizon QLIKE : 20.00497640 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/C/ITransformer_H5.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=2.07223, max=13.9202 === C | H=10 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.80914068636715 Min value: -9.273594127665117 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.111052537752672 Min value: -2.5777824852265807 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.833292676987295 Min value: -3.3424547859577265 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.6439291882044116 Min value: -1.6625848496872644 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.61558566904287 Min value: -5.593651881065264 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.108415972443237 Min value: -2.058078783451692 Epoch 1/50 Train 26.2970 Val 0.8227 0.2s Epoch 2/50 Train 26.0119 Val 0.8191 0.2s Epoch 3/50 Train 25.7533 Val 0.8158 0.2s Epoch 4/50 Train 25.5069 Val 0.8125 0.2s Epoch 5/50 Train 25.2723 Val 0.8095 0.2s Epoch 6/50 Train 25.0490 Val 0.8066 0.2s Epoch 7/50 Train 24.8365 Val 0.8038 0.2s Epoch 8/50 Train 24.6341 Val 0.8011 0.2s Epoch 9/50 Train 24.4411 Val 0.7986 0.2s Epoch 10/50 Train 24.2569 Val 0.7961 0.2s Epoch 11/50 Train 24.0811 Val 0.7938 0.2s Epoch 12/50 Train 23.9132 Val 0.7915 0.2s Epoch 13/50 Train 23.7526 Val 0.7894 0.3s Epoch 14/50 Train 23.5989 Val 0.7873 0.2s Epoch 15/50 Train 23.4517 Val 0.7853 0.2s Epoch 16/50 Train 23.3107 Val 0.7834 0.2s Epoch 17/50 Train 23.1756 Val 0.7815 0.2s Epoch 18/50 Train 23.0459 Val 0.7797 0.2s Epoch 19/50 Train 22.9215 Val 0.7780 0.2s Epoch 20/50 Train 22.8020 Val 0.7763 0.2s Epoch 21/50 Train 22.6873 Val 0.7747 0.2s Epoch 22/50 Train 22.5770 Val 0.7732 0.2s Epoch 23/50 Train 22.4709 Val 0.7717 0.3s Epoch 24/50 Train 22.3690 Val 0.7703 0.2s Epoch 25/50 Train 22.2708 Val 0.7689 0.2s Epoch 26/50 Train 22.1764 Val 0.7675 0.2s Epoch 27/50 Train 22.0854 Val 0.7662 0.2s Epoch 28/50 Train 21.9978 Val 0.7649 0.3s Epoch 29/50 Train 21.9134 Val 0.7637 0.3s Epoch 30/50 Train 21.8321 Val 0.7626 0.3s Epoch 31/50 Train 21.7536 Val 0.7614 0.2s Epoch 32/50 Train 21.6780 Val 0.7603 0.3s Epoch 33/50 Train 21.6050 Val 0.7592 0.3s Epoch 34/50 Train 21.5346 Val 0.7582 0.3s Epoch 35/50 Train 21.4667 Val 0.7572 0.3s Epoch 36/50 Train 21.4010 Val 0.7562 0.3s Epoch 37/50 Train 21.3377 Val 0.7553 0.3s Epoch 38/50 Train 21.2764 Val 0.7544 0.2s Epoch 39/50 Train 21.2173 Val 0.7535 0.2s Epoch 40/50 Train 21.1601 Val 0.7527 0.2s Epoch 41/50 Train 21.1047 Val 0.7518 0.2s Epoch 42/50 Train 21.0512 Val 0.7510 0.2s Epoch 43/50 Train 20.9995 Val 0.7503 0.2s Epoch 44/50 Train 20.9494 Val 0.7495 0.2s Epoch 45/50 Train 20.9008 Val 0.7488 0.2s Epoch 46/50 Train 20.8539 Val 0.7481 0.2s Epoch 47/50 Train 20.8083 Val 0.7474 0.2s Epoch 48/50 Train 20.7642 Val 0.7467 0.2s Epoch 49/50 Train 20.7215 Val 0.7461 0.2s Epoch 50/50 Train 20.6800 Val 0.7454 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 10 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.69037872 1 day(s) RMSE : 9.38564782 1 day(s) R2 : 0.01129162 1 day(s) Pearson r : 0.32184399 1 day(s) QLIKE : 0.36478074 3 day(s) MAE : 2.76187360 3 day(s) RMSE : 9.48619077 3 day(s) R2 : -0.00507972 3 day(s) Pearson r : 0.23872082 3 day(s) QLIKE : 0.38878258 5 day(s) MAE : 2.79955066 5 day(s) RMSE : 9.53946106 5 day(s) R2 : -0.01316400 5 day(s) Pearson r : 0.19726214 5 day(s) QLIKE : 0.39985257 10 day(s) MAE : 2.84440518 10 day(s) RMSE : 9.59662015 10 day(s) R2 : -0.02294856 10 day(s) Pearson r : 0.14174197 10 day(s) QLIKE : 0.41193269 full horizon MAE : 2.84440518 full horizon RMSE : 9.59662015 full horizon R2 : -0.02294856 full horizon Pearson r : 0.14174197 full horizon QLIKE : 0.41193269 --- Task 2 --- 1 day(s) MAE : 0.70232961 1 day(s) RMSE : 1.31990754 1 day(s) R2 : -0.39470133 1 day(s) Pearson r : -0.00411324 1 day(s) QLIKE : 19.43378800 3 day(s) MAE : 0.70670024 3 day(s) RMSE : 1.32445608 3 day(s) R2 : -0.39763934 3 day(s) Pearson r : 0.00484610 3 day(s) QLIKE : 19.38850505 5 day(s) MAE : 0.70934500 5 day(s) RMSE : 1.32661225 5 day(s) R2 : -0.40009307 5 day(s) Pearson r : 0.00243988 5 day(s) QLIKE : 18.93820893 10 day(s) MAE : 0.71564725 10 day(s) RMSE : 1.33276365 10 day(s) R2 : -0.40454283 10 day(s) Pearson r : 0.00627965 10 day(s) QLIKE : 19.39781744 full horizon MAE : 0.71564725 full horizon RMSE : 1.33276365 full horizon R2 : -0.40454283 full horizon Pearson r : 0.00627965 full horizon QLIKE : 19.39781744 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/C/ITransformer_H10.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=1.88049, max=13.7505 === C | H=20 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.80914068636715 Min value: -9.273594127665117 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.111456003225517 Min value: -2.5798794252140365 Checking X_price_val: Shape: (161, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.833292676987295 Min value: -3.3424547859577265 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.643530346347732 Min value: -1.6643841678427629 Checking X_price_test: Shape: (404, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.61558566904287 Min value: -5.593651881065264 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.108493380986118 Min value: -2.0600067160446893 Epoch 1/50 Train 27.1941 Val 0.8313 0.3s Epoch 2/50 Train 26.9603 Val 0.8282 0.3s Epoch 3/50 Train 26.7462 Val 0.8252 0.2s Epoch 4/50 Train 26.5396 Val 0.8223 0.2s Epoch 5/50 Train 26.3405 Val 0.8195 0.3s Epoch 6/50 Train 26.1487 Val 0.8168 0.2s Epoch 7/50 Train 25.9639 Val 0.8143 0.2s Epoch 8/50 Train 25.7858 Val 0.8118 0.2s Epoch 9/50 Train 25.6142 Val 0.8093 0.2s Epoch 10/50 Train 25.4487 Val 0.8070 0.2s Epoch 11/50 Train 25.2890 Val 0.8047 0.3s Epoch 12/50 Train 25.1348 Val 0.8025 0.2s Epoch 13/50 Train 24.9859 Val 0.8004 0.2s Epoch 14/50 Train 24.8421 Val 0.7983 0.2s Epoch 15/50 Train 24.7030 Val 0.7963 0.2s Epoch 16/50 Train 24.5684 Val 0.7943 0.2s Epoch 17/50 Train 24.4383 Val 0.7924 0.2s Epoch 18/50 Train 24.3123 Val 0.7905 0.2s Epoch 19/50 Train 24.1902 Val 0.7887 0.2s Epoch 20/50 Train 24.0719 Val 0.7870 0.2s Epoch 21/50 Train 23.9573 Val 0.7852 0.2s Epoch 22/50 Train 23.8461 Val 0.7836 0.2s Epoch 23/50 Train 23.7383 Val 0.7819 0.2s Epoch 24/50 Train 23.6337 Val 0.7803 0.3s Epoch 25/50 Train 23.5321 Val 0.7788 0.2s Epoch 26/50 Train 23.4335 Val 0.7773 0.2s Epoch 27/50 Train 23.3377 Val 0.7758 0.3s Epoch 28/50 Train 23.2447 Val 0.7743 0.3s Epoch 29/50 Train 23.1543 Val 0.7729 0.3s Epoch 30/50 Train 23.0664 Val 0.7715 0.3s Epoch 31/50 Train 22.9810 Val 0.7702 0.3s Epoch 32/50 Train 22.8980 Val 0.7688 0.3s Epoch 33/50 Train 22.8172 Val 0.7675 0.3s Epoch 34/50 Train 22.7387 Val 0.7663 0.3s Epoch 35/50 Train 22.6624 Val 0.7650 0.3s Epoch 36/50 Train 22.5881 Val 0.7638 0.2s Epoch 37/50 Train 22.5158 Val 0.7626 0.2s Epoch 38/50 Train 22.4456 Val 0.7614 0.2s Epoch 39/50 Train 22.3772 Val 0.7603 0.2s Epoch 40/50 Train 22.3107 Val 0.7592 0.2s Epoch 41/50 Train 22.2460 Val 0.7581 0.2s Epoch 42/50 Train 22.1831 Val 0.7570 0.2s Epoch 43/50 Train 22.1219 Val 0.7560 0.2s Epoch 44/50 Train 22.0624 Val 0.7549 0.2s Epoch 45/50 Train 22.0045 Val 0.7539 0.2s Epoch 46/50 Train 21.9482 Val 0.7529 0.2s Epoch 47/50 Train 21.8935 Val 0.7520 0.2s Epoch 48/50 Train 21.8402 Val 0.7510 0.2s Epoch 49/50 Train 21.7885 Val 0.7501 0.2s Epoch 50/50 Train 21.7382 Val 0.7492 0.2s Parameters used in the single-fit model: input_len: 60 output_len: 20 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.70631479 1 day(s) RMSE : 9.42320368 1 day(s) R2 : 0.00336333 1 day(s) Pearson r : 0.30236464 1 day(s) QLIKE : 0.37209230 3 day(s) MAE : 2.77249987 3 day(s) RMSE : 9.50783989 3 day(s) R2 : -0.00967249 3 day(s) Pearson r : 0.21415863 3 day(s) QLIKE : 0.39384140 5 day(s) MAE : 2.80897893 5 day(s) RMSE : 9.55272473 5 day(s) R2 : -0.01598337 5 day(s) Pearson r : 0.17910323 5 day(s) QLIKE : 0.40347178 10 day(s) MAE : 2.85058594 10 day(s) RMSE : 9.60195301 10 day(s) R2 : -0.02408578 10 day(s) Pearson r : 0.13423456 10 day(s) QLIKE : 0.41284053 20 day(s) MAE : 2.88366101 20 day(s) RMSE : 9.63394037 20 day(s) R2 : -0.03010366 20 day(s) Pearson r : 0.09118316 20 day(s) QLIKE : 0.41818786 full horizon MAE : 2.88366101 full horizon RMSE : 9.63394037 full horizon R2 : -0.03010366 full horizon Pearson r : 0.09118316 full horizon QLIKE : 0.41818786 --- Task 2 --- 1 day(s) MAE : 0.70226960 1 day(s) RMSE : 1.31979455 1 day(s) R2 : -0.39446254 1 day(s) Pearson r : 0.02488110 1 day(s) QLIKE : 19.70470885 3 day(s) MAE : 0.70657815 3 day(s) RMSE : 1.32431427 3 day(s) R2 : -0.39734007 3 day(s) Pearson r : 0.02169638 3 day(s) QLIKE : 19.24765300 5 day(s) MAE : 0.70927187 5 day(s) RMSE : 1.32651146 5 day(s) R2 : -0.39988033 5 day(s) Pearson r : 0.01375201 5 day(s) QLIKE : 18.87363303 10 day(s) MAE : 0.71569802 10 day(s) RMSE : 1.33268300 10 day(s) R2 : -0.40437284 10 day(s) Pearson r : 0.00013399 10 day(s) QLIKE : 19.83611214 20 day(s) MAE : 0.72275205 20 day(s) RMSE : 1.34092018 20 day(s) R2 : -0.40874050 20 day(s) Pearson r : 0.00881202 20 day(s) QLIKE : 21.03369004 full horizon MAE : 0.72275205 full horizon RMSE : 1.34092018 full horizon R2 : -0.40874050 full horizon Pearson r : 0.00881202 full horizon QLIKE : 21.03369004 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/C/ITransformer_H20.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=1.68933, max=13.9002 === BTCUSDT | H=1 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2399 Time steps for y: 1 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514246752 Min value: -13.368147249813383 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.551349524903824 Min value: -3.605334167767801 Checking X_price_val: Shape: (192, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.571610898151818 Min value: -2.1425236768054545 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3448190912377083 Min value: -4.2413266524913515 Checking X_price_test: Shape: (480, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.959482481265578 Min value: -2.392150617253993 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.5802586573962314 Min value: -2.5674860022183505 Epoch 1/50 Train 30.8599 Val 1.2149 0.5s Epoch 2/50 Train 29.9380 Val 1.2131 0.3s Epoch 3/50 Train 29.1856 Val 1.2116 0.3s Epoch 4/50 Train 28.5342 Val 1.2105 0.3s Epoch 5/50 Train 27.9711 Val 1.2096 0.3s Epoch 6/50 Train 27.4833 Val 1.2090 0.3s Epoch 7/50 Train 27.0591 Val 1.2087 0.3s Epoch 8/50 Train 26.6885 Val 1.2085 0.3s Epoch 9/50 Train 26.3632 Val 1.2084 0.3s Epoch 10/50 Train 26.0763 Val 1.2085 0.3s Epoch 11/50 Train 25.8222 Val 1.2087 0.3s Epoch 12/50 Train 25.5959 Val 1.2089 0.3s Epoch 13/50 Train 25.3936 Val 1.2092 0.3s Epoch 14/50 Train 25.2119 Val 1.2095 0.3s Epoch 15/50 Train 25.0480 Val 1.2099 0.3s Epoch 16/50 Train 24.8994 Val 1.2103 0.3s Epoch 17/50 Train 24.7642 Val 1.2107 0.3s Epoch 18/50 Train 24.6406 Val 1.2111 0.3s Epoch 19/50 Train 24.5271 Val 1.2115 0.3s Epoch 20/50 Train 24.4225 Val 1.2119 0.3s Epoch 21/50 Train 24.3257 Val 1.2123 0.3s Epoch 22/50 Train 24.2358 Val 1.2127 0.3s Epoch 23/50 Train 24.1520 Val 1.2131 0.3s Epoch 24/50 Train 24.0735 Val 1.2135 0.4s Epoch 25/50 Train 23.9999 Val 1.2138 0.3s Epoch 26/50 Train 23.9305 Val 1.2142 0.3s Epoch 27/50 Train 23.8649 Val 1.2145 0.3s Epoch 28/50 Train 23.8027 Val 1.2149 0.4s Epoch 29/50 Train 23.7435 Val 1.2152 0.3s Epoch 30/50 Train 23.6872 Val 1.2155 0.3s Early stopping triggered. Parameters used in the single-fit model: input_len: 60 output_len: 1 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.56819729 1 day(s) RMSE : 9.13572798 1 day(s) R2 : 0.01090433 1 day(s) Pearson r : 0.23279664 1 day(s) QLIKE : 0.36080001 full horizon MAE : 4.56819729 full horizon RMSE : 9.13572798 full horizon R2 : 0.01090433 full horizon Pearson r : 0.23279664 full horizon QLIKE : 0.36080001 --- Task 2 --- 1 day(s) MAE : 1.07432868 1 day(s) RMSE : 2.07893535 1 day(s) R2 : -0.36418522 1 day(s) Pearson r : -0.02577862 1 day(s) QLIKE : 17.44495424 full horizon MAE : 1.07432868 full horizon RMSE : 2.07893535 full horizon R2 : -0.36418522 full horizon Pearson r : -0.02577862 full horizon QLIKE : 17.44495424 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BTCUSDT/ITransformer_H1.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=5.36128, max=10.3 === BTCUSDT | H=5 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2399 Time steps for y: 5 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514246752 Min value: -13.368147249813383 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.5541968122841485 Min value: -3.608723363499145 Checking X_price_val: Shape: (192, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.571610898151818 Min value: -2.1425236768054545 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.345979297163924 Min value: -4.245202118935104 Checking X_price_test: Shape: (480, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.959482481265578 Min value: -2.392150617253993 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.5815988770306517 Min value: -2.5700816741597823 Epoch 1/50 Train 32.6186 Val 1.2527 0.5s Epoch 2/50 Train 32.0400 Val 1.2506 0.3s Epoch 3/50 Train 31.5298 Val 1.2485 0.3s Epoch 4/50 Train 31.0571 Val 1.2465 0.3s Epoch 5/50 Train 30.6203 Val 1.2447 0.3s Epoch 6/50 Train 30.2169 Val 1.2429 0.3s Epoch 7/50 Train 29.8438 Val 1.2412 0.3s Epoch 8/50 Train 29.4984 Val 1.2396 0.3s Epoch 9/50 Train 29.1782 Val 1.2382 0.3s Epoch 10/50 Train 28.8809 Val 1.2368 0.3s Epoch 11/50 Train 28.6047 Val 1.2355 0.3s Epoch 12/50 Train 28.3476 Val 1.2343 0.3s Epoch 13/50 Train 28.1082 Val 1.2332 0.3s Epoch 14/50 Train 27.8850 Val 1.2322 0.3s Epoch 15/50 Train 27.6766 Val 1.2313 0.3s Epoch 16/50 Train 27.4820 Val 1.2304 0.3s Epoch 17/50 Train 27.2999 Val 1.2296 0.3s Epoch 18/50 Train 27.1294 Val 1.2289 0.3s Epoch 19/50 Train 26.9697 Val 1.2282 0.3s Epoch 20/50 Train 26.8198 Val 1.2275 0.3s Epoch 21/50 Train 26.6790 Val 1.2270 0.3s Epoch 22/50 Train 26.5467 Val 1.2264 0.3s Epoch 23/50 Train 26.4222 Val 1.2259 0.3s Epoch 24/50 Train 26.3049 Val 1.2255 0.3s Epoch 25/50 Train 26.1943 Val 1.2250 0.3s Epoch 26/50 Train 26.0898 Val 1.2246 0.3s Epoch 27/50 Train 25.9911 Val 1.2243 0.3s Epoch 28/50 Train 25.8978 Val 1.2239 0.3s Epoch 29/50 Train 25.8094 Val 1.2236 0.3s Epoch 30/50 Train 25.7255 Val 1.2233 0.4s Epoch 31/50 Train 25.6460 Val 1.2231 0.3s Epoch 32/50 Train 25.5704 Val 1.2228 0.3s Epoch 33/50 Train 25.4986 Val 1.2226 0.3s Epoch 34/50 Train 25.4302 Val 1.2224 0.4s Epoch 35/50 Train 25.3650 Val 1.2222 0.3s Epoch 36/50 Train 25.3028 Val 1.2220 0.3s Epoch 37/50 Train 25.2434 Val 1.2218 0.3s Epoch 38/50 Train 25.1866 Val 1.2217 0.3s Epoch 39/50 Train 25.1323 Val 1.2215 0.3s Epoch 40/50 Train 25.0803 Val 1.2214 0.3s Epoch 41/50 Train 25.0305 Val 1.2213 0.3s Epoch 42/50 Train 24.9826 Val 1.2211 0.3s Epoch 43/50 Train 24.9367 Val 1.2210 0.3s Epoch 44/50 Train 24.8925 Val 1.2209 0.3s Epoch 45/50 Train 24.8500 Val 1.2208 0.3s Epoch 46/50 Train 24.8090 Val 1.2207 0.3s Epoch 47/50 Train 24.7695 Val 1.2206 0.3s Epoch 48/50 Train 24.7315 Val 1.2206 0.3s Epoch 49/50 Train 24.6947 Val 1.2205 0.3s Epoch 50/50 Train 24.6591 Val 1.2204 0.3s Parameters used in the single-fit model: input_len: 60 output_len: 5 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.57932620 1 day(s) RMSE : 9.14450809 1 day(s) R2 : 0.00900223 1 day(s) Pearson r : 0.21966725 1 day(s) QLIKE : 0.36219083 3 day(s) MAE : 4.63116424 3 day(s) RMSE : 9.19633994 3 day(s) R2 : -0.00212486 3 day(s) Pearson r : 0.14545807 3 day(s) QLIKE : 0.37042560 5 day(s) MAE : 4.65333735 5 day(s) RMSE : 9.22392387 5 day(s) R2 : -0.00846164 5 day(s) Pearson r : 0.09558021 5 day(s) QLIKE : 0.37417337 full horizon MAE : 4.65333735 full horizon RMSE : 9.22392387 full horizon R2 : -0.00846164 full horizon Pearson r : 0.09558021 full horizon QLIKE : 0.37417337 --- Task 2 --- 1 day(s) MAE : 1.07441102 1 day(s) RMSE : 2.07892683 1 day(s) R2 : -0.36417403 1 day(s) Pearson r : -0.02518527 1 day(s) QLIKE : 17.05803713 3 day(s) MAE : 1.07488781 3 day(s) RMSE : 2.07925103 3 day(s) R2 : -0.36456769 3 day(s) Pearson r : -0.00351063 3 day(s) QLIKE : 16.77037832 5 day(s) MAE : 1.07514942 5 day(s) RMSE : 2.07933487 5 day(s) R2 : -0.36480724 5 day(s) Pearson r : 0.00157542 5 day(s) QLIKE : 16.57784562 full horizon MAE : 1.07514942 full horizon RMSE : 2.07933487 full horizon R2 : -0.36480724 full horizon Pearson r : 0.00157542 full horizon QLIKE : 16.57784562 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BTCUSDT/ITransformer_H5.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=5.05334, max=10.371 === BTCUSDT | H=10 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2399 Time steps for y: 10 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514246752 Min value: -13.368147249813383 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.556915178048574 Min value: -3.6122923012225012 Checking X_price_val: Shape: (192, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.571610898151818 Min value: -2.1425236768054545 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3469968336435327 Min value: -4.249261289934393 Checking X_price_test: Shape: (480, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.959482481265578 Min value: -2.392150617253993 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.5827978941292526 Min value: -2.57285062176008 Epoch 1/50 Train 33.2139 Val 1.2563 0.4s Epoch 2/50 Train 32.7574 Val 1.2545 0.3s Epoch 3/50 Train 32.3451 Val 1.2528 0.3s Epoch 4/50 Train 31.9559 Val 1.2511 0.3s Epoch 5/50 Train 31.5896 Val 1.2494 0.3s Epoch 6/50 Train 31.2453 Val 1.2478 0.3s Epoch 7/50 Train 30.9216 Val 1.2463 0.3s Epoch 8/50 Train 30.6168 Val 1.2448 0.3s Epoch 9/50 Train 30.3296 Val 1.2433 0.3s Epoch 10/50 Train 30.0588 Val 1.2419 0.3s Epoch 11/50 Train 29.8032 Val 1.2406 0.3s Epoch 12/50 Train 29.5617 Val 1.2393 0.3s Epoch 13/50 Train 29.3335 Val 1.2381 0.3s Epoch 14/50 Train 29.1175 Val 1.2369 0.3s Epoch 15/50 Train 28.9131 Val 1.2358 0.3s Epoch 16/50 Train 28.7194 Val 1.2348 0.3s Epoch 17/50 Train 28.5359 Val 1.2338 0.3s Epoch 18/50 Train 28.3619 Val 1.2328 0.3s Epoch 19/50 Train 28.1968 Val 1.2319 0.3s Epoch 20/50 Train 28.0401 Val 1.2310 0.3s Epoch 21/50 Train 27.8913 Val 1.2302 0.3s Epoch 22/50 Train 27.7500 Val 1.2294 0.3s Epoch 23/50 Train 27.6156 Val 1.2286 0.3s Epoch 24/50 Train 27.4879 Val 1.2279 0.3s Epoch 25/50 Train 27.3664 Val 1.2272 0.3s Epoch 26/50 Train 27.2507 Val 1.2266 0.3s Epoch 27/50 Train 27.1407 Val 1.2260 0.3s Epoch 28/50 Train 27.0358 Val 1.2254 0.3s Epoch 29/50 Train 26.9359 Val 1.2248 0.3s Epoch 30/50 Train 26.8407 Val 1.2243 0.3s Epoch 31/50 Train 26.7500 Val 1.2238 0.3s Epoch 32/50 Train 26.6634 Val 1.2233 0.3s Epoch 33/50 Train 26.5808 Val 1.2229 0.3s Epoch 34/50 Train 26.5019 Val 1.2225 0.3s Epoch 35/50 Train 26.4266 Val 1.2221 0.3s Epoch 36/50 Train 26.3546 Val 1.2217 0.3s Epoch 37/50 Train 26.2858 Val 1.2213 0.3s Epoch 38/50 Train 26.2201 Val 1.2209 0.3s Epoch 39/50 Train 26.1572 Val 1.2206 0.3s Epoch 40/50 Train 26.0970 Val 1.2203 0.4s Epoch 41/50 Train 26.0394 Val 1.2200 0.3s Epoch 42/50 Train 25.9842 Val 1.2197 0.3s Epoch 43/50 Train 25.9313 Val 1.2194 0.4s Epoch 44/50 Train 25.8807 Val 1.2192 0.3s Epoch 45/50 Train 25.8321 Val 1.2189 0.3s Epoch 46/50 Train 25.7855 Val 1.2187 0.3s Epoch 47/50 Train 25.7408 Val 1.2185 0.4s Epoch 48/50 Train 25.6978 Val 1.2182 0.3s Epoch 49/50 Train 25.6566 Val 1.2180 0.3s Epoch 50/50 Train 25.6170 Val 1.2178 0.3s Parameters used in the single-fit model: input_len: 60 output_len: 10 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.58544661 1 day(s) RMSE : 9.14835549 1 day(s) R2 : 0.00816816 1 day(s) Pearson r : 0.21215352 1 day(s) QLIKE : 0.36296119 3 day(s) MAE : 4.64641993 3 day(s) RMSE : 9.20348794 3 day(s) R2 : -0.00368330 3 day(s) Pearson r : 0.13168242 3 day(s) QLIKE : 0.37183984 5 day(s) MAE : 4.67175682 5 day(s) RMSE : 9.23254202 5 day(s) R2 : -0.01034698 5 day(s) Pearson r : 0.07788501 5 day(s) QLIKE : 0.37587060 10 day(s) MAE : 4.69765340 10 day(s) RMSE : 9.26094785 10 day(s) R2 : -0.01632901 10 day(s) Pearson r : 0.02949930 10 day(s) QLIKE : 0.38041279 full horizon MAE : 4.69765340 full horizon RMSE : 9.26094785 full horizon R2 : -0.01632901 full horizon Pearson r : 0.02949930 full horizon QLIKE : 0.38041279 --- Task 2 --- 1 day(s) MAE : 1.07456617 1 day(s) RMSE : 2.07888773 1 day(s) R2 : -0.36412272 1 day(s) Pearson r : -0.02377877 1 day(s) QLIKE : 17.19035464 3 day(s) MAE : 1.07493604 3 day(s) RMSE : 2.07923487 3 day(s) R2 : -0.36454648 3 day(s) Pearson r : -0.00697546 3 day(s) QLIKE : 16.86138621 5 day(s) MAE : 1.07517958 5 day(s) RMSE : 2.07932597 5 day(s) R2 : -0.36479556 5 day(s) Pearson r : -0.00401541 5 day(s) QLIKE : 16.74062254 10 day(s) MAE : 1.07573453 10 day(s) RMSE : 2.07955548 10 day(s) R2 : -0.36523024 10 day(s) Pearson r : -0.00020532 10 day(s) QLIKE : 16.56821238 full horizon MAE : 1.07573453 full horizon RMSE : 2.07955548 full horizon R2 : -0.36523024 full horizon Pearson r : -0.00020532 full horizon QLIKE : 16.56821238 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BTCUSDT/ITransformer_H10.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=4.96807, max=10.5114 === BTCUSDT | H=20 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 2399 Time steps for y: 20 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514246752 Min value: -13.368147249813383 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.564043013270618 Min value: -3.620214771499493 Checking X_price_val: Shape: (192, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.571610898151818 Min value: -2.1425236768054545 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3500532890780494 Min value: -4.25835726179007 Checking X_price_test: Shape: (480, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.959482481265578 Min value: -2.392150617253993 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.5862887708961093 Min value: -2.578858106420164 Epoch 1/50 Train 34.2684 Val 1.2553 0.4s Epoch 2/50 Train 33.8747 Val 1.2530 0.3s Epoch 3/50 Train 33.5116 Val 1.2508 0.3s Epoch 4/50 Train 33.1639 Val 1.2487 0.3s Epoch 5/50 Train 32.8322 Val 1.2466 0.3s Epoch 6/50 Train 32.5164 Val 1.2446 0.3s Epoch 7/50 Train 32.2156 Val 1.2427 0.3s Epoch 8/50 Train 31.9287 Val 1.2408 0.3s Epoch 9/50 Train 31.6549 Val 1.2391 0.3s Epoch 10/50 Train 31.3932 Val 1.2374 0.3s Epoch 11/50 Train 31.1427 Val 1.2357 0.3s Epoch 12/50 Train 30.9028 Val 1.2342 0.3s Epoch 13/50 Train 30.6726 Val 1.2326 0.3s Epoch 14/50 Train 30.4515 Val 1.2312 0.3s Epoch 15/50 Train 30.2390 Val 1.2298 0.3s Epoch 16/50 Train 30.0346 Val 1.2284 0.3s Epoch 17/50 Train 29.8377 Val 1.2271 0.3s Epoch 18/50 Train 29.6481 Val 1.2258 0.3s Epoch 19/50 Train 29.4652 Val 1.2246 0.3s Epoch 20/50 Train 29.2889 Val 1.2234 0.3s Epoch 21/50 Train 29.1189 Val 1.2222 0.3s Epoch 22/50 Train 28.9548 Val 1.2211 0.3s Epoch 23/50 Train 28.7965 Val 1.2200 0.3s Epoch 24/50 Train 28.6438 Val 1.2190 0.3s Epoch 25/50 Train 28.4966 Val 1.2179 0.3s Epoch 26/50 Train 28.3546 Val 1.2169 0.3s Epoch 27/50 Train 28.2178 Val 1.2160 0.3s Epoch 28/50 Train 28.0861 Val 1.2151 0.3s Epoch 29/50 Train 27.9593 Val 1.2142 0.3s Epoch 30/50 Train 27.8375 Val 1.2133 0.3s Epoch 31/50 Train 27.7205 Val 1.2125 0.3s Epoch 32/50 Train 27.6082 Val 1.2116 0.3s Epoch 33/50 Train 27.5006 Val 1.2109 0.3s Epoch 34/50 Train 27.3976 Val 1.2101 0.3s Epoch 35/50 Train 27.2992 Val 1.2094 0.3s Epoch 36/50 Train 27.2051 Val 1.2087 0.3s Epoch 37/50 Train 27.1155 Val 1.2080 0.3s Epoch 38/50 Train 27.0301 Val 1.2074 0.3s Epoch 39/50 Train 26.9489 Val 1.2068 0.3s Epoch 40/50 Train 26.8717 Val 1.2062 0.3s Epoch 41/50 Train 26.7985 Val 1.2057 0.3s Epoch 42/50 Train 26.7291 Val 1.2051 0.3s Epoch 43/50 Train 26.6634 Val 1.2046 0.3s Epoch 44/50 Train 26.6012 Val 1.2041 0.3s Epoch 45/50 Train 26.5425 Val 1.2037 0.3s Epoch 46/50 Train 26.4870 Val 1.2033 0.3s Epoch 47/50 Train 26.4346 Val 1.2029 0.3s Epoch 48/50 Train 26.3852 Val 1.2025 0.3s Epoch 49/50 Train 26.3386 Val 1.2021 0.3s Epoch 50/50 Train 26.2947 Val 1.2018 0.3s Parameters used in the single-fit model: input_len: 60 output_len: 20 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.63929678 1 day(s) RMSE : 9.18348201 1 day(s) R2 : 0.00053695 1 day(s) Pearson r : 0.16082911 1 day(s) QLIKE : 0.36934606 3 day(s) MAE : 4.68231190 3 day(s) RMSE : 9.22573041 3 day(s) R2 : -0.00854045 3 day(s) Pearson r : 0.09061207 3 day(s) QLIKE : 0.37610574 5 day(s) MAE : 4.70119127 5 day(s) RMSE : 9.24766953 5 day(s) R2 : -0.01366060 5 day(s) Pearson r : 0.04548436 5 day(s) QLIKE : 0.37889718 10 day(s) MAE : 4.72196144 10 day(s) RMSE : 9.27015628 10 day(s) R2 : -0.01835114 10 day(s) Pearson r : 0.00381513 10 day(s) QLIKE : 0.38248811 20 day(s) MAE : 4.74077585 20 day(s) RMSE : 9.29371604 20 day(s) R2 : -0.02389550 20 day(s) Pearson r : -0.04187069 20 day(s) QLIKE : 0.38518369 full horizon MAE : 4.74077585 full horizon RMSE : 9.29371604 full horizon R2 : -0.02389550 full horizon Pearson r : -0.04187069 full horizon QLIKE : 0.38518369 --- Task 2 --- 1 day(s) MAE : 1.07430688 1 day(s) RMSE : 2.07893146 1 day(s) R2 : -0.36418011 1 day(s) Pearson r : -0.02172156 1 day(s) QLIKE : 16.41582240 3 day(s) MAE : 1.07485180 3 day(s) RMSE : 2.07925768 3 day(s) R2 : -0.36457642 3 day(s) Pearson r : 0.00564538 3 day(s) QLIKE : 16.14046695 5 day(s) MAE : 1.07513051 5 day(s) RMSE : 2.07934401 5 day(s) R2 : -0.36481924 5 day(s) Pearson r : 0.00788970 5 day(s) QLIKE : 16.01422074 10 day(s) MAE : 1.07572604 10 day(s) RMSE : 2.07958537 10 day(s) R2 : -0.36526948 10 day(s) Pearson r : -0.00262408 10 day(s) QLIKE : 15.87760580 20 day(s) MAE : 1.07565920 20 day(s) RMSE : 2.07745150 20 day(s) R2 : -0.36623701 20 day(s) Pearson r : -0.00558122 20 day(s) QLIKE : 15.99342530 full horizon MAE : 1.07565920 full horizon RMSE : 2.07745150 full horizon R2 : -0.36623701 full horizon Pearson r : -0.00558122 full horizon QLIKE : 15.99342530 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/BTCUSDT/ITransformer_H20.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=4.89321, max=9.37998 === EURUSD | H=1 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 3782 Time steps for y: 1 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525526 Min value: -6.065318026131415 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.996396510173233 Min value: -3.718602223017602 Checking X_price_val: Shape: (302, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.40892116517157 Min value: -3.843231093045373 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.319059579642507 Min value: -2.9759282116206096 Checking X_price_test: Shape: (757, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144347 Min value: -4.219325170615048 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3834322546316087 Min value: -5.3499699676080334 Epoch 1/50 Train 51.8466 Val 1.3247 0.6s Epoch 2/50 Train 48.6439 Val 1.2399 0.4s Epoch 3/50 Train 46.2796 Val 1.1752 0.4s Epoch 4/50 Train 44.4880 Val 1.1251 0.5s Epoch 5/50 Train 43.1070 Val 1.0857 0.5s Epoch 6/50 Train 42.0202 Val 1.0541 0.5s Epoch 7/50 Train 41.1478 Val 1.0284 0.6s Epoch 8/50 Train 40.4344 Val 1.0072 0.5s Epoch 9/50 Train 39.8407 Val 0.9893 0.5s Epoch 10/50 Train 39.3385 Val 0.9742 0.4s Epoch 11/50 Train 38.9068 Val 0.9612 0.4s Epoch 12/50 Train 38.5304 Val 0.9499 0.4s Epoch 13/50 Train 38.1977 Val 0.9400 0.4s Epoch 14/50 Train 37.8999 Val 0.9312 0.4s Epoch 15/50 Train 37.6303 Val 0.9233 0.4s Epoch 16/50 Train 37.3839 Val 0.9163 0.4s Epoch 17/50 Train 37.1566 Val 0.9099 0.4s Epoch 18/50 Train 36.9453 Val 0.9040 0.4s Epoch 19/50 Train 36.7476 Val 0.8987 0.4s Epoch 20/50 Train 36.5616 Val 0.8938 0.4s Epoch 21/50 Train 36.3855 Val 0.8892 0.4s Epoch 22/50 Train 36.2181 Val 0.8850 0.5s Epoch 23/50 Train 36.0584 Val 0.8811 0.5s Epoch 24/50 Train 35.9054 Val 0.8775 0.5s Epoch 25/50 Train 35.7584 Val 0.8741 0.5s Epoch 26/50 Train 35.6167 Val 0.8708 0.5s Epoch 27/50 Train 35.4798 Val 0.8678 0.4s Epoch 28/50 Train 35.3471 Val 0.8650 0.4s Epoch 29/50 Train 35.2184 Val 0.8623 0.4s Epoch 30/50 Train 35.0932 Val 0.8598 0.4s Epoch 31/50 Train 34.9711 Val 0.8574 0.4s Epoch 32/50 Train 34.8520 Val 0.8551 0.4s Epoch 33/50 Train 34.7355 Val 0.8529 0.4s Epoch 34/50 Train 34.6215 Val 0.8509 0.4s Epoch 35/50 Train 34.5097 Val 0.8490 0.4s Epoch 36/50 Train 34.4000 Val 0.8471 0.4s Epoch 37/50 Train 34.2921 Val 0.8453 0.4s Epoch 38/50 Train 34.1860 Val 0.8437 0.4s Epoch 39/50 Train 34.0815 Val 0.8421 0.4s Epoch 40/50 Train 33.9784 Val 0.8406 0.4s Epoch 41/50 Train 33.8768 Val 0.8391 0.4s Epoch 42/50 Train 33.7764 Val 0.8378 0.4s Epoch 43/50 Train 33.6772 Val 0.8365 0.4s Epoch 44/50 Train 33.5790 Val 0.8352 0.4s Epoch 45/50 Train 33.4819 Val 0.8341 0.4s Epoch 46/50 Train 33.3858 Val 0.8330 0.4s Epoch 47/50 Train 33.2905 Val 0.8319 0.4s Epoch 48/50 Train 33.1960 Val 0.8309 0.4s Epoch 49/50 Train 33.1023 Val 0.8300 0.4s Epoch 50/50 Train 33.0093 Val 0.8291 0.4s Parameters used in the single-fit model: input_len: 60 output_len: 1 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.10740309 1 day(s) RMSE : 0.21317248 1 day(s) R2 : 0.00440063 1 day(s) Pearson r : 0.39703932 1 day(s) QLIKE : 0.40742558 full horizon MAE : 0.10740309 full horizon RMSE : 0.21317248 full horizon R2 : 0.00440063 full horizon Pearson r : 0.39703932 full horizon QLIKE : 0.40742558 --- Task 2 --- 1 day(s) MAE : 0.15718333 1 day(s) RMSE : 0.31431941 1 day(s) R2 : -0.35386302 1 day(s) Pearson r : -0.01014323 1 day(s) QLIKE : 20.56105744 full horizon MAE : 0.15718333 full horizon RMSE : 0.31431941 full horizon R2 : -0.35386302 full horizon Pearson r : -0.01014323 full horizon QLIKE : 20.56105744 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/EURUSD/ITransformer_H1.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.0226416, max=1.39534 === EURUSD | H=5 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 3782 Time steps for y: 5 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525526 Min value: -6.065318026131415 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.997012744911091 Min value: -3.7204515821714925 Checking X_price_val: Shape: (302, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.40892116517157 Min value: -3.843231093045373 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3194271117573377 Min value: -2.977504877870329 Checking X_price_test: Shape: (757, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144347 Min value: -4.219325170615048 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3838234229198108 Min value: -5.352418327671095 Epoch 1/50 Train 51.8824 Val 1.3031 0.6s Epoch 2/50 Train 50.0269 Val 1.2519 0.4s Epoch 3/50 Train 48.4626 Val 1.2083 0.4s Epoch 4/50 Train 47.1263 Val 1.1710 0.4s Epoch 5/50 Train 45.9842 Val 1.1390 0.4s Epoch 6/50 Train 45.0035 Val 1.1114 0.4s Epoch 7/50 Train 44.1571 Val 1.0874 0.4s Epoch 8/50 Train 43.4225 Val 1.0664 0.4s Epoch 9/50 Train 42.7814 Val 1.0480 0.4s Epoch 10/50 Train 42.2188 Val 1.0318 0.4s Epoch 11/50 Train 41.7224 Val 1.0175 0.5s Epoch 12/50 Train 41.2820 Val 1.0047 0.5s Epoch 13/50 Train 40.8892 Val 0.9933 0.5s Epoch 14/50 Train 40.5371 Val 0.9830 0.6s Epoch 15/50 Train 40.2199 Val 0.9738 0.5s Epoch 16/50 Train 39.9327 Val 0.9654 0.5s Epoch 17/50 Train 39.6714 Val 0.9579 0.4s Epoch 18/50 Train 39.4327 Val 0.9509 0.4s Epoch 19/50 Train 39.2136 Val 0.9446 0.4s Epoch 20/50 Train 39.0117 Val 0.9388 0.4s Epoch 21/50 Train 38.8248 Val 0.9335 0.4s Epoch 22/50 Train 38.6512 Val 0.9286 0.4s Epoch 23/50 Train 38.4893 Val 0.9240 0.4s Epoch 24/50 Train 38.3378 Val 0.9198 0.4s Epoch 25/50 Train 38.1956 Val 0.9159 0.4s Epoch 26/50 Train 38.0616 Val 0.9122 0.4s Epoch 27/50 Train 37.9350 Val 0.9088 0.5s Epoch 28/50 Train 37.8151 Val 0.9055 0.5s Epoch 29/50 Train 37.7010 Val 0.9025 0.4s Epoch 30/50 Train 37.5923 Val 0.8996 0.4s Epoch 31/50 Train 37.4885 Val 0.8969 0.4s Epoch 32/50 Train 37.3890 Val 0.8944 0.4s Epoch 33/50 Train 37.2935 Val 0.8919 0.4s Epoch 34/50 Train 37.2016 Val 0.8896 0.4s Epoch 35/50 Train 37.1129 Val 0.8874 0.4s Epoch 36/50 Train 37.0273 Val 0.8853 0.4s Epoch 37/50 Train 36.9444 Val 0.8833 0.4s Epoch 38/50 Train 36.8640 Val 0.8814 0.4s Epoch 39/50 Train 36.7859 Val 0.8796 0.4s Epoch 40/50 Train 36.7098 Val 0.8778 0.4s Epoch 41/50 Train 36.6357 Val 0.8761 0.4s Epoch 42/50 Train 36.5634 Val 0.8745 0.4s Epoch 43/50 Train 36.4927 Val 0.8729 0.4s Epoch 44/50 Train 36.4235 Val 0.8714 0.4s Epoch 45/50 Train 36.3557 Val 0.8699 0.4s Epoch 46/50 Train 36.2891 Val 0.8685 0.4s Epoch 47/50 Train 36.2238 Val 0.8671 0.4s Epoch 48/50 Train 36.1595 Val 0.8658 0.4s Epoch 49/50 Train 36.0963 Val 0.8645 0.4s Epoch 50/50 Train 36.0340 Val 0.8633 0.4s Parameters used in the single-fit model: input_len: 60 output_len: 5 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.11069154 1 day(s) RMSE : 0.21376866 1 day(s) R2 : -0.00117593 1 day(s) Pearson r : 0.38421541 1 day(s) QLIKE : 0.43660064 3 day(s) MAE : 0.11004906 3 day(s) RMSE : 0.21312944 3 day(s) R2 : -0.00287517 3 day(s) Pearson r : 0.36990348 3 day(s) QLIKE : 0.43712543 5 day(s) MAE : 0.11013812 5 day(s) RMSE : 0.21358665 5 day(s) R2 : -0.01862768 5 day(s) Pearson r : 0.35784695 5 day(s) QLIKE : 0.43917631 full horizon MAE : 0.11013812 full horizon RMSE : 0.21358665 full horizon R2 : -0.01862768 full horizon Pearson r : 0.35784695 full horizon QLIKE : 0.43917631 --- Task 2 --- 1 day(s) MAE : 0.15554622 1 day(s) RMSE : 0.31160090 1 day(s) R2 : -0.33054559 1 day(s) Pearson r : -0.02222840 1 day(s) QLIKE : 17.47711197 3 day(s) MAE : 0.15558054 3 day(s) RMSE : 0.31315213 3 day(s) R2 : -0.35985700 3 day(s) Pearson r : 0.04490921 3 day(s) QLIKE : 19.85736359 5 day(s) MAE : 0.15492740 5 day(s) RMSE : 0.31176308 5 day(s) R2 : -0.35444635 5 day(s) Pearson r : 0.03787643 5 day(s) QLIKE : 19.37374005 full horizon MAE : 0.15492740 full horizon RMSE : 0.31176308 full horizon R2 : -0.35444635 full horizon Pearson r : 0.03787643 full horizon QLIKE : 19.37374005 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/EURUSD/ITransformer_H5.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.0332308, max=1.59374 === EURUSD | H=10 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 3782 Time steps for y: 10 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525526 Min value: -6.065318026131415 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9973267885669737 Min value: -3.719996871136402 Checking X_price_val: Shape: (302, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.40892116517157 Min value: -3.843231093045373 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3197553444271937 Min value: -2.9770657245458123 Checking X_price_test: Shape: (757, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144347 Min value: -4.219325170615048 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3841503070956698 Min value: -5.351929442359245 Epoch 1/50 Train 51.9688 Val 1.3031 0.6s Epoch 2/50 Train 50.5664 Val 1.2625 0.4s Epoch 3/50 Train 49.3322 Val 1.2267 0.4s Epoch 4/50 Train 48.2363 Val 1.1951 0.4s Epoch 5/50 Train 47.2663 Val 1.1673 0.4s Epoch 6/50 Train 46.4064 Val 1.1426 0.4s Epoch 7/50 Train 45.6421 Val 1.1207 0.4s Epoch 8/50 Train 44.9607 Val 1.1012 0.4s Epoch 9/50 Train 44.3513 Val 1.0837 0.4s Epoch 10/50 Train 43.8045 Val 1.0681 0.4s Epoch 11/50 Train 43.3122 Val 1.0540 0.4s Epoch 12/50 Train 42.8677 Val 1.0412 0.4s Epoch 13/50 Train 42.4649 Val 1.0297 0.4s Epoch 14/50 Train 42.0989 Val 1.0192 0.6s Epoch 15/50 Train 41.7652 Val 1.0096 0.5s Epoch 16/50 Train 41.4601 Val 1.0009 0.5s Epoch 17/50 Train 41.1803 Val 0.9928 0.6s Epoch 18/50 Train 40.9231 Val 0.9855 0.5s Epoch 19/50 Train 40.6860 Val 0.9787 0.4s Epoch 20/50 Train 40.4668 Val 0.9724 0.4s Epoch 21/50 Train 40.2638 Val 0.9667 0.4s Epoch 22/50 Train 40.0751 Val 0.9613 0.4s Epoch 23/50 Train 39.8995 Val 0.9563 0.4s Epoch 24/50 Train 39.7355 Val 0.9517 0.4s Epoch 25/50 Train 39.5822 Val 0.9473 0.4s Epoch 26/50 Train 39.4385 Val 0.9433 0.4s Epoch 27/50 Train 39.3034 Val 0.9394 0.4s Epoch 28/50 Train 39.1763 Val 0.9359 0.4s Epoch 29/50 Train 39.0563 Val 0.9325 0.4s Epoch 30/50 Train 38.9428 Val 0.9293 0.4s Epoch 31/50 Train 38.8353 Val 0.9263 0.4s Epoch 32/50 Train 38.7332 Val 0.9235 0.4s Epoch 33/50 Train 38.6361 Val 0.9208 0.4s Epoch 34/50 Train 38.5435 Val 0.9183 0.4s Epoch 35/50 Train 38.4550 Val 0.9158 0.4s Epoch 36/50 Train 38.3703 Val 0.9135 0.4s Epoch 37/50 Train 38.2891 Val 0.9113 0.4s Epoch 38/50 Train 38.2111 Val 0.9092 0.4s Epoch 39/50 Train 38.1360 Val 0.9072 0.4s Epoch 40/50 Train 38.0635 Val 0.9052 0.4s Epoch 41/50 Train 37.9935 Val 0.9033 0.4s Epoch 42/50 Train 37.9258 Val 0.9015 0.4s Epoch 43/50 Train 37.8601 Val 0.8998 0.4s Epoch 44/50 Train 37.7963 Val 0.8981 0.4s Epoch 45/50 Train 37.7343 Val 0.8965 0.4s Epoch 46/50 Train 37.6739 Val 0.8949 0.4s Epoch 47/50 Train 37.6150 Val 0.8934 0.4s Epoch 48/50 Train 37.5574 Val 0.8920 0.4s Epoch 49/50 Train 37.5011 Val 0.8905 0.4s Epoch 50/50 Train 37.4460 Val 0.8891 0.4s Parameters used in the single-fit model: input_len: 60 output_len: 10 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.11241824 1 day(s) RMSE : 0.21327612 1 day(s) R2 : 0.00343232 1 day(s) Pearson r : 0.37827363 1 day(s) QLIKE : 0.45527922 3 day(s) MAE : 0.11128690 3 day(s) RMSE : 0.21298418 3 day(s) R2 : -0.00150860 3 day(s) Pearson r : 0.36543215 3 day(s) QLIKE : 0.44910316 5 day(s) MAE : 0.11131507 5 day(s) RMSE : 0.21338882 5 day(s) R2 : -0.01674159 5 day(s) Pearson r : 0.35254399 5 day(s) QLIKE : 0.45279564 10 day(s) MAE : 0.11222074 10 day(s) RMSE : 0.21664850 10 day(s) R2 : -0.05804605 10 day(s) Pearson r : 0.32289727 10 day(s) QLIKE : 0.46333618 full horizon MAE : 0.11222074 full horizon RMSE : 0.21664850 full horizon R2 : -0.05804605 full horizon Pearson r : 0.32289727 full horizon QLIKE : 0.46333618 --- Task 2 --- 1 day(s) MAE : 0.15561729 1 day(s) RMSE : 0.31160571 1 day(s) R2 : -0.33058665 1 day(s) Pearson r : -0.03785973 1 day(s) QLIKE : 17.22103285 3 day(s) MAE : 0.15582354 3 day(s) RMSE : 0.31462757 3 day(s) R2 : -0.37270132 3 day(s) Pearson r : 0.04505178 3 day(s) QLIKE : 19.88995231 5 day(s) MAE : 0.15894415 5 day(s) RMSE : 0.36479184 5 day(s) R2 : -0.85439683 5 day(s) Pearson r : 0.01876336 5 day(s) QLIKE : 19.37891985 10 day(s) MAE : 0.15588827 10 day(s) RMSE : 0.33725861 10 day(s) R2 : -0.59177874 10 day(s) Pearson r : 0.01448535 10 day(s) QLIKE : 18.88679584 full horizon MAE : 0.15588827 full horizon RMSE : 0.33725861 full horizon R2 : -0.59177874 full horizon Pearson r : 0.01448535 full horizon QLIKE : 18.88679584 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/EURUSD/ITransformer_H10.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.0371635, max=1.65787 === EURUSD | H=20 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 19.199765452525526 Min value: -6.065318026131415 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.998957169490096 Min value: -3.7200073694899167 Checking X_price_val: Shape: (302, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 7.40892116517157 Min value: -3.843231093045373 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.321220211077452 Min value: -2.97689474283611 Checking X_price_test: Shape: (757, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144347 Min value: -4.219325170615048 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737455 Min value: -5.35233858226595 Epoch 1/50 Train 54.0597 Val 1.3526 0.5s Epoch 2/50 Train 52.8202 Val 1.3182 0.4s Epoch 3/50 Train 51.6931 Val 1.2867 0.4s Epoch 4/50 Train 50.6634 Val 1.2582 0.4s Epoch 5/50 Train 49.7266 Val 1.2322 0.4s Epoch 6/50 Train 48.8733 Val 1.2085 0.4s Epoch 7/50 Train 48.0942 Val 1.1869 0.4s Epoch 8/50 Train 47.3806 Val 1.1670 0.4s Epoch 9/50 Train 46.7251 Val 1.1486 0.4s Epoch 10/50 Train 46.1216 Val 1.1318 0.4s Epoch 11/50 Train 45.5646 Val 1.1161 0.4s Epoch 12/50 Train 45.0500 Val 1.1017 0.4s Epoch 13/50 Train 44.5740 Val 1.0884 0.6s Epoch 14/50 Train 44.1335 Val 1.0760 0.5s Epoch 15/50 Train 43.7259 Val 1.0646 0.5s Epoch 16/50 Train 43.3489 Val 1.0541 0.5s Epoch 17/50 Train 43.0004 Val 1.0443 0.5s Epoch 18/50 Train 42.6788 Val 1.0354 0.5s Epoch 19/50 Train 42.3821 Val 1.0271 0.4s Epoch 20/50 Train 42.1090 Val 1.0195 0.4s Epoch 21/50 Train 41.8580 Val 1.0126 0.4s Epoch 22/50 Train 41.6274 Val 1.0062 0.4s Epoch 23/50 Train 41.4159 Val 1.0003 0.4s Epoch 24/50 Train 41.2221 Val 0.9950 0.4s Epoch 25/50 Train 41.0445 Val 0.9900 0.4s Epoch 26/50 Train 40.8818 Val 0.9855 0.4s Epoch 27/50 Train 40.7327 Val 0.9814 0.4s Epoch 28/50 Train 40.5958 Val 0.9776 0.4s Epoch 29/50 Train 40.4699 Val 0.9741 0.4s Epoch 30/50 Train 40.3539 Val 0.9708 0.4s Epoch 31/50 Train 40.2468 Val 0.9678 0.4s Epoch 32/50 Train 40.1475 Val 0.9650 0.4s Epoch 33/50 Train 40.0553 Val 0.9624 0.4s Epoch 34/50 Train 39.9692 Val 0.9599 0.4s Epoch 35/50 Train 39.8886 Val 0.9576 0.4s Epoch 36/50 Train 39.8128 Val 0.9554 0.4s Epoch 37/50 Train 39.7412 Val 0.9533 0.4s Epoch 38/50 Train 39.6735 Val 0.9513 0.4s Epoch 39/50 Train 39.6090 Val 0.9494 0.4s Epoch 40/50 Train 39.5475 Val 0.9476 0.4s Epoch 41/50 Train 39.4885 Val 0.9458 0.4s Epoch 42/50 Train 39.4319 Val 0.9441 0.4s Epoch 43/50 Train 39.3773 Val 0.9425 0.4s Epoch 44/50 Train 39.3245 Val 0.9409 0.4s Epoch 45/50 Train 39.2733 Val 0.9393 0.4s Epoch 46/50 Train 39.2237 Val 0.9378 0.4s Epoch 47/50 Train 39.1753 Val 0.9363 0.4s Epoch 48/50 Train 39.1281 Val 0.9349 0.4s Epoch 49/50 Train 39.0820 Val 0.9335 0.4s Epoch 50/50 Train 39.0369 Val 0.9321 0.4s Parameters used in the single-fit model: input_len: 60 output_len: 20 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.11378915 1 day(s) RMSE : 0.21566529 1 day(s) R2 : -0.01902035 1 day(s) Pearson r : 0.35257478 1 day(s) QLIKE : 0.47075334 3 day(s) MAE : 0.11293622 3 day(s) RMSE : 0.21400405 3 day(s) R2 : -0.01112297 3 day(s) Pearson r : 0.35171329 3 day(s) QLIKE : 0.46418141 5 day(s) MAE : 0.11310347 5 day(s) RMSE : 0.21374879 5 day(s) R2 : -0.02017480 5 day(s) Pearson r : 0.34083542 5 day(s) QLIKE : 0.47062534 10 day(s) MAE : 0.11369853 10 day(s) RMSE : 0.21565286 10 day(s) R2 : -0.04834358 10 day(s) Pearson r : 0.31625272 10 day(s) QLIKE : 0.47942926 20 day(s) MAE : 0.11481844 20 day(s) RMSE : 0.21794224 20 day(s) R2 : -0.08334841 20 day(s) Pearson r : 0.28339285 20 day(s) QLIKE : 0.48601505 full horizon MAE : 0.11481844 full horizon RMSE : 0.21794224 full horizon R2 : -0.08334841 full horizon Pearson r : 0.28339285 full horizon QLIKE : 0.48601505 --- Task 2 --- 1 day(s) MAE : 0.15577716 1 day(s) RMSE : 0.31169297 1 day(s) R2 : -0.33133193 1 day(s) Pearson r : -0.01799058 1 day(s) QLIKE : 15.93345827 3 day(s) MAE : 0.15459587 3 day(s) RMSE : 0.30976333 3 day(s) R2 : -0.33058466 3 day(s) Pearson r : 0.00610804 3 day(s) QLIKE : 19.19536312 5 day(s) MAE : 0.15409403 5 day(s) RMSE : 0.30893926 5 day(s) R2 : -0.33002148 5 day(s) Pearson r : 0.00308210 5 day(s) QLIKE : 18.67517869 10 day(s) MAE : 0.15356561 10 day(s) RMSE : 0.30853669 10 day(s) R2 : -0.33220252 10 day(s) Pearson r : 0.00831004 10 day(s) QLIKE : 18.27948040 20 day(s) MAE : 0.16129901 20 day(s) RMSE : 0.99332468 20 day(s) R2 : -12.82686556 20 day(s) Pearson r : -0.00479153 20 day(s) QLIKE : 17.81236997 full horizon MAE : 0.16129901 full horizon RMSE : 0.99332468 full horizon R2 : -12.82686556 full horizon Pearson r : -0.00479153 full horizon QLIKE : 17.81236997 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/EURUSD/ITransformer_H20.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.039297, max=1.67595 === GOLD | H=1 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 5534 Time steps for y: 1 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 17.638767766110508 Min value: -9.898129597066724 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.283234900000937 Min value: -5.250298730157774 Checking X_price_val: Shape: (443, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722397423 Min value: -6.160721883827869 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.7902797110229687 Min value: -2.1194734374756146 Checking X_price_test: Shape: (1107, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698434 Min value: -4.182160059445359 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5405778347636003 Min value: -2.3606736150182788 Epoch 1/50 Train 78.1283 Val 0.9661 0.8s Epoch 2/50 Train 74.8692 Val 0.9403 0.6s Epoch 3/50 Train 72.2289 Val 0.9202 0.6s Epoch 4/50 Train 70.1923 Val 0.9044 0.6s Epoch 5/50 Train 68.6266 Val 0.8917 0.6s Epoch 6/50 Train 67.4021 Val 0.8813 0.6s Epoch 7/50 Train 66.4230 Val 0.8726 0.6s Epoch 8/50 Train 65.6218 Val 0.8652 0.6s Epoch 9/50 Train 64.9510 Val 0.8589 0.6s Epoch 10/50 Train 64.3770 Val 0.8533 0.8s Epoch 11/50 Train 63.8757 Val 0.8484 0.7s Epoch 12/50 Train 63.4299 Val 0.8440 0.8s Epoch 13/50 Train 63.0272 Val 0.8400 0.7s Epoch 14/50 Train 62.6583 Val 0.8364 0.6s Epoch 15/50 Train 62.3163 Val 0.8330 0.6s Epoch 16/50 Train 61.9962 Val 0.8299 0.6s Epoch 17/50 Train 61.6938 Val 0.8270 0.6s Epoch 18/50 Train 61.4063 Val 0.8243 0.7s Epoch 19/50 Train 61.1312 Val 0.8218 0.6s Epoch 20/50 Train 60.8667 Val 0.8194 0.6s Epoch 21/50 Train 60.6112 Val 0.8172 0.6s Epoch 22/50 Train 60.3636 Val 0.8151 0.6s Epoch 23/50 Train 60.1228 Val 0.8131 0.6s Epoch 24/50 Train 59.8880 Val 0.8112 0.6s Epoch 25/50 Train 59.6585 Val 0.8094 0.6s Epoch 26/50 Train 59.4338 Val 0.8076 0.6s Epoch 27/50 Train 59.2134 Val 0.8060 0.6s Epoch 28/50 Train 58.9969 Val 0.8044 0.6s Epoch 29/50 Train 58.7839 Val 0.8028 0.6s Epoch 30/50 Train 58.5741 Val 0.8014 0.6s Epoch 31/50 Train 58.3673 Val 0.7999 0.6s Epoch 32/50 Train 58.1633 Val 0.7986 0.6s Epoch 33/50 Train 57.9618 Val 0.7973 0.6s Epoch 34/50 Train 57.7627 Val 0.7960 0.6s Epoch 35/50 Train 57.5659 Val 0.7947 0.6s Epoch 36/50 Train 57.3711 Val 0.7935 0.6s Epoch 37/50 Train 57.1784 Val 0.7924 0.6s Epoch 38/50 Train 56.9875 Val 0.7912 0.6s Epoch 39/50 Train 56.7985 Val 0.7901 0.6s Epoch 40/50 Train 56.6112 Val 0.7891 0.6s Epoch 41/50 Train 56.4256 Val 0.7880 0.6s Epoch 42/50 Train 56.2415 Val 0.7870 0.6s Epoch 43/50 Train 56.0591 Val 0.7861 0.6s Epoch 44/50 Train 55.8781 Val 0.7851 0.6s Epoch 45/50 Train 55.6987 Val 0.7842 0.6s Epoch 46/50 Train 55.5206 Val 0.7833 0.6s Epoch 47/50 Train 55.3441 Val 0.7824 0.6s Epoch 48/50 Train 55.1689 Val 0.7816 0.6s Epoch 49/50 Train 54.9951 Val 0.7807 0.6s Epoch 50/50 Train 54.8227 Val 0.7799 0.6s Parameters used in the single-fit model: input_len: 60 output_len: 1 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.46983714 1 day(s) RMSE : 0.75653242 1 day(s) R2 : -0.33276814 1 day(s) Pearson r : 0.31732394 1 day(s) QLIKE : 0.49017776 full horizon MAE : 0.46983714 full horizon RMSE : 0.75653242 full horizon R2 : -0.33276814 full horizon Pearson r : 0.31732394 full horizon QLIKE : 0.49017776 --- Task 2 --- 1 day(s) MAE : 0.32657440 1 day(s) RMSE : 0.61959390 1 day(s) R2 : -0.38332829 1 day(s) Pearson r : 0.02324505 1 day(s) QLIKE : 20.28863665 full horizon MAE : 0.32657440 full horizon RMSE : 0.61959390 full horizon R2 : -0.38332829 full horizon Pearson r : 0.02324505 full horizon QLIKE : 20.28863665 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GOLD/ITransformer_H1.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.145447, max=1.04788 === GOLD | H=5 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 5534 Time steps for y: 5 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 17.638767766110508 Min value: -9.898129597066724 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.2836777458630495 Min value: -5.250623802304656 Checking X_price_val: Shape: (443, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722397423 Min value: -6.160721883827869 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.790672308331636 Min value: -2.1194793742415055 Checking X_price_test: Shape: (1107, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698434 Min value: -4.182160059445359 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5409449791338365 Min value: -2.3607041381163207 Epoch 1/50 Train 82.4484 Val 0.9904 0.9s Epoch 2/50 Train 80.1410 Val 0.9707 0.6s Epoch 3/50 Train 77.9792 Val 0.9536 0.6s Epoch 4/50 Train 76.0676 Val 0.9390 0.6s Epoch 5/50 Train 74.4107 Val 0.9265 0.6s Epoch 6/50 Train 72.9787 Val 0.9156 0.6s Epoch 7/50 Train 71.7381 Val 0.9062 0.6s Epoch 8/50 Train 70.6588 Val 0.8978 0.6s Epoch 9/50 Train 69.7151 Val 0.8905 0.8s Epoch 10/50 Train 68.8855 Val 0.8840 0.8s Epoch 11/50 Train 68.1522 Val 0.8782 0.7s Epoch 12/50 Train 67.5004 Val 0.8730 0.8s Epoch 13/50 Train 66.9179 Val 0.8683 0.6s Epoch 14/50 Train 66.3943 Val 0.8640 0.6s Epoch 15/50 Train 65.9213 Val 0.8602 0.6s Epoch 16/50 Train 65.4917 Val 0.8567 0.6s Epoch 17/50 Train 65.0996 Val 0.8535 0.6s Epoch 18/50 Train 64.7399 Val 0.8506 0.6s Epoch 19/50 Train 64.4085 Val 0.8479 0.7s Epoch 20/50 Train 64.1016 Val 0.8454 0.6s Epoch 21/50 Train 63.8164 Val 0.8431 0.6s Epoch 22/50 Train 63.5501 Val 0.8410 0.6s Epoch 23/50 Train 63.3005 Val 0.8390 0.6s Epoch 24/50 Train 63.0658 Val 0.8372 0.6s Epoch 25/50 Train 62.8442 Val 0.8355 0.6s Epoch 26/50 Train 62.6343 Val 0.8339 0.6s Epoch 27/50 Train 62.4350 Val 0.8324 0.7s Epoch 28/50 Train 62.2450 Val 0.8310 0.6s Epoch 29/50 Train 62.0635 Val 0.8297 0.6s Epoch 30/50 Train 61.8897 Val 0.8285 0.6s Epoch 31/50 Train 61.7227 Val 0.8273 0.6s Epoch 32/50 Train 61.5620 Val 0.8262 0.7s Epoch 33/50 Train 61.4069 Val 0.8252 0.6s Epoch 34/50 Train 61.2570 Val 0.8242 0.7s Epoch 35/50 Train 61.1118 Val 0.8232 0.7s Epoch 36/50 Train 60.9709 Val 0.8223 0.7s Epoch 37/50 Train 60.8339 Val 0.8215 0.7s Epoch 38/50 Train 60.7006 Val 0.8207 0.7s Epoch 39/50 Train 60.5705 Val 0.8199 0.7s Epoch 40/50 Train 60.4435 Val 0.8191 0.7s Epoch 41/50 Train 60.3194 Val 0.8184 0.6s Epoch 42/50 Train 60.1978 Val 0.8177 0.7s Epoch 43/50 Train 60.0787 Val 0.8171 0.7s Epoch 44/50 Train 59.9618 Val 0.8164 0.6s Epoch 45/50 Train 59.8469 Val 0.8158 0.7s Epoch 46/50 Train 59.7340 Val 0.8152 0.7s Epoch 47/50 Train 59.6229 Val 0.8147 0.7s Epoch 48/50 Train 59.5134 Val 0.8141 0.7s Epoch 49/50 Train 59.4055 Val 0.8136 0.7s Epoch 50/50 Train 59.2990 Val 0.8131 0.6s Parameters used in the single-fit model: input_len: 60 output_len: 5 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.47903853 1 day(s) RMSE : 0.76553197 1 day(s) R2 : -0.36466540 1 day(s) Pearson r : 0.26455793 1 day(s) QLIKE : 0.50596971 3 day(s) MAE : 0.47875015 3 day(s) RMSE : 0.76667549 3 day(s) R2 : -0.36760247 3 day(s) Pearson r : 0.26202412 3 day(s) QLIKE : 0.50201074 5 day(s) MAE : 0.48050772 5 day(s) RMSE : 0.76915910 5 day(s) R2 : -0.37256129 5 day(s) Pearson r : 0.25031910 5 day(s) QLIKE : 0.50532984 full horizon MAE : 0.48050772 full horizon RMSE : 0.76915910 full horizon R2 : -0.37256129 full horizon Pearson r : 0.25031910 full horizon QLIKE : 0.50532984 --- Task 2 --- 1 day(s) MAE : 0.32668954 1 day(s) RMSE : 0.61977874 1 day(s) R2 : -0.38415377 1 day(s) Pearson r : -0.02965635 1 day(s) QLIKE : 18.53727944 3 day(s) MAE : 0.32657770 3 day(s) RMSE : 0.61967998 3 day(s) R2 : -0.38384500 3 day(s) Pearson r : 0.01159551 3 day(s) QLIKE : 18.70344641 5 day(s) MAE : 0.32786677 5 day(s) RMSE : 0.62225947 5 day(s) R2 : -0.38382550 5 day(s) Pearson r : 0.00988120 5 day(s) QLIKE : 18.31819188 full horizon MAE : 0.32786677 full horizon RMSE : 0.62225947 full horizon R2 : -0.38382550 full horizon Pearson r : 0.00988120 full horizon QLIKE : 18.31819188 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GOLD/ITransformer_H5.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.0897933, max=1.17833 === GOLD | H=10 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 5534 Time steps for y: 10 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 17.638767766110508 Min value: -9.898129597066724 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.285154362272003 Min value: -5.254244390683287 Checking X_price_val: Shape: (443, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722397423 Min value: -6.160721883827869 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.7918153902171274 Min value: -2.1209816396054144 Checking X_price_test: Shape: (1107, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698434 Min value: -4.182160059445359 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5419191122017313 Min value: -2.362369600030494 Epoch 1/50 Train 82.7117 Val 0.9736 0.8s Epoch 2/50 Train 80.9526 Val 0.9583 0.7s Epoch 3/50 Train 79.2458 Val 0.9446 0.6s Epoch 4/50 Train 77.6769 Val 0.9325 0.7s Epoch 5/50 Train 76.2639 Val 0.9219 0.7s Epoch 6/50 Train 74.9982 Val 0.9124 0.7s Epoch 7/50 Train 73.8655 Val 0.9041 0.6s Epoch 8/50 Train 72.8511 Val 0.8967 0.7s Epoch 9/50 Train 71.9410 Val 0.8900 0.9s Epoch 10/50 Train 71.1227 Val 0.8841 0.7s Epoch 11/50 Train 70.3850 Val 0.8787 0.8s Epoch 12/50 Train 69.7181 Val 0.8738 0.7s Epoch 13/50 Train 69.1135 Val 0.8695 0.6s Epoch 14/50 Train 68.5637 Val 0.8654 0.6s Epoch 15/50 Train 68.0621 Val 0.8618 0.6s Epoch 16/50 Train 67.6030 Val 0.8584 0.6s Epoch 17/50 Train 67.1817 Val 0.8553 0.6s Epoch 18/50 Train 66.7937 Val 0.8525 0.6s Epoch 19/50 Train 66.4353 Val 0.8498 0.6s Epoch 20/50 Train 66.1033 Val 0.8473 0.6s Epoch 21/50 Train 65.7949 Val 0.8450 0.7s Epoch 22/50 Train 65.5075 Val 0.8429 0.7s Epoch 23/50 Train 65.2391 Val 0.8409 0.6s Epoch 24/50 Train 64.9875 Val 0.8390 0.7s Epoch 25/50 Train 64.7513 Val 0.8373 0.7s Epoch 26/50 Train 64.5289 Val 0.8356 0.7s Epoch 27/50 Train 64.3189 Val 0.8341 0.7s Epoch 28/50 Train 64.1202 Val 0.8326 0.7s Epoch 29/50 Train 63.9318 Val 0.8313 0.7s Epoch 30/50 Train 63.7527 Val 0.8300 0.7s Epoch 31/50 Train 63.5821 Val 0.8288 0.7s Epoch 32/50 Train 63.4193 Val 0.8276 0.7s Epoch 33/50 Train 63.2635 Val 0.8265 0.7s Epoch 34/50 Train 63.1142 Val 0.8255 0.7s Epoch 35/50 Train 62.9708 Val 0.8246 0.6s Epoch 36/50 Train 62.8329 Val 0.8236 0.6s Epoch 37/50 Train 62.6999 Val 0.8228 0.7s Epoch 38/50 Train 62.5716 Val 0.8220 0.6s Epoch 39/50 Train 62.4474 Val 0.8212 0.7s Epoch 40/50 Train 62.3271 Val 0.8205 0.7s Epoch 41/50 Train 62.2103 Val 0.8198 0.7s Epoch 42/50 Train 62.0969 Val 0.8191 0.6s Epoch 43/50 Train 61.9864 Val 0.8185 0.7s Epoch 44/50 Train 61.8788 Val 0.8179 0.6s Epoch 45/50 Train 61.7737 Val 0.8173 0.6s Epoch 46/50 Train 61.6710 Val 0.8167 0.7s Epoch 47/50 Train 61.5706 Val 0.8162 0.7s Epoch 48/50 Train 61.4721 Val 0.8157 0.7s Epoch 49/50 Train 61.3755 Val 0.8152 0.7s Epoch 50/50 Train 61.2807 Val 0.8148 0.6s Parameters used in the single-fit model: input_len: 60 output_len: 10 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.48273984 1 day(s) RMSE : 0.76824331 1 day(s) R2 : -0.37434920 1 day(s) Pearson r : 0.23311930 1 day(s) QLIKE : 0.51466694 3 day(s) MAE : 0.48156643 3 day(s) RMSE : 0.76896014 3 day(s) R2 : -0.37576538 3 day(s) Pearson r : 0.24426101 3 day(s) QLIKE : 0.50747199 5 day(s) MAE : 0.48392906 5 day(s) RMSE : 0.77218507 5 day(s) R2 : -0.38338219 5 day(s) Pearson r : 0.23003641 5 day(s) QLIKE : 0.51119606 10 day(s) MAE : 0.48671746 10 day(s) RMSE : 0.77803664 10 day(s) R2 : -0.38876230 10 day(s) Pearson r : 0.20796057 10 day(s) QLIKE : 0.51971154 full horizon MAE : 0.48671746 full horizon RMSE : 0.77803664 full horizon R2 : -0.38876230 full horizon Pearson r : 0.20796057 full horizon QLIKE : 0.51971154 --- Task 2 --- 1 day(s) MAE : 0.32659310 1 day(s) RMSE : 0.61976655 1 day(s) R2 : -0.38409934 1 day(s) Pearson r : -0.02792015 1 day(s) QLIKE : 19.53967183 3 day(s) MAE : 0.32660520 3 day(s) RMSE : 0.61975809 3 day(s) R2 : -0.38419390 3 day(s) Pearson r : -0.00317298 3 day(s) QLIKE : 18.63991057 5 day(s) MAE : 0.32788277 5 day(s) RMSE : 0.62230439 5 day(s) R2 : -0.38402530 5 day(s) Pearson r : 0.00482389 5 day(s) QLIKE : 18.19305753 10 day(s) MAE : 0.32833387 10 day(s) RMSE : 0.62372469 10 day(s) R2 : -0.38297029 10 day(s) Pearson r : 0.02230478 10 day(s) QLIKE : 18.37036808 full horizon MAE : 0.32833387 full horizon RMSE : 0.62372469 full horizon R2 : -0.38297029 full horizon Pearson r : 0.02230478 full horizon QLIKE : 18.37036808 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GOLD/ITransformer_H10.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.0863574, max=1.02075 === GOLD | H=20 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 5534 Time steps for y: 20 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 17.638767766110508 Min value: -9.898129597066724 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.287022865369746 Min value: -5.257736685381158 Checking X_price_val: Shape: (443, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722397423 Min value: -6.160721883827869 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.7933331106383543 Min value: -2.122246065928174 Checking X_price_test: Shape: (1107, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698434 Min value: -4.182160059445359 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5432591469134986 Min value: -2.363805662326961 Epoch 1/50 Train 85.5811 Val 1.0068 0.7s Epoch 2/50 Train 84.0044 Val 0.9919 0.7s Epoch 3/50 Train 82.4401 Val 0.9782 0.6s Epoch 4/50 Train 80.9704 Val 0.9659 0.6s Epoch 5/50 Train 79.6161 Val 0.9548 0.6s Epoch 6/50 Train 78.3748 Val 0.9448 0.6s Epoch 7/50 Train 77.2387 Val 0.9358 0.6s Epoch 8/50 Train 76.1987 Val 0.9276 0.6s Epoch 9/50 Train 75.2457 Val 0.9202 0.8s Epoch 10/50 Train 74.3711 Val 0.9133 0.8s Epoch 11/50 Train 73.5674 Val 0.9071 0.7s Epoch 12/50 Train 72.8275 Val 0.9013 0.8s Epoch 13/50 Train 72.1453 Val 0.8959 0.6s Epoch 14/50 Train 71.5155 Val 0.8909 0.6s Epoch 15/50 Train 70.9332 Val 0.8862 0.7s Epoch 16/50 Train 70.3942 Val 0.8818 0.6s Epoch 17/50 Train 69.8948 Val 0.8777 0.6s Epoch 18/50 Train 69.4316 Val 0.8738 0.7s Epoch 19/50 Train 69.0018 Val 0.8701 0.7s Epoch 20/50 Train 68.6027 Val 0.8666 0.6s Epoch 21/50 Train 68.2319 Val 0.8633 0.6s Epoch 22/50 Train 67.8873 Val 0.8602 0.6s Epoch 23/50 Train 67.5670 Val 0.8573 0.7s Epoch 24/50 Train 67.2693 Val 0.8545 0.6s Epoch 25/50 Train 66.9925 Val 0.8518 0.6s Epoch 26/50 Train 66.7351 Val 0.8493 0.7s Epoch 27/50 Train 66.4958 Val 0.8470 0.6s Epoch 28/50 Train 66.2732 Val 0.8447 0.6s Epoch 29/50 Train 66.0662 Val 0.8426 0.6s Epoch 30/50 Train 65.8734 Val 0.8407 0.6s Epoch 31/50 Train 65.6939 Val 0.8388 0.7s Epoch 32/50 Train 65.5266 Val 0.8371 0.6s Epoch 33/50 Train 65.3704 Val 0.8355 0.7s Epoch 34/50 Train 65.2246 Val 0.8339 0.7s Epoch 35/50 Train 65.0881 Val 0.8325 0.7s Epoch 36/50 Train 64.9601 Val 0.8312 0.6s Epoch 37/50 Train 64.8399 Val 0.8300 0.6s Epoch 38/50 Train 64.7267 Val 0.8289 0.7s Epoch 39/50 Train 64.6198 Val 0.8278 0.6s Epoch 40/50 Train 64.5187 Val 0.8269 0.6s Epoch 41/50 Train 64.4228 Val 0.8260 0.7s Epoch 42/50 Train 64.3315 Val 0.8252 0.7s Epoch 43/50 Train 64.2444 Val 0.8244 0.7s Epoch 44/50 Train 64.1610 Val 0.8237 0.7s Epoch 45/50 Train 64.0809 Val 0.8231 0.7s Epoch 46/50 Train 64.0039 Val 0.8225 0.6s Epoch 47/50 Train 63.9295 Val 0.8220 0.7s Epoch 48/50 Train 63.8574 Val 0.8215 0.6s Epoch 49/50 Train 63.7875 Val 0.8210 0.7s Epoch 50/50 Train 63.7195 Val 0.8206 0.7s Parameters used in the single-fit model: input_len: 60 output_len: 20 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.48883442 1 day(s) RMSE : 0.77494206 1 day(s) R2 : -0.39842114 1 day(s) Pearson r : 0.18486377 1 day(s) QLIKE : 0.52188202 3 day(s) MAE : 0.48665862 3 day(s) RMSE : 0.77388099 3 day(s) R2 : -0.39342975 3 day(s) Pearson r : 0.22077202 3 day(s) QLIKE : 0.51202520 5 day(s) MAE : 0.48832853 5 day(s) RMSE : 0.77634676 5 day(s) R2 : -0.39833386 5 day(s) Pearson r : 0.20528554 5 day(s) QLIKE : 0.51617973 10 day(s) MAE : 0.49154218 10 day(s) RMSE : 0.78257928 10 day(s) R2 : -0.40502648 10 day(s) Pearson r : 0.17813990 10 day(s) QLIKE : 0.52541978 20 day(s) MAE : 0.49832608 20 day(s) RMSE : 0.79234265 20 day(s) R2 : -0.41904473 20 day(s) Pearson r : 0.12970564 20 day(s) QLIKE : 0.53874714 full horizon MAE : 0.49832608 full horizon RMSE : 0.79234265 full horizon R2 : -0.41904473 full horizon Pearson r : 0.12970564 full horizon QLIKE : 0.53874714 --- Task 2 --- 1 day(s) MAE : 0.32651673 1 day(s) RMSE : 0.61977992 1 day(s) R2 : -0.38415902 1 day(s) Pearson r : -0.01403945 1 day(s) QLIKE : 17.46685909 3 day(s) MAE : 0.32660005 3 day(s) RMSE : 0.61979392 3 day(s) R2 : -0.38435394 3 day(s) Pearson r : -0.00967951 3 day(s) QLIKE : 16.65282808 5 day(s) MAE : 0.32788889 5 day(s) RMSE : 0.62234032 5 day(s) R2 : -0.38418515 5 day(s) Pearson r : -0.00689891 5 day(s) QLIKE : 16.37985976 10 day(s) MAE : 0.32835496 10 day(s) RMSE : 0.62378425 10 day(s) R2 : -0.38323441 10 day(s) Pearson r : 0.00031534 10 day(s) QLIKE : 16.33060003 20 day(s) MAE : 0.32935175 20 day(s) RMSE : 0.62600993 20 day(s) R2 : -0.38266394 20 day(s) Pearson r : 0.01162598 20 day(s) QLIKE : 16.44055496 full horizon MAE : 0.32935175 full horizon RMSE : 0.62600993 full horizon R2 : -0.38266394 full horizon Pearson r : 0.01162598 full horizon QLIKE : 16.44055496 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/GOLD/ITransformer_H20.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.0802805, max=0.910797 === SP500 | H=1 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 3466 Time steps for y: 1 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790614 Min value: -11.560339011034328 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.5981030806998047 Min value: -18.186962127218408 Checking X_price_val: Shape: (277, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.583786604265051 Min value: -4.793129353735297 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3850009093345803 Min value: -2.4181466917899535 Checking X_price_test: Shape: (694, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.658768610066629 Min value: -6.142887457561203 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 1, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.3795163284544945 Min value: -3.2441546953028637 Epoch 1/50 Train 45.1144 Val 1.2804 0.5s Epoch 2/50 Train 43.7581 Val 1.2542 0.4s Epoch 3/50 Train 42.7944 Val 1.2323 0.4s Epoch 4/50 Train 41.9874 Val 1.2135 0.4s Epoch 5/50 Train 41.2992 Val 1.1972 0.4s Epoch 6/50 Train 40.7050 Val 1.1830 0.4s Epoch 7/50 Train 40.1870 Val 1.1706 0.4s Epoch 8/50 Train 39.7315 Val 1.1596 0.4s Epoch 9/50 Train 39.3282 Val 1.1500 0.4s Epoch 10/50 Train 38.9687 Val 1.1414 0.4s Epoch 11/50 Train 38.6463 Val 1.1339 0.4s Epoch 12/50 Train 38.3556 Val 1.1271 0.4s Epoch 13/50 Train 38.0921 Val 1.1212 0.4s Epoch 14/50 Train 37.8521 Val 1.1158 0.4s Epoch 15/50 Train 37.6325 Val 1.1111 0.4s Epoch 16/50 Train 37.4307 Val 1.1068 0.4s Epoch 17/50 Train 37.2446 Val 1.1030 0.4s Epoch 18/50 Train 37.0722 Val 1.0996 0.4s Epoch 19/50 Train 36.9120 Val 1.0966 0.4s Epoch 20/50 Train 36.7627 Val 1.0938 0.5s Epoch 21/50 Train 36.6229 Val 1.0913 0.5s Epoch 22/50 Train 36.4918 Val 1.0891 0.4s Epoch 23/50 Train 36.3684 Val 1.0871 0.5s Epoch 24/50 Train 36.2520 Val 1.0852 0.4s Epoch 25/50 Train 36.1419 Val 1.0836 0.5s Epoch 26/50 Train 36.0374 Val 1.0821 0.5s Epoch 27/50 Train 35.9382 Val 1.0807 0.4s Epoch 28/50 Train 35.8437 Val 1.0794 0.4s Epoch 29/50 Train 35.7535 Val 1.0783 0.4s Epoch 30/50 Train 35.6672 Val 1.0773 0.4s Epoch 31/50 Train 35.5845 Val 1.0763 0.4s Epoch 32/50 Train 35.5052 Val 1.0755 0.4s Epoch 33/50 Train 35.4290 Val 1.0747 0.4s Epoch 34/50 Train 35.3555 Val 1.0739 0.4s Epoch 35/50 Train 35.2847 Val 1.0733 0.4s Epoch 36/50 Train 35.2162 Val 1.0727 0.4s Epoch 37/50 Train 35.1500 Val 1.0721 0.4s Epoch 38/50 Train 35.0859 Val 1.0716 0.4s Epoch 39/50 Train 35.0236 Val 1.0711 0.4s Epoch 40/50 Train 34.9632 Val 1.0706 0.4s Epoch 41/50 Train 34.9043 Val 1.0702 0.4s Epoch 42/50 Train 34.8470 Val 1.0698 0.4s Epoch 43/50 Train 34.7910 Val 1.0695 0.4s Epoch 44/50 Train 34.7364 Val 1.0691 0.4s Epoch 45/50 Train 34.6830 Val 1.0688 0.4s Epoch 46/50 Train 34.6307 Val 1.0685 0.4s Epoch 47/50 Train 34.5794 Val 1.0682 0.4s Epoch 48/50 Train 34.5290 Val 1.0680 0.4s Epoch 49/50 Train 34.4796 Val 1.0677 0.4s Epoch 50/50 Train 34.4309 Val 1.0675 0.4s Parameters used in the single-fit model: input_len: 60 output_len: 1 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.61160013 1 day(s) RMSE : 2.65623362 1 day(s) R2 : 0.12007411 1 day(s) Pearson r : 0.38617106 1 day(s) QLIKE : 0.70871706 full horizon MAE : 0.61160013 full horizon RMSE : 2.65623362 full horizon R2 : 0.12007411 full horizon Pearson r : 0.38617106 full horizon QLIKE : 0.70871706 --- Task 2 --- 1 day(s) MAE : 0.36389051 1 day(s) RMSE : 1.47529254 1 day(s) R2 : -4.45668995 1 day(s) Pearson r : -0.01896530 1 day(s) QLIKE : 20.42307283 full horizon MAE : 0.36389051 full horizon RMSE : 1.47529254 full horizon R2 : -4.45668995 full horizon Pearson r : -0.01896530 full horizon QLIKE : 20.42307283 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/SP500/ITransformer_H1.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.250041, max=14.2538 === SP500 | H=5 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 3466 Time steps for y: 5 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790614 Min value: -11.560339011034328 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.5969074867518915 Min value: -18.18009964581938 Checking X_price_val: Shape: (277, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.583786604265051 Min value: -4.793129353735297 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.384254029613919 Min value: -2.417116935778614 Checking X_price_test: Shape: (694, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.658768610066629 Min value: -6.142887457561203 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 5, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.3784015875360596 Min value: -3.2428194072829686 Epoch 1/50 Train 47.6498 Val 1.3128 0.5s Epoch 2/50 Train 46.7132 Val 1.2950 0.4s Epoch 3/50 Train 45.9787 Val 1.2795 0.4s Epoch 4/50 Train 45.3150 Val 1.2656 0.4s Epoch 5/50 Train 44.7104 Val 1.2531 0.4s Epoch 6/50 Train 44.1572 Val 1.2417 0.4s Epoch 7/50 Train 43.6498 Val 1.2314 0.4s Epoch 8/50 Train 43.1831 Val 1.2220 0.4s Epoch 9/50 Train 42.7531 Val 1.2134 0.4s Epoch 10/50 Train 42.3562 Val 1.2055 0.4s Epoch 11/50 Train 41.9892 Val 1.1982 0.4s Epoch 12/50 Train 41.6493 Val 1.1915 0.4s Epoch 13/50 Train 41.3341 Val 1.1853 0.5s Epoch 14/50 Train 41.0413 Val 1.1796 0.5s Epoch 15/50 Train 40.7689 Val 1.1743 0.5s Epoch 16/50 Train 40.5151 Val 1.1694 0.5s Epoch 17/50 Train 40.2783 Val 1.1648 0.5s Epoch 18/50 Train 40.0569 Val 1.1605 0.5s Epoch 19/50 Train 39.8496 Val 1.1566 0.4s Epoch 20/50 Train 39.6553 Val 1.1529 0.4s Epoch 21/50 Train 39.4727 Val 1.1494 0.4s Epoch 22/50 Train 39.3010 Val 1.1461 0.4s Epoch 23/50 Train 39.1392 Val 1.1430 0.4s Epoch 24/50 Train 38.9864 Val 1.1402 0.4s Epoch 25/50 Train 38.8420 Val 1.1374 0.4s Epoch 26/50 Train 38.7053 Val 1.1349 0.4s Epoch 27/50 Train 38.5756 Val 1.1324 0.4s Epoch 28/50 Train 38.4524 Val 1.1301 0.4s Epoch 29/50 Train 38.3352 Val 1.1280 0.4s Epoch 30/50 Train 38.2235 Val 1.1259 0.4s Epoch 31/50 Train 38.1170 Val 1.1240 0.4s Epoch 32/50 Train 38.0152 Val 1.1221 0.4s Epoch 33/50 Train 37.9177 Val 1.1203 0.4s Epoch 34/50 Train 37.8244 Val 1.1186 0.4s Epoch 35/50 Train 37.7348 Val 1.1170 0.4s Epoch 36/50 Train 37.6488 Val 1.1155 0.4s Epoch 37/50 Train 37.5661 Val 1.1140 0.4s Epoch 38/50 Train 37.4864 Val 1.1127 0.4s Epoch 39/50 Train 37.4097 Val 1.1113 0.4s Epoch 40/50 Train 37.3356 Val 1.1101 0.4s Epoch 41/50 Train 37.2640 Val 1.1088 0.4s Epoch 42/50 Train 37.1948 Val 1.1077 0.4s Epoch 43/50 Train 37.1279 Val 1.1066 0.4s Epoch 44/50 Train 37.0630 Val 1.1055 0.4s Epoch 45/50 Train 37.0002 Val 1.1045 0.4s Epoch 46/50 Train 36.9392 Val 1.1035 0.4s Epoch 47/50 Train 36.8799 Val 1.1026 0.4s Epoch 48/50 Train 36.8223 Val 1.1017 0.4s Epoch 49/50 Train 36.7663 Val 1.1008 0.4s Epoch 50/50 Train 36.7118 Val 1.1000 0.4s Parameters used in the single-fit model: input_len: 60 output_len: 5 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.62554621 1 day(s) RMSE : 2.74048685 1 day(s) R2 : 0.06336798 1 day(s) Pearson r : 0.30744566 1 day(s) QLIKE : 0.73823164 3 day(s) MAE : 0.63422960 3 day(s) RMSE : 2.78937172 3 day(s) R2 : 0.02970153 3 day(s) Pearson r : 0.23340032 3 day(s) QLIKE : 0.77870776 5 day(s) MAE : 0.64390517 5 day(s) RMSE : 2.82267855 5 day(s) R2 : 0.00644816 5 day(s) Pearson r : 0.18132912 5 day(s) QLIKE : 0.82592934 full horizon MAE : 0.64390517 full horizon RMSE : 2.82267855 full horizon R2 : 0.00644816 full horizon Pearson r : 0.18132912 full horizon QLIKE : 0.82592934 --- Task 2 --- 1 day(s) MAE : 0.45966821 1 day(s) RMSE : 3.88614302 1 day(s) R2 : -36.86260990 1 day(s) Pearson r : -0.01892179 1 day(s) QLIKE : 21.05932861 3 day(s) MAE : 7.93635391 3 day(s) RMSE : 345.12490215 3 day(s) R2 : -297234.14250704 3 day(s) Pearson r : 0.01003929 3 day(s) QLIKE : 21.36841122 5 day(s) MAE : 4.88857797 5 day(s) RMSE : 267.33297503 5 day(s) R2 : -178167.28013541 5 day(s) Pearson r : 0.00776420 5 day(s) QLIKE : 21.32332631 full horizon MAE : 4.88857797 full horizon RMSE : 267.33297503 full horizon R2 : -178167.28013541 full horizon Pearson r : 0.00776420 full horizon QLIKE : 21.32332631 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/SP500/ITransformer_H5.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.241409, max=15.5996 === SP500 | H=10 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 3466 Time steps for y: 10 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790614 Min value: -11.560339011034328 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.5969471269414797 Min value: -18.183354534705547 Checking X_price_val: Shape: (277, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.583786604265051 Min value: -4.793129353735297 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3841102138286323 Min value: -2.4179871257691663 Checking X_price_test: Shape: (694, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.658768610066629 Min value: -6.142887457561203 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 10, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.378408171115352 Min value: -3.2438145134653436 Epoch 1/50 Train 48.2602 Val 1.3476 0.5s Epoch 2/50 Train 47.5359 Val 1.3326 0.4s Epoch 3/50 Train 46.9548 Val 1.3192 0.4s Epoch 4/50 Train 46.4170 Val 1.3068 0.4s Epoch 5/50 Train 45.9158 Val 1.2954 0.4s Epoch 6/50 Train 45.4473 Val 1.2848 0.4s Epoch 7/50 Train 45.0086 Val 1.2750 0.5s Epoch 8/50 Train 44.5970 Val 1.2657 0.4s Epoch 9/50 Train 44.2104 Val 1.2571 0.4s Epoch 10/50 Train 43.8468 Val 1.2490 0.5s Epoch 11/50 Train 43.5047 Val 1.2414 0.4s Epoch 12/50 Train 43.1823 Val 1.2342 0.4s Epoch 13/50 Train 42.8784 Val 1.2274 0.5s Epoch 14/50 Train 42.5916 Val 1.2211 0.5s Epoch 15/50 Train 42.3207 Val 1.2150 0.4s Epoch 16/50 Train 42.0647 Val 1.2093 0.5s Epoch 17/50 Train 41.8226 Val 1.2039 0.5s Epoch 18/50 Train 41.5934 Val 1.1988 0.5s Epoch 19/50 Train 41.3763 Val 1.1940 0.4s Epoch 20/50 Train 41.1705 Val 1.1894 0.4s Epoch 21/50 Train 40.9752 Val 1.1850 0.4s Epoch 22/50 Train 40.7899 Val 1.1808 0.4s Epoch 23/50 Train 40.6137 Val 1.1768 0.4s Epoch 24/50 Train 40.4463 Val 1.1730 0.4s Epoch 25/50 Train 40.2869 Val 1.1694 0.4s Epoch 26/50 Train 40.1351 Val 1.1660 0.4s Epoch 27/50 Train 39.9904 Val 1.1627 0.4s Epoch 28/50 Train 39.8524 Val 1.1595 0.4s Epoch 29/50 Train 39.7206 Val 1.1565 0.4s Epoch 30/50 Train 39.5947 Val 1.1536 0.4s Epoch 31/50 Train 39.4743 Val 1.1508 0.4s Epoch 32/50 Train 39.3591 Val 1.1482 0.4s Epoch 33/50 Train 39.2487 Val 1.1456 0.4s Epoch 34/50 Train 39.1429 Val 1.1432 0.4s Epoch 35/50 Train 39.0414 Val 1.1408 0.4s Epoch 36/50 Train 38.9440 Val 1.1386 0.4s Epoch 37/50 Train 38.8504 Val 1.1364 0.4s Epoch 38/50 Train 38.7603 Val 1.1343 0.4s Epoch 39/50 Train 38.6737 Val 1.1323 0.4s Epoch 40/50 Train 38.5903 Val 1.1304 0.4s Epoch 41/50 Train 38.5098 Val 1.1286 0.4s Epoch 42/50 Train 38.4323 Val 1.1268 0.4s Epoch 43/50 Train 38.3574 Val 1.1251 0.4s Epoch 44/50 Train 38.2851 Val 1.1234 0.4s Epoch 45/50 Train 38.2152 Val 1.1218 0.4s Epoch 46/50 Train 38.1476 Val 1.1203 0.4s Epoch 47/50 Train 38.0821 Val 1.1189 0.4s Epoch 48/50 Train 38.0187 Val 1.1174 0.4s Epoch 49/50 Train 37.9573 Val 1.1161 0.4s Epoch 50/50 Train 37.8977 Val 1.1148 0.4s Parameters used in the single-fit model: input_len: 60 output_len: 10 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.62029807 1 day(s) RMSE : 2.74175011 1 day(s) R2 : 0.06250428 1 day(s) Pearson r : 0.31278666 1 day(s) QLIKE : 0.74311908 3 day(s) MAE : 0.63595289 3 day(s) RMSE : 2.80776236 3 day(s) R2 : 0.01686478 3 day(s) Pearson r : 0.20567995 3 day(s) QLIKE : 0.79316359 5 day(s) MAE : 0.64571023 5 day(s) RMSE : 2.83464102 5 day(s) R2 : -0.00199100 5 day(s) Pearson r : 0.15966235 5 day(s) QLIKE : 0.84332178 10 day(s) MAE : 0.65503985 10 day(s) RMSE : 2.85955321 10 day(s) R2 : -0.01954228 10 day(s) Pearson r : 0.11230385 10 day(s) QLIKE : 0.89542495 full horizon MAE : 0.65503985 full horizon RMSE : 2.85955321 full horizon R2 : -0.01954228 full horizon Pearson r : 0.11230385 full horizon QLIKE : 0.89542495 --- Task 2 --- 1 day(s) MAE : 0.32060680 1 day(s) RMSE : 0.72309081 1 day(s) R2 : -0.31086713 1 day(s) Pearson r : -0.01887645 1 day(s) QLIKE : 22.73184950 3 day(s) MAE : 82.95266698 3 day(s) RMSE : 3764.83106388 3 day(s) R2 : -35370283.15071109 3 day(s) Pearson r : 0.01010470 3 day(s) QLIKE : 22.29449948 5 day(s) MAE : 49.89843438 5 day(s) RMSE : 2916.22563671 5 day(s) R2 : -21201547.02119072 5 day(s) Pearson r : 0.00781455 5 day(s) QLIKE : 21.95571357 10 day(s) MAE : 25.13548457 10 day(s) RMSE : 2062.08422510 10 day(s) R2 : -10600244.92215646 10 day(s) Pearson r : 0.00554911 10 day(s) QLIKE : 21.31745771 full horizon MAE : 25.13548457 full horizon RMSE : 2062.08422510 full horizon R2 : -10600244.92215646 full horizon Pearson r : 0.00554911 full horizon QLIKE : 21.31745771 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/SP500/ITransformer_H10.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.214759, max=15.9192 === SP500 | H=20 | ITransformer (simple fit) | no_tasks=2 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True [ITransformerWrapper] Using device: mps Batch size for y: 3466 Time steps for y: 20 Features for y: 2 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790614 Min value: -11.560339011034328 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.5963112311058953 Min value: -18.187442810157968 Checking X_price_val: Shape: (277, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.583786604265051 Min value: -4.793129353735297 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3832820721048864 Min value: -2.4468344131842463 Checking X_price_test: Shape: (694, 60, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.658768610066629 Min value: -6.142887457561203 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 20, 2) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.3777376348304156 Min value: -3.2455347334034057 Epoch 1/50 Train 49.9810 Val 1.4030 0.5s Epoch 2/50 Train 49.3372 Val 1.3887 0.4s Epoch 3/50 Train 48.8166 Val 1.3757 0.4s Epoch 4/50 Train 48.3292 Val 1.3635 0.4s Epoch 5/50 Train 47.8697 Val 1.3521 0.4s Epoch 6/50 Train 47.4350 Val 1.3414 0.4s Epoch 7/50 Train 47.0229 Val 1.3312 0.4s Epoch 8/50 Train 46.6315 Val 1.3216 0.4s Epoch 9/50 Train 46.2592 Val 1.3125 0.4s Epoch 10/50 Train 45.9047 Val 1.3039 0.4s Epoch 11/50 Train 45.5667 Val 1.2956 0.4s Epoch 12/50 Train 45.2441 Val 1.2878 0.4s Epoch 13/50 Train 44.9358 Val 1.2802 0.4s Epoch 14/50 Train 44.6410 Val 1.2730 0.5s Epoch 15/50 Train 44.3589 Val 1.2661 0.4s Epoch 16/50 Train 44.0885 Val 1.2595 0.4s Epoch 17/50 Train 43.8294 Val 1.2531 0.5s Epoch 18/50 Train 43.5809 Val 1.2470 0.5s Epoch 19/50 Train 43.3423 Val 1.2411 0.4s Epoch 20/50 Train 43.1132 Val 1.2354 0.4s Epoch 21/50 Train 42.8931 Val 1.2299 0.4s Epoch 22/50 Train 42.6816 Val 1.2245 0.4s Epoch 23/50 Train 42.4782 Val 1.2194 0.4s Epoch 24/50 Train 42.2827 Val 1.2144 0.4s Epoch 25/50 Train 42.0947 Val 1.2096 0.4s Epoch 26/50 Train 41.9139 Val 1.2049 0.4s Epoch 27/50 Train 41.7400 Val 1.2003 0.4s Epoch 28/50 Train 41.5728 Val 1.1959 0.4s Epoch 29/50 Train 41.4120 Val 1.1917 0.4s Epoch 30/50 Train 41.2574 Val 1.1875 0.4s Epoch 31/50 Train 41.1089 Val 1.1835 0.4s Epoch 32/50 Train 40.9661 Val 1.1796 0.4s Epoch 33/50 Train 40.8290 Val 1.1758 0.4s Epoch 34/50 Train 40.6974 Val 1.1722 0.4s Epoch 35/50 Train 40.5710 Val 1.1686 0.4s Epoch 36/50 Train 40.4498 Val 1.1652 0.4s Epoch 37/50 Train 40.3335 Val 1.1618 0.4s Epoch 38/50 Train 40.2221 Val 1.1586 0.4s Epoch 39/50 Train 40.1153 Val 1.1555 0.4s Epoch 40/50 Train 40.0131 Val 1.1524 0.4s Epoch 41/50 Train 39.9152 Val 1.1495 0.4s Epoch 42/50 Train 39.8215 Val 1.1467 0.4s Epoch 43/50 Train 39.7320 Val 1.1440 0.4s Epoch 44/50 Train 39.6463 Val 1.1414 0.4s Epoch 45/50 Train 39.5644 Val 1.1388 0.4s Epoch 46/50 Train 39.4861 Val 1.1364 0.4s Epoch 47/50 Train 39.4113 Val 1.1341 0.4s Epoch 48/50 Train 39.3398 Val 1.1318 0.4s Epoch 49/50 Train 39.2714 Val 1.1297 0.4s Epoch 50/50 Train 39.2061 Val 1.1276 0.4s Parameters used in the single-fit model: input_len: 60 output_len: 20 input_dim: 2 output_dim: 2 no_tasks: 2 device: mps epochs: 50 batch_size: 64 verbose: True lr: 0.00001000 checkpoint_path: None dropout: 0.00000000 l2_weight: 0.00001000 patience: 10 min_epochs: 30 min_delta: 0.00010000 d_model: 64 d_ff: 256 n_heads: 4 e_layers: 4 attn: prob activation: gelu output_attention: False distil: True embed: timeF freq: d features: MS moving_avg: 25 factor: 3 task_name: long_term_forecast hidden_dim: None hidden_layers: None loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.62293285 1 day(s) RMSE : 2.78140908 1 day(s) R2 : 0.03518668 1 day(s) Pearson r : 0.31395607 1 day(s) QLIKE : 0.77024815 3 day(s) MAE : 0.64255838 3 day(s) RMSE : 2.85562896 3 day(s) R2 : -0.01694184 3 day(s) Pearson r : 0.13443186 3 day(s) QLIKE : 0.81615630 5 day(s) MAE : 0.64870067 5 day(s) RMSE : 2.86412968 5 day(s) R2 : -0.02294678 5 day(s) Pearson r : 0.11027048 5 day(s) QLIKE : 0.86353055 10 day(s) MAE : 0.65201994 10 day(s) RMSE : 2.86880321 10 day(s) R2 : -0.02614891 10 day(s) Pearson r : 0.08861676 10 day(s) QLIKE : 0.90433764 20 day(s) MAE : 0.66397599 20 day(s) RMSE : 2.89140316 20 day(s) R2 : -0.04225660 20 day(s) Pearson r : 0.05447117 20 day(s) QLIKE : 0.92956264 full horizon MAE : 0.66397599 full horizon RMSE : 2.89140316 full horizon R2 : -0.04225660 full horizon Pearson r : 0.05447117 full horizon QLIKE : 0.92956264 --- Task 2 --- 1 day(s) MAE : 0.31459947 1 day(s) RMSE : 0.70555513 1 day(s) R2 : -0.24805836 1 day(s) Pearson r : 0.03161115 1 day(s) QLIKE : 21.17692774 3 day(s) MAE : 111.47469893 3 day(s) RMSE : 4763.35197436 3 day(s) R2 : -56620395.92976415 3 day(s) Pearson r : 0.00936466 3 day(s) QLIKE : 21.21323920 5 day(s) MAE : 67.03270204 5 day(s) RMSE : 3689.67673836 5 day(s) R2 : -33939227.14359390 5 day(s) Pearson r : 0.00725068 5 day(s) QLIKE : 21.20319951 10 day(s) MAE : 33.67479180 10 day(s) RMSE : 2608.99549019 10 day(s) R2 : -16968746.91207429 10 day(s) Pearson r : 0.00512689 10 day(s) QLIKE : 20.49381617 20 day(s) MAE : 16.99865950 20 day(s) RMSE : 1844.83847213 20 day(s) R2 : -8442555.01501749 20 day(s) Pearson r : 0.00357271 20 day(s) QLIKE : 20.82399532 full horizon MAE : 16.99865950 full horizon RMSE : 1844.83847213 full horizon R2 : -8442555.01501749 full horizon Pearson r : 0.00357271 full horizon QLIKE : 20.82399532 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/Saved_objects/final_results_saved_object/SP500/ITransformer_H20.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.0510465, max=22.705
In [153]:
final_tickers = ["AAPL", "MSFT", "GE", "BAC", "C", "BTCUSDT", "EURUSD", "GOLD", "SP500"]
final_models = ["Simple_MLP", "Simple_KAN", "Simple_LSTM", "ITransformer"]
final_horizons = [1, 5, 10, 20]
all_results = run_garch_over_dict(
structured_data_dict=structured_data_dict_10,
tickers=final_tickers,
horizons=final_horizons,
results_dict=final_results_store,
anchor_step=59,
anchor_feature_idx=1,
y_feature_idx=1,
split_ratio=0.8,
roll_window=500,
mean_mode="Zero",
candidates=[("GARCH", 1, 1, "t")],
sim_paths=2000,
metric_horizons=(1, 5, 10, 20,-1),
verbose=True,
save_dir=None
)
[GARCH] AAPL — H=1 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=1, roll_window=500 anchor 1613/2017: sd=1.87362, h1_pred=2.38816 anchor 1694/2017: sd=1.83036, h1_pred=0.971728 anchor 1775/2017: sd=1.75437, h1_pred=1.87798 anchor 1856/2017: sd=1.63043, h1_pred=2.47048 anchor 1937/2017: sd=1.36801, h1_pred=1.3947 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.244934 RMSE : 3.204341 R2 : -2.334505 Pearson r : 0.037045 QLIKE : 7.146105 full horizon MAE : 2.244934 RMSE : 3.204341 R2 : -2.334505 Pearson r : 0.037045 QLIKE : 7.146105 [GARCH] AAPL — H=5 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=5, roll_window=500 anchor 1613/2017: sd=1.87362, h1_pred=2.38816 anchor 1694/2017: sd=1.83036, h1_pred=0.971728 anchor 1775/2017: sd=1.75437, h1_pred=1.87798 anchor 1856/2017: sd=1.63043, h1_pred=2.47048 anchor 1937/2017: sd=1.36801, h1_pred=1.3947 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.244934 RMSE : 3.204341 R2 : -2.334505 Pearson r : 0.037045 QLIKE : 7.146105 5 day(s) MAE : 2.255908 RMSE : 3.162423 R2 : -2.228654 Pearson r : 0.050060 QLIKE : 7.189652 full horizon MAE : 2.255908 RMSE : 3.162423 R2 : -2.228654 Pearson r : 0.050060 QLIKE : 7.189652 [GARCH] AAPL — H=10 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=10, roll_window=500 anchor 1613/2017: sd=1.87362, h1_pred=2.38816 anchor 1694/2017: sd=1.83036, h1_pred=0.971728 anchor 1775/2017: sd=1.75437, h1_pred=1.87798 anchor 1856/2017: sd=1.63043, h1_pred=2.47048 anchor 1937/2017: sd=1.36801, h1_pred=1.3947 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.244934 RMSE : 3.204341 R2 : -2.334505 Pearson r : 0.037045 QLIKE : 7.146105 5 day(s) MAE : 2.255908 RMSE : 3.162423 R2 : -2.228654 Pearson r : 0.050060 QLIKE : 7.189652 10 day(s) MAE : 2.259492 RMSE : 3.123894 R2 : -2.140761 Pearson r : 0.053776 QLIKE : 7.200451 full horizon MAE : 2.259492 RMSE : 3.123894 R2 : -2.140761 Pearson r : 0.053776 QLIKE : 7.200451 [GARCH] AAPL — H=20 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=20, roll_window=500 anchor 1613/2017: sd=1.87362, h1_pred=2.38816 anchor 1694/2017: sd=1.83036, h1_pred=0.971728 anchor 1775/2017: sd=1.75437, h1_pred=1.87798 anchor 1856/2017: sd=1.63043, h1_pred=2.47048 anchor 1937/2017: sd=1.36801, h1_pred=1.3947 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.244934 RMSE : 3.204341 R2 : -2.334505 Pearson r : 0.037045 QLIKE : 7.146105 5 day(s) MAE : 2.255908 RMSE : 3.162423 R2 : -2.228654 Pearson r : 0.050060 QLIKE : 7.189652 10 day(s) MAE : 2.259492 RMSE : 3.123894 R2 : -2.140761 Pearson r : 0.053776 QLIKE : 7.200451 20 day(s) MAE : 2.264942 RMSE : 3.099011 R2 : -2.072988 Pearson r : 0.022402 QLIKE : 7.186950 full horizon MAE : 2.264942 RMSE : 3.099011 R2 : -2.072988 Pearson r : 0.022402 QLIKE : 7.186950 [GARCH] MSFT — H=1 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=1, roll_window=500 anchor 1613/2017: sd=1.94741, h1_pred=2.76349 anchor 1694/2017: sd=1.9336, h1_pred=1.52029 anchor 1775/2017: sd=1.80593, h1_pred=1.26253 anchor 1856/2017: sd=1.65404, h1_pred=1.57182 anchor 1937/2017: sd=1.42743, h1_pred=1.33005 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 1.786189 RMSE : 2.192145 R2 : -1.374047 Pearson r : 0.062285 QLIKE : 7.146787 full horizon MAE : 1.786189 RMSE : 2.192145 R2 : -1.374047 Pearson r : 0.062285 QLIKE : 7.146787 [GARCH] MSFT — H=5 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=5, roll_window=500 anchor 1613/2017: sd=1.94741, h1_pred=2.76349 anchor 1694/2017: sd=1.9336, h1_pred=1.52029 anchor 1775/2017: sd=1.80593, h1_pred=1.26253 anchor 1856/2017: sd=1.65404, h1_pred=1.57182 anchor 1937/2017: sd=1.42743, h1_pred=1.33005 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 1.786189 RMSE : 2.192145 R2 : -1.374047 Pearson r : 0.062285 QLIKE : 7.146787 5 day(s) MAE : 1.798916 RMSE : 2.209159 R2 : -1.395942 Pearson r : 0.050124 QLIKE : 7.217582 full horizon MAE : 1.798916 RMSE : 2.209159 R2 : -1.395942 Pearson r : 0.050124 QLIKE : 7.217582 [GARCH] MSFT — H=10 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=10, roll_window=500 anchor 1613/2017: sd=1.94741, h1_pred=2.76349 anchor 1694/2017: sd=1.9336, h1_pred=1.52029 anchor 1775/2017: sd=1.80593, h1_pred=1.26253 anchor 1856/2017: sd=1.65404, h1_pred=1.57182 anchor 1937/2017: sd=1.42743, h1_pred=1.33005 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 1.786189 RMSE : 2.192145 R2 : -1.374047 Pearson r : 0.062285 QLIKE : 7.146787 5 day(s) MAE : 1.798916 RMSE : 2.209159 R2 : -1.395942 Pearson r : 0.050124 QLIKE : 7.217582 10 day(s) MAE : 1.807977 RMSE : 2.225826 R2 : -1.402882 Pearson r : 0.041701 QLIKE : 7.243873 full horizon MAE : 1.807977 RMSE : 2.225826 R2 : -1.402882 Pearson r : 0.041701 QLIKE : 7.243873 [GARCH] MSFT — H=20 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=20, roll_window=500 anchor 1613/2017: sd=1.94741, h1_pred=2.76349 anchor 1694/2017: sd=1.9336, h1_pred=1.52029 anchor 1775/2017: sd=1.80593, h1_pred=1.26253 anchor 1856/2017: sd=1.65404, h1_pred=1.57182 anchor 1937/2017: sd=1.42743, h1_pred=1.33005 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 1.786189 RMSE : 2.192145 R2 : -1.374047 Pearson r : 0.062285 QLIKE : 7.146787 5 day(s) MAE : 1.798916 RMSE : 2.209159 R2 : -1.395942 Pearson r : 0.050124 QLIKE : 7.217582 10 day(s) MAE : 1.807977 RMSE : 2.225826 R2 : -1.402882 Pearson r : 0.041701 QLIKE : 7.243873 20 day(s) MAE : 1.810601 RMSE : 2.235479 R2 : -1.358412 Pearson r : 0.048946 QLIKE : 7.203543 full horizon MAE : 1.810601 RMSE : 2.235479 R2 : -1.358412 Pearson r : 0.048946 QLIKE : 7.203543 [GARCH] GE — H=1 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=1, roll_window=500 anchor 1613/2017: sd=2.11616, h1_pred=3.57706 anchor 1694/2017: sd=2.07402, h1_pred=3.26143 anchor 1775/2017: sd=2.28575, h1_pred=6.23968 anchor 1856/2017: sd=2.22776, h1_pred=3.23082 anchor 1937/2017: sd=2.22359, h1_pred=3.6585 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 3.633551 RMSE : 4.848706 R2 : -3.263633 Pearson r : 0.004758 QLIKE : 7.128652 full horizon MAE : 3.633551 RMSE : 4.848706 R2 : -3.263633 Pearson r : 0.004758 QLIKE : 7.128652 [GARCH] GE — H=5 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=5, roll_window=500 anchor 1613/2017: sd=2.11616, h1_pred=3.57706 anchor 1694/2017: sd=2.07402, h1_pred=3.26143 anchor 1775/2017: sd=2.28575, h1_pred=6.23968 anchor 1856/2017: sd=2.22776, h1_pred=3.23082 anchor 1937/2017: sd=2.22359, h1_pred=3.6585 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 3.633551 RMSE : 4.848706 R2 : -3.263633 Pearson r : 0.004758 QLIKE : 7.128652 5 day(s) MAE : 3.566507 RMSE : 4.593790 R2 : -2.819007 Pearson r : 0.031238 QLIKE : 7.134824 full horizon MAE : 3.566507 RMSE : 4.593790 R2 : -2.819007 Pearson r : 0.031238 QLIKE : 7.134824 [GARCH] GE — H=10 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=10, roll_window=500 anchor 1613/2017: sd=2.11616, h1_pred=3.57706 anchor 1694/2017: sd=2.07402, h1_pred=3.26143 anchor 1775/2017: sd=2.28575, h1_pred=6.23968 anchor 1856/2017: sd=2.22776, h1_pred=3.23082 anchor 1937/2017: sd=2.22359, h1_pred=3.6585 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 3.633551 RMSE : 4.848706 R2 : -3.263633 Pearson r : 0.004758 QLIKE : 7.128652 5 day(s) MAE : 3.566507 RMSE : 4.593790 R2 : -2.819007 Pearson r : 0.031238 QLIKE : 7.134824 10 day(s) MAE : 3.535873 RMSE : 4.478415 R2 : -2.602853 Pearson r : 0.014892 QLIKE : 7.128936 full horizon MAE : 3.535873 RMSE : 4.478415 R2 : -2.602853 Pearson r : 0.014892 QLIKE : 7.128936 [GARCH] GE — H=20 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=20, roll_window=500 anchor 1613/2017: sd=2.11616, h1_pred=3.57706 anchor 1694/2017: sd=2.07402, h1_pred=3.26143 anchor 1775/2017: sd=2.28575, h1_pred=6.23968 anchor 1856/2017: sd=2.22776, h1_pred=3.23082 anchor 1937/2017: sd=2.22359, h1_pred=3.6585 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 3.633551 RMSE : 4.848706 R2 : -3.263633 Pearson r : 0.004758 QLIKE : 7.128652 5 day(s) MAE : 3.566507 RMSE : 4.593790 R2 : -2.819007 Pearson r : 0.031238 QLIKE : 7.134824 10 day(s) MAE : 3.535873 RMSE : 4.478415 R2 : -2.602853 Pearson r : 0.014892 QLIKE : 7.128936 20 day(s) MAE : 3.492241 RMSE : 4.346271 R2 : -2.377670 Pearson r : 0.002262 QLIKE : 7.074895 full horizon MAE : 3.492241 RMSE : 4.346271 R2 : -2.377670 Pearson r : 0.002262 QLIKE : 7.074895 [GARCH] BAC — H=1 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=1, roll_window=500 anchor 1613/2017: sd=1.85719, h1_pred=2.64216 anchor 1694/2017: sd=1.87482, h1_pred=2.98279 anchor 1775/2017: sd=1.7586, h1_pred=3.65846 anchor 1856/2017: sd=1.65528, h1_pred=2.10081 anchor 1937/2017: sd=1.55539, h1_pred=2.13467 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.870229 RMSE : 3.402386 R2 : -3.005962 Pearson r : -0.032652 QLIKE : 7.860950 full horizon MAE : 2.870229 RMSE : 3.402386 R2 : -3.005962 Pearson r : -0.032652 QLIKE : 7.860950 [GARCH] BAC — H=5 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=5, roll_window=500 anchor 1613/2017: sd=1.85719, h1_pred=2.64216 anchor 1694/2017: sd=1.87482, h1_pred=2.98279 anchor 1775/2017: sd=1.7586, h1_pred=3.65846 anchor 1856/2017: sd=1.65528, h1_pred=2.10081 anchor 1937/2017: sd=1.55539, h1_pred=2.13467 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.870229 RMSE : 3.402386 R2 : -3.005962 Pearson r : -0.032652 QLIKE : 7.860950 5 day(s) MAE : 2.931345 RMSE : 3.338518 R2 : -2.828464 Pearson r : 0.046543 QLIKE : 7.818094 full horizon MAE : 2.931345 RMSE : 3.338518 R2 : -2.828464 Pearson r : 0.046543 QLIKE : 7.818094 [GARCH] BAC — H=10 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=10, roll_window=500 anchor 1613/2017: sd=1.85719, h1_pred=2.64216 anchor 1694/2017: sd=1.87482, h1_pred=2.98279 anchor 1775/2017: sd=1.7586, h1_pred=3.65846 anchor 1856/2017: sd=1.65528, h1_pred=2.10081 anchor 1937/2017: sd=1.55539, h1_pred=2.13467 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.870229 RMSE : 3.402386 R2 : -3.005962 Pearson r : -0.032652 QLIKE : 7.860950 5 day(s) MAE : 2.931345 RMSE : 3.338518 R2 : -2.828464 Pearson r : 0.046543 QLIKE : 7.818094 10 day(s) MAE : 2.978968 RMSE : 3.359030 R2 : -2.847093 Pearson r : 0.063185 QLIKE : 7.802285 full horizon MAE : 2.978968 RMSE : 3.359030 R2 : -2.847093 Pearson r : 0.063185 QLIKE : 7.802285 [GARCH] BAC — H=20 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=20, roll_window=500 anchor 1613/2017: sd=1.85719, h1_pred=2.64216 anchor 1694/2017: sd=1.87482, h1_pred=2.98279 anchor 1775/2017: sd=1.7586, h1_pred=3.65846 anchor 1856/2017: sd=1.65528, h1_pred=2.10081 anchor 1937/2017: sd=1.55539, h1_pred=2.13467 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.870229 RMSE : 3.402386 R2 : -3.005962 Pearson r : -0.032652 QLIKE : 7.860950 5 day(s) MAE : 2.931345 RMSE : 3.338518 R2 : -2.828464 Pearson r : 0.046543 QLIKE : 7.818094 10 day(s) MAE : 2.978968 RMSE : 3.359030 R2 : -2.847093 Pearson r : 0.063185 QLIKE : 7.802285 20 day(s) MAE : 3.009191 RMSE : 3.376419 R2 : -2.870120 Pearson r : 0.066700 QLIKE : 7.729510 full horizon MAE : 3.009191 RMSE : 3.376419 R2 : -2.870120 Pearson r : 0.066700 QLIKE : 7.729510 [GARCH] C — H=1 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=1, roll_window=500 anchor 1613/2017: sd=1.85564, h1_pred=2.64944 anchor 1694/2017: sd=1.85929, h1_pred=2.85303 anchor 1775/2017: sd=1.81797, h1_pred=2.15036 anchor 1856/2017: sd=1.65736, h1_pred=2.84016 anchor 1937/2017: sd=1.62662, h1_pred=2.14779 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.924805 RMSE : 3.507115 R2 : -2.456264 Pearson r : -0.071509 QLIKE : 7.395842 full horizon MAE : 2.924805 RMSE : 3.507115 R2 : -2.456264 Pearson r : -0.071509 QLIKE : 7.395842 [GARCH] C — H=5 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=5, roll_window=500 anchor 1613/2017: sd=1.85564, h1_pred=2.64944 anchor 1694/2017: sd=1.85929, h1_pred=2.85303 anchor 1775/2017: sd=1.81797, h1_pred=2.15036 anchor 1856/2017: sd=1.65736, h1_pred=2.84016 anchor 1937/2017: sd=1.62662, h1_pred=2.14779 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.924805 RMSE : 3.507115 R2 : -2.456264 Pearson r : -0.071509 QLIKE : 7.395842 5 day(s) MAE : 2.915682 RMSE : 3.423420 R2 : -2.266680 Pearson r : 0.006983 QLIKE : 7.370242 full horizon MAE : 2.915682 RMSE : 3.423420 R2 : -2.266680 Pearson r : 0.006983 QLIKE : 7.370242 [GARCH] C — H=10 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=10, roll_window=500 anchor 1613/2017: sd=1.85564, h1_pred=2.64944 anchor 1694/2017: sd=1.85929, h1_pred=2.85303 anchor 1775/2017: sd=1.81797, h1_pred=2.15036 anchor 1856/2017: sd=1.65736, h1_pred=2.84016 anchor 1937/2017: sd=1.62662, h1_pred=2.14779 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.924805 RMSE : 3.507115 R2 : -2.456264 Pearson r : -0.071509 QLIKE : 7.395842 5 day(s) MAE : 2.915682 RMSE : 3.423420 R2 : -2.266680 Pearson r : 0.006983 QLIKE : 7.370242 10 day(s) MAE : 2.912444 RMSE : 3.399253 R2 : -2.198418 Pearson r : 0.024564 QLIKE : 7.305151 full horizon MAE : 2.912444 RMSE : 3.399253 R2 : -2.198418 Pearson r : 0.024564 QLIKE : 7.305151 [GARCH] C — H=20 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2018, split=0.8 -> n_train=1614, t0=1613, anchors=405, H=20, roll_window=500 anchor 1613/2017: sd=1.85564, h1_pred=2.64944 anchor 1694/2017: sd=1.85929, h1_pred=2.85303 anchor 1775/2017: sd=1.81797, h1_pred=2.15036 anchor 1856/2017: sd=1.65736, h1_pred=2.84016 anchor 1937/2017: sd=1.62662, h1_pred=2.14779 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 2.924805 RMSE : 3.507115 R2 : -2.456264 Pearson r : -0.071509 QLIKE : 7.395842 5 day(s) MAE : 2.915682 RMSE : 3.423420 R2 : -2.266680 Pearson r : 0.006983 QLIKE : 7.370242 10 day(s) MAE : 2.912444 RMSE : 3.399253 R2 : -2.198418 Pearson r : 0.024564 QLIKE : 7.305151 20 day(s) MAE : 2.899955 RMSE : 3.379488 R2 : -2.145620 Pearson r : 0.034844 QLIKE : 7.280166 full horizon MAE : 2.899955 RMSE : 3.379488 R2 : -2.145620 Pearson r : 0.034844 QLIKE : 7.280166 [GARCH] BTCUSDT — H=1 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2399, split=0.8 -> n_train=1919, t0=1918, anchors=481, H=1, roll_window=500 anchor 1918/2398: sd=2.49753, h1_pred=7.90099 anchor 2014/2398: sd=2.46728, h1_pred=11.4554 anchor 2110/2398: sd=2.42276, h1_pred=6.17304 anchor 2206/2398: sd=2.49018, h1_pred=6.24523 anchor 2302/2398: sd=2.62524, h1_pred=7.74882 anchor 2398/2398: sd=2.72702, h1_pred=7.38778 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 10.581360 RMSE : 13.109958 R2 : -22.367818 Pearson r : 0.028521 QLIKE : 7.610465 full horizon MAE : 10.581360 RMSE : 13.109958 R2 : -22.367818 Pearson r : 0.028521 QLIKE : 7.610465 [GARCH] BTCUSDT — H=5 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2399, split=0.8 -> n_train=1919, t0=1918, anchors=481, H=5, roll_window=500 anchor 1918/2398: sd=2.49753, h1_pred=7.90099 anchor 2014/2398: sd=2.46728, h1_pred=11.4554 anchor 2110/2398: sd=2.42276, h1_pred=6.17304 anchor 2206/2398: sd=2.49018, h1_pred=6.24523 anchor 2302/2398: sd=2.62524, h1_pred=7.74882 anchor 2398/2398: sd=2.72702, h1_pred=7.38778 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 10.581360 RMSE : 13.109958 R2 : -22.367818 Pearson r : 0.028521 QLIKE : 7.610465 5 day(s) MAE : 11.462170 RMSE : 14.056496 R2 : -25.936163 Pearson r : 0.049892 QLIKE : 7.593759 full horizon MAE : 11.462170 RMSE : 14.056496 R2 : -25.936163 Pearson r : 0.049892 QLIKE : 7.593759 [GARCH] BTCUSDT — H=10 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2399, split=0.8 -> n_train=1919, t0=1918, anchors=481, H=10, roll_window=500 anchor 1918/2398: sd=2.49753, h1_pred=7.90099 anchor 2014/2398: sd=2.46728, h1_pred=11.4554 anchor 2110/2398: sd=2.42276, h1_pred=6.17304 anchor 2206/2398: sd=2.49018, h1_pred=6.24523 anchor 2302/2398: sd=2.62524, h1_pred=7.74882 anchor 2398/2398: sd=2.72702, h1_pred=7.38778 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 10.581360 RMSE : 13.109958 R2 : -22.367818 Pearson r : 0.028521 QLIKE : 7.610465 5 day(s) MAE : 11.462170 RMSE : 14.056496 R2 : -25.936163 Pearson r : 0.049892 QLIKE : 7.593759 10 day(s) MAE : 12.160214 RMSE : 15.322183 R2 : -31.112685 Pearson r : 0.043659 QLIKE : 7.602872 full horizon MAE : 12.160214 RMSE : 15.322183 R2 : -31.112685 Pearson r : 0.043659 QLIKE : 7.602872 [GARCH] BTCUSDT — H=20 (step=59, x_feat=1, y_feat=1) [vol rolling] B=2399, split=0.8 -> n_train=1919, t0=1918, anchors=481, H=20, roll_window=500 anchor 1918/2398: sd=2.49753, h1_pred=7.90099 anchor 2014/2398: sd=2.46728, h1_pred=11.4554 anchor 2110/2398: sd=2.42276, h1_pred=6.17304 anchor 2206/2398: sd=2.49018, h1_pred=6.24523 anchor 2302/2398: sd=2.62524, h1_pred=7.74882 anchor 2398/2398: sd=2.72702, h1_pred=7.38778 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 10.581360 RMSE : 13.109958 R2 : -22.367818 Pearson r : 0.028521 QLIKE : 7.610465 5 day(s) MAE : 11.462170 RMSE : 14.056496 R2 : -25.936163 Pearson r : 0.049892 QLIKE : 7.593759 10 day(s) MAE : 12.160214 RMSE : 15.322183 R2 : -31.112685 Pearson r : 0.043659 QLIKE : 7.602872 20 day(s) MAE : 13.009847 RMSE : 17.526014 R2 : -41.054064 Pearson r : 0.026922 QLIKE : 7.624235 full horizon MAE : 13.009847 RMSE : 17.526014 R2 : -41.054064 Pearson r : 0.026922 QLIKE : 7.624235 [GARCH] EURUSD — H=1 (step=59, x_feat=1, y_feat=1) [vol rolling] B=3782, split=0.8 -> n_train=3025, t0=3024, anchors=758, H=1, roll_window=500 anchor 3024/3781: sd=0.482831, h1_pred=0.544266 anchor 3175/3781: sd=0.523006, h1_pred=0.210495 anchor 3326/3781: sd=0.514012, h1_pred=0.236107 anchor 3477/3781: sd=0.442679, h1_pred=0.0999663 anchor 3628/3781: sd=0.351926, h1_pred=0.0976806 anchor 3779/3781: sd=0.415898, h1_pred=0.424184 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.349073 RMSE : 0.465803 R2 : -0.233381 Pearson r : 0.053204 QLIKE : 7.951148 full horizon MAE : 0.349073 RMSE : 0.465803 R2 : -0.233381 Pearson r : 0.053204 QLIKE : 7.951148 [GARCH] EURUSD — H=5 (step=59, x_feat=1, y_feat=1) [vol rolling] B=3782, split=0.8 -> n_train=3025, t0=3024, anchors=758, H=5, roll_window=500 anchor 3024/3781: sd=0.482831, h1_pred=0.544266 anchor 3175/3781: sd=0.523006, h1_pred=0.210495 anchor 3326/3781: sd=0.514012, h1_pred=0.236107 anchor 3477/3781: sd=0.442679, h1_pred=0.0999663 anchor 3628/3781: sd=0.351926, h1_pred=0.0976806 anchor 3779/3781: sd=0.415898, h1_pred=0.424184 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.349073 RMSE : 0.465803 R2 : -0.233381 Pearson r : 0.053204 QLIKE : 7.951148 5 day(s) MAE : 0.349318 RMSE : 0.466015 R2 : -0.243071 Pearson r : 0.040456 QLIKE : 7.965275 full horizon MAE : 0.349318 RMSE : 0.466015 R2 : -0.243071 Pearson r : 0.040456 QLIKE : 7.965275 [GARCH] EURUSD — H=10 (step=59, x_feat=1, y_feat=1) [vol rolling] B=3782, split=0.8 -> n_train=3025, t0=3024, anchors=758, H=10, roll_window=500 anchor 3024/3781: sd=0.482831, h1_pred=0.544266 anchor 3175/3781: sd=0.523006, h1_pred=0.210495 anchor 3326/3781: sd=0.514012, h1_pred=0.236107 anchor 3477/3781: sd=0.442679, h1_pred=0.0999663 anchor 3628/3781: sd=0.351926, h1_pred=0.0976806 anchor 3779/3781: sd=0.415898, h1_pred=0.424184 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.349073 RMSE : 0.465803 R2 : -0.233381 Pearson r : 0.053204 QLIKE : 7.951148 5 day(s) MAE : 0.349318 RMSE : 0.466015 R2 : -0.243071 Pearson r : 0.040456 QLIKE : 7.965275 10 day(s) MAE : 0.351075 RMSE : 0.468390 R2 : -0.257742 Pearson r : 0.022274 QLIKE : 7.985322 full horizon MAE : 0.351075 RMSE : 0.468390 R2 : -0.257742 Pearson r : 0.022274 QLIKE : 7.985322 [GARCH] EURUSD — H=20 (step=59, x_feat=1, y_feat=1) [vol rolling] B=3782, split=0.8 -> n_train=3025, t0=3024, anchors=758, H=20, roll_window=500 anchor 3024/3781: sd=0.482831, h1_pred=0.544266 anchor 3175/3781: sd=0.523006, h1_pred=0.210495 anchor 3326/3781: sd=0.514012, h1_pred=0.236107 anchor 3477/3781: sd=0.442679, h1_pred=0.0999663 anchor 3628/3781: sd=0.351926, h1_pred=0.0976806 anchor 3779/3781: sd=0.415898, h1_pred=0.424184 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.349073 RMSE : 0.465803 R2 : -0.233381 Pearson r : 0.053204 QLIKE : 7.951148 5 day(s) MAE : 0.349318 RMSE : 0.466015 R2 : -0.243071 Pearson r : 0.040456 QLIKE : 7.965275 10 day(s) MAE : 0.351075 RMSE : 0.468390 R2 : -0.257742 Pearson r : 0.022274 QLIKE : 7.985322 20 day(s) MAE : 0.354225 RMSE : 0.471626 R2 : -0.270650 Pearson r : 0.010490 QLIKE : 7.998339 full horizon MAE : 0.354225 RMSE : 0.471626 R2 : -0.270650 Pearson r : 0.010490 QLIKE : 7.998339 [GARCH] GOLD — H=1 (step=59, x_feat=1, y_feat=1) [vol rolling] B=5534, split=0.8 -> n_train=4427, t0=4426, anchors=1108, H=1, roll_window=500 anchor 4426/5533: sd=1.04494, h1_pred=0.70255 anchor 4647/5533: sd=0.795476, h1_pred=0.745113 anchor 4868/5533: sd=0.809417, h1_pred=0.85374 anchor 5089/5533: sd=0.788481, h1_pred=0.492214 anchor 5310/5533: sd=0.810009, h1_pred=1.0791 anchor 5531/5533: sd=0.906483, h1_pred=1.49614 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.952836 RMSE : 1.178645 R2 : -0.956726 Pearson r : -0.016528 QLIKE : 7.512093 full horizon MAE : 0.952836 RMSE : 1.178645 R2 : -0.956726 Pearson r : -0.016528 QLIKE : 7.512093 [GARCH] GOLD — H=5 (step=59, x_feat=1, y_feat=1) [vol rolling] B=5534, split=0.8 -> n_train=4427, t0=4426, anchors=1108, H=5, roll_window=500 anchor 4426/5533: sd=1.04494, h1_pred=0.70255 anchor 4647/5533: sd=0.795476, h1_pred=0.745113 anchor 4868/5533: sd=0.809417, h1_pred=0.85374 anchor 5089/5533: sd=0.788481, h1_pred=0.492214 anchor 5310/5533: sd=0.810009, h1_pred=1.0791 anchor 5531/5533: sd=0.906483, h1_pred=1.49614 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.952836 RMSE : 1.178645 R2 : -0.956726 Pearson r : -0.016528 QLIKE : 7.512093 5 day(s) MAE : 0.955287 RMSE : 1.179401 R2 : -0.951522 Pearson r : -0.003565 QLIKE : 7.490822 full horizon MAE : 0.955287 RMSE : 1.179401 R2 : -0.951522 Pearson r : -0.003565 QLIKE : 7.490822 [GARCH] GOLD — H=10 (step=59, x_feat=1, y_feat=1) [vol rolling] B=5534, split=0.8 -> n_train=4427, t0=4426, anchors=1108, H=10, roll_window=500 anchor 4426/5533: sd=1.04494, h1_pred=0.70255 anchor 4647/5533: sd=0.795476, h1_pred=0.745113 anchor 4868/5533: sd=0.809417, h1_pred=0.85374 anchor 5089/5533: sd=0.788481, h1_pred=0.492214 anchor 5310/5533: sd=0.810009, h1_pred=1.0791 anchor 5531/5533: sd=0.906483, h1_pred=1.49614 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.952836 RMSE : 1.178645 R2 : -0.956726 Pearson r : -0.016528 QLIKE : 7.512093 5 day(s) MAE : 0.955287 RMSE : 1.179401 R2 : -0.951522 Pearson r : -0.003565 QLIKE : 7.490822 10 day(s) MAE : 0.961594 RMSE : 1.183407 R2 : -0.955371 Pearson r : 0.005137 QLIKE : 7.497311 full horizon MAE : 0.961594 RMSE : 1.183407 R2 : -0.955371 Pearson r : 0.005137 QLIKE : 7.497311 [GARCH] GOLD — H=20 (step=59, x_feat=1, y_feat=1) [vol rolling] B=5534, split=0.8 -> n_train=4427, t0=4426, anchors=1108, H=20, roll_window=500 anchor 4426/5533: sd=1.04494, h1_pred=0.70255 anchor 4647/5533: sd=0.795476, h1_pred=0.745113 anchor 4868/5533: sd=0.809417, h1_pred=0.85374 anchor 5089/5533: sd=0.788481, h1_pred=0.492214 anchor 5310/5533: sd=0.810009, h1_pred=1.0791 anchor 5531/5533: sd=0.906483, h1_pred=1.49614 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.952836 RMSE : 1.178645 R2 : -0.956726 Pearson r : -0.016528 QLIKE : 7.512093 5 day(s) MAE : 0.955287 RMSE : 1.179401 R2 : -0.951522 Pearson r : -0.003565 QLIKE : 7.490822 10 day(s) MAE : 0.961594 RMSE : 1.183407 R2 : -0.955371 Pearson r : 0.005137 QLIKE : 7.497311 20 day(s) MAE : 0.973243 RMSE : 1.194606 R2 : -0.975036 Pearson r : 0.002903 QLIKE : 7.532503 full horizon MAE : 0.973243 RMSE : 1.194606 R2 : -0.975036 Pearson r : 0.002903 QLIKE : 7.532503 [GARCH] SP500 — H=1 (step=59, x_feat=1, y_feat=1) [vol rolling] B=3466, split=0.8 -> n_train=2772, t0=2771, anchors=695, H=1, roll_window=500 anchor 2771/3465: sd=1.15234, h1_pred=0.784653 anchor 2910/3465: sd=1.16472, h1_pred=0.36848 anchor 3049/3465: sd=1.02343, h1_pred=0.403425 anchor 3188/3465: sd=0.790157, h1_pred=0.431721 anchor 3327/3465: sd=0.681993, h1_pred=0.501682 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.964632 RMSE : 2.217733 R2 : -4.540694 Pearson r : 0.010440 QLIKE : 7.309367 full horizon MAE : 0.964632 RMSE : 2.217733 R2 : -4.540694 Pearson r : 0.010440 QLIKE : 7.309367 [GARCH] SP500 — H=5 (step=59, x_feat=1, y_feat=1) [vol rolling] B=3466, split=0.8 -> n_train=2772, t0=2771, anchors=695, H=5, roll_window=500 anchor 2771/3465: sd=1.15234, h1_pred=0.784653 anchor 2910/3465: sd=1.16472, h1_pred=0.36848 anchor 3049/3465: sd=1.02343, h1_pred=0.403425 anchor 3188/3465: sd=0.790157, h1_pred=0.431721 anchor 3327/3465: sd=0.681993, h1_pred=0.501682 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.964632 RMSE : 2.217733 R2 : -4.540694 Pearson r : 0.010440 QLIKE : 7.309367 5 day(s) MAE : 0.963199 RMSE : 2.112908 R2 : -4.020409 Pearson r : 0.025739 QLIKE : 7.327925 full horizon MAE : 0.963199 RMSE : 2.112908 R2 : -4.020409 Pearson r : 0.025739 QLIKE : 7.327925 [GARCH] SP500 — H=10 (step=59, x_feat=1, y_feat=1) [vol rolling] B=3466, split=0.8 -> n_train=2772, t0=2771, anchors=695, H=10, roll_window=500 anchor 2771/3465: sd=1.15234, h1_pred=0.784653 anchor 2910/3465: sd=1.16472, h1_pred=0.36848 anchor 3049/3465: sd=1.02343, h1_pred=0.403425 anchor 3188/3465: sd=0.790157, h1_pred=0.431721 anchor 3327/3465: sd=0.681993, h1_pred=0.501682 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.964632 RMSE : 2.217733 R2 : -4.540694 Pearson r : 0.010440 QLIKE : 7.309367 5 day(s) MAE : 0.963199 RMSE : 2.112908 R2 : -4.020409 Pearson r : 0.025739 QLIKE : 7.327925 10 day(s) MAE : 0.958098 RMSE : 2.002501 R2 : -3.512686 Pearson r : 0.038548 QLIKE : 7.347556 full horizon MAE : 0.958098 RMSE : 2.002501 R2 : -3.512686 Pearson r : 0.038548 QLIKE : 7.347556 [GARCH] SP500 — H=20 (step=59, x_feat=1, y_feat=1) [vol rolling] B=3466, split=0.8 -> n_train=2772, t0=2771, anchors=695, H=20, roll_window=500 anchor 2771/3465: sd=1.15234, h1_pred=0.784653 anchor 2910/3465: sd=1.16472, h1_pred=0.36848 anchor 3049/3465: sd=1.02343, h1_pred=0.403425 anchor 3188/3465: sd=0.790157, h1_pred=0.431721 anchor 3327/3465: sd=0.681993, h1_pred=0.501682 [metrics — vol rolling (ML-aligned split)] 1 day(s) MAE : 0.964632 RMSE : 2.217733 R2 : -4.540694 Pearson r : 0.010440 QLIKE : 7.309367 5 day(s) MAE : 0.963199 RMSE : 2.112908 R2 : -4.020409 Pearson r : 0.025739 QLIKE : 7.327925 10 day(s) MAE : 0.958098 RMSE : 2.002501 R2 : -3.512686 Pearson r : 0.038548 QLIKE : 7.347556 20 day(s) MAE : 0.937416 RMSE : 1.837063 R2 : -2.796084 Pearson r : 0.045488 QLIKE : 7.333914 full horizon MAE : 0.937416 RMSE : 1.837063 R2 : -2.796084 Pearson r : 0.045488 QLIKE : 7.333914
In [160]:
final_frames = build_metric_frames(final_results_store, outer_horizon="full", pretty_print=True)
=== EURUSD | GARCH | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.349073 0.349318 0.351075 0.354225
Pearson r 0.053204 0.040456 0.022274 0.010490
QLIKE 7.951148 7.965275 7.985322 7.998339
R2 -0.233381 -0.243071 -0.257742 -0.270650
RMSE 0.465803 0.466015 0.468390 0.471626
=== EURUSD | ITransformer | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.107403 0.110138 0.112221 0.114818
Pearson r 0.397039 0.357847 0.322897 0.283393
QLIKE 0.407426 0.439176 0.463336 0.486015
R2 0.004401 -0.018628 -0.058046 -0.083348
RMSE 0.213172 0.213587 0.216649 0.217942
=== EURUSD | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.084093 0.090159 0.094697 0.099880
Pearson r 0.614829 0.538825 0.505370 0.462240
QLIKE 0.410261 0.425955 0.424235 0.488096
R2 0.352545 0.271619 0.233426 0.176566
RMSE 0.171907 0.180612 0.184408 0.190008
=== EURUSD | Simple_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.085509 0.090961 0.098495 0.095458
Pearson r 0.646411 0.558189 0.487512 0.444499
QLIKE 0.399726 0.438345 0.510281 0.511468
R2 0.401153 0.297512 0.190665 0.177578
RMSE 0.165328 0.177372 0.189482 0.189891
=== EURUSD | Simple_MLP | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.088861 0.094690 0.100409 0.101710
Pearson r 0.561921 0.520609 0.483216 0.451376
QLIKE 0.487990 0.438364 0.451981 0.483321
R2 0.306227 0.244793 0.193126 0.158975
RMSE 0.177950 0.183907 0.189194 0.192027
=== AAPL | GARCH | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.244934 2.255908 2.259492 2.264942
Pearson r 0.037045 0.050060 0.053776 0.022402
QLIKE 7.146105 7.189652 7.200451 7.186950
R2 -2.334505 -2.228654 -2.140761 -2.072988
RMSE 3.204341 3.162423 3.123894 3.099011
=== AAPL | ITransformer | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.440809 2.552977 2.581058 2.612338
Pearson r 0.230568 0.197919 0.149980 0.106349
QLIKE 0.533305 0.555028 0.558112 0.558632
R2 -0.017570 -0.019944 -0.020692 -0.021553
RMSE 8.718175 9.027910 9.068595 9.091777
=== AAPL | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.092918 2.424971 2.503462 2.518576
Pearson r 0.425086 0.345513 0.251507 0.185200
QLIKE 0.278484 0.403303 0.426101 0.476400
R2 0.154880 0.026756 0.012712 -0.024511
RMSE 7.945159 8.818807 8.918966 9.104931
=== AAPL | Simple_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.381797 2.396292 2.724439 2.683451
Pearson r 0.450763 0.397629 0.188058 0.075199
QLIKE 0.533954 0.359122 0.445717 0.491763
R2 -0.026858 0.116960 -0.057191 -0.066081
RMSE 8.757876 8.400193 9.229315 9.287809
=== AAPL | Simple_MLP | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.133557 2.490259 2.587152 2.561629
Pearson r 0.467360 0.354377 0.192889 0.153891
QLIKE 0.288490 0.368075 0.456740 0.484823
R2 0.183208 0.110879 -0.000617 -0.023261
RMSE 7.810869 8.429067 8.978971 9.099373
=== MSFT | GARCH | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 1.786189 1.798916 1.807977 1.810601
Pearson r 0.062285 0.050124 0.041701 0.048946
QLIKE 7.146787 7.217582 7.243873 7.203543
R2 -1.374047 -1.395942 -1.402882 -1.358412
RMSE 2.192145 2.209159 2.225826 2.235479
=== MSFT | ITransformer | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 1.931308 2.031079 2.074009 2.139999
Pearson r 0.225590 0.063310 0.016352 0.002562
QLIKE 0.415285 0.467463 0.489862 0.504887
R2 0.016365 -0.023119 -0.033173 -0.037122
RMSE 6.647700 6.779962 6.813446 7.032386
=== MSFT | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.059900 2.102978 2.127596 2.177952
Pearson r 0.131766 -0.008312 -0.032547 -0.022945
QLIKE 0.488507 0.496964 0.502815 0.503067
R2 0.002540 -0.026013 -0.031073 -0.027268
RMSE 6.694253 6.789545 6.806518 6.998898
=== MSFT | Simple_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 1.794123 1.995786 1.999023 2.161636
Pearson r 0.306164 0.177077 0.151018 0.077415
QLIKE 0.352571 0.386446 0.429503 0.504174
R2 0.070195 -0.005889 -0.008239 -0.028414
RMSE 6.463240 6.722631 6.730728 7.002798
=== MSFT | Simple_MLP | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.170486 2.076898 2.117085 2.182670
Pearson r 0.089911 0.006929 0.006607 0.002986
QLIKE 0.461403 0.496065 0.494443 0.505668
R2 -0.000529 -0.032200 -0.027595 -0.026518
RMSE 6.704543 6.809986 6.795027 6.996340
=== GE | GARCH | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 3.633551 3.566507 3.535873 3.492241
Pearson r 0.004758 0.031238 0.014892 0.002262
QLIKE 7.128652 7.134824 7.128936 7.074895
R2 -3.263633 -2.819007 -2.602853 -2.377670
RMSE 4.848706 4.593790 4.478415 4.346271
=== GE | ITransformer | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.965051 4.962709 4.971934 4.985579
Pearson r -0.028817 -0.017652 -0.010269 -0.006401
QLIKE 0.685489 0.682444 0.679309 0.675004
R2 -0.001451 -0.001453 -0.001492 -0.001500
RMSE 27.446072 27.444522 27.447080 27.447914
=== GE | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.165101 4.162069 4.398488 4.299499
Pearson r 0.021352 0.009339 0.004121 -0.008817
QLIKE 0.707827 0.714538 0.751861 0.710987
R2 -0.014899 -0.013084 -0.015871 -0.011972
RMSE 27.629740 27.603435 27.643412 27.591037
=== GE | Simple_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.868009 4.076695 4.529892 4.529903
Pearson r 0.057023 0.054758 0.017229 0.004986
QLIKE 0.684371 0.653308 0.683444 0.703472
R2 -0.001841 -0.007966 -0.037158 -0.012408
RMSE 27.451418 27.533626 27.931529 27.596981
=== GE | Simple_MLP | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.312359 4.216504 4.366981 4.661683
Pearson r 0.020144 0.024821 -0.002464 -0.002972
QLIKE 0.704225 0.693025 0.734319 0.753618
R2 -0.009984 -0.014046 -0.015077 -0.015395
RMSE 27.562751 27.616541 27.632609 27.637670
=== BAC | GARCH | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.870229 2.931345 2.978968 3.009191
Pearson r -0.032652 0.046543 0.063185 0.066700
QLIKE 7.860950 7.818094 7.802285 7.729510
R2 -3.005962 -2.828464 -2.847093 -2.870120
RMSE 3.402386 3.338518 3.359030 3.376419
=== BAC | ITransformer | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.135931 2.221024 2.247263 2.265229
Pearson r 0.338053 0.132651 0.079439 0.039138
QLIKE 0.347708 0.400563 0.412225 0.416911
R2 0.039809 -0.004133 -0.011983 -0.017090
RMSE 7.606597 7.782393 7.813123 7.831522
=== BAC | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.322486 2.229797 2.245889 3.150809
Pearson r 0.383914 0.102959 0.032765 0.027081
QLIKE 0.277494 0.416571 0.425780 0.488646
R2 0.090022 -0.010511 -0.017102 -0.939095
RMSE 7.405035 7.807068 7.832858 10.813494
=== BAC | Simple_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 1.952279 2.185919 2.228739 2.389312
Pearson r 0.373382 0.232467 0.155889 0.065314
QLIKE 0.241762 0.335168 0.356134 0.411970
R2 0.123545 -0.002525 -0.030704 -0.157433
RMSE 7.267358 7.776159 7.885061 8.354384
=== BAC | Simple_MLP | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.207774 2.427097 2.471279 2.348198
Pearson r 0.368325 0.169163 0.068570 -0.007246
QLIKE 0.321662 0.408902 0.458190 0.433901
R2 0.077676 0.020946 -0.014267 -0.016783
RMSE 7.455098 7.684592 7.821933 7.830341
=== C | GARCH | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.924805 2.915682 2.912444 2.899955
Pearson r -0.071509 0.006983 0.024564 0.034844
QLIKE 7.395842 7.370242 7.305151 7.280166
R2 -2.456264 -2.266680 -2.198418 -2.145620
RMSE 3.507115 3.423420 3.399253 3.379488
=== C | ITransformer | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.645889 2.790753 2.844405 2.883661
Pearson r 0.374447 0.210043 0.141742 0.091183
QLIKE 0.346335 0.396539 0.411933 0.418188
R2 0.038077 -0.010547 -0.022949 -0.030104
RMSE 9.257640 9.527135 9.596620 9.633940
=== C | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.699713 2.862133 3.018361 2.868924
Pearson r 0.465585 0.277344 0.161802 0.046229
QLIKE 0.281128 0.369118 0.421271 0.429703
R2 0.203245 0.058570 -0.014295 -0.037031
RMSE 8.425441 9.195553 9.555944 9.666279
=== C | Simple_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.415491 2.835218 3.431802 3.222991
Pearson r 0.507865 0.280032 0.143978 0.084988
QLIKE 0.247028 0.338508 0.374971 0.418580
R2 0.203331 0.006735 -0.509005 -0.134118
RMSE 8.424982 9.445315 11.655656 10.108636
=== C | Simple_MLP | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.703733 3.033120 2.904410 2.939347
Pearson r 0.548943 0.239157 0.101051 0.044397
QLIKE 0.284079 0.387526 0.423407 0.433286
R2 0.282685 0.037287 -0.022719 -0.032764
RMSE 7.994385 9.298918 9.595545 9.646375
=== BTCUSDT | GARCH | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 10.581360 11.462170 12.160214 13.009847
Pearson r 0.028521 0.049892 0.043659 0.026922
QLIKE 7.610465 7.593759 7.602872 7.624235
R2 -22.367818 -25.936163 -31.112685 -41.054064
RMSE 13.109958 14.056496 15.322183 17.526014
=== BTCUSDT | ITransformer | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.568197 4.653337 4.697653 4.740776
Pearson r 0.232797 0.095580 0.029499 -0.041871
QLIKE 0.360800 0.374173 0.380413 0.385184
R2 0.010904 -0.008462 -0.016329 -0.023895
RMSE 9.135728 9.223924 9.260948 9.293716
=== BTCUSDT | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 3.931521 4.448533 4.647752 4.842995
Pearson r 0.410540 0.231193 0.123902 0.058660
QLIKE 0.243445 0.347832 0.383631 0.403062
R2 0.112389 0.006071 -0.049880 -0.059379
RMSE 8.654369 9.157219 9.412567 9.453382
=== BTCUSDT | Simple_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.171120 4.231545 4.366575 5.884454
Pearson r 0.346522 0.260723 0.198533 0.101963
QLIKE 0.281291 0.324546 0.357001 0.484802
R2 0.050646 0.023611 -0.041306 -0.221017
RMSE 8.950310 9.076061 9.374053 10.148982
=== BTCUSDT | Simple_MLP | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.187088 4.548116 4.574250 4.601800
Pearson r 0.347905 0.234948 0.189766 0.121917
QLIKE 0.271769 0.336755 0.365160 0.373111
R2 0.023958 -0.082035 -0.076952 -0.108087
RMSE 9.075244 9.554470 9.533149 9.668266
=== GOLD | GARCH | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.952836 0.955287 0.961594 0.973243
Pearson r -0.016528 -0.003565 0.005137 0.002903
QLIKE 7.512093 7.490822 7.497311 7.532503
R2 -0.956726 -0.951522 -0.955371 -0.975036
RMSE 1.178645 1.179401 1.183407 1.194606
=== GOLD | ITransformer | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.469837 0.480508 0.486717 0.498326
Pearson r 0.317324 0.250319 0.207961 0.129706
QLIKE 0.490178 0.505330 0.519712 0.538747
R2 -0.332768 -0.372561 -0.388762 -0.419045
RMSE 0.756532 0.769159 0.778037 0.792343
=== GOLD | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.288624 0.306847 0.316029 0.342880
Pearson r 0.594212 0.504068 0.499918 0.414966
QLIKE 0.544326 0.443687 0.492770 0.582242
R2 0.310299 0.242446 0.206566 0.108536
RMSE 0.544228 0.571422 0.588087 0.628010
=== GOLD | Simple_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.265208 0.282760 0.304010 0.330226
Pearson r 0.608979 0.543527 0.473712 0.368577
QLIKE 0.393908 0.486091 0.541941 0.681796
R2 0.356698 0.251851 0.206356 0.042044
RMSE 0.525603 0.567864 0.588165 0.651010
=== GOLD | Simple_MLP | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.298556 0.309862 0.316044 0.342462
Pearson r 0.547382 0.490942 0.443987 0.353597
QLIKE 0.367743 0.509587 0.562998 0.556682
R2 0.238052 0.216846 0.146739 0.034796
RMSE 0.572022 0.580997 0.609856 0.653468
=== SP500 | GARCH | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.964632 0.963199 0.958098 0.937416
Pearson r 0.010440 0.025739 0.038548 0.045488
QLIKE 7.309367 7.327925 7.347556 7.333914
R2 -4.540694 -4.020409 -3.512686 -2.796084
RMSE 2.217733 2.112908 2.002501 1.837063
=== SP500 | ITransformer | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.611600 0.643905 0.655040 0.663976
Pearson r 0.386171 0.181329 0.112304 0.054471
QLIKE 0.708717 0.825929 0.895425 0.929563
R2 0.120074 0.006448 -0.019542 -0.042257
RMSE 2.656234 2.822679 2.859553 2.891403
=== SP500 | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.499970 0.790432 0.855580 0.907591
Pearson r 0.654030 0.251395 0.158396 0.107911
QLIKE 0.471918 0.731043 0.889797 0.872193
R2 0.283898 -0.750919 -0.660475 -0.756777
RMSE 2.396241 3.747134 3.649318 3.753873
=== SP500 | Simple_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.469331 0.670513 0.641873 0.690240
Pearson r 0.584546 0.320906 0.248781 0.217409
QLIKE 0.386871 0.721224 0.847337 0.948819
R2 0.258683 -0.212322 0.019493 0.039615
RMSE 2.438065 3.117993 2.804276 2.775518
=== SP500 | Simple_MLP | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.519677 0.614845 0.695073 0.703336
Pearson r 0.615524 0.413420 0.230992 0.166332
QLIKE 0.543671 1.015928 0.943434 0.867437
R2 0.328406 0.157536 -0.020003 -0.019545
RMSE 2.320580 2.599212 2.860199 2.859727
In [161]:
import os
os.makedirs("results", exist_ok=True)
export_metrics(final_results_store, "results/metrics_1_task_all.csv", outer_horizon="full")
export_metrics(final_results_store, "results/metrics_1_task_all.txt", outer_horizon="full")
Out[161]:
{'mode': 'text',
'path': '/Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/results/metrics_1_task_all.txt',
'sections': 45}
In [162]:
saved_metric_plots = plot_metric_vs_horizon(
final_frames,
tickers=None,
models=None,
metrics=None,
include_full=True,
save_dir="plots/task_1",
show=True
)
print("Saved", len(saved_metric_plots), "charts to plots/task_1")
saved_y_plots = plot_h1_full_from_results(
final_results_store,
tickers=None,
models=None,
save_dir="plots/task_1/y_plots",
show=True,
verbose=True
)
Saved 45 charts to plots/task_1
[EURUSD H=1] Simple_MLP: blob_shape=(757, 1, 2), yt=ok, yp=ok
[EURUSD H=1] Simple_KAN: blob_shape=(757, 1, 2), yt=ok, yp=ok
[EURUSD H=1] Simple_LSTM: blob_shape=(757, 1, 2), yt=ok, yp=ok
[EURUSD H=1] ITransformer: blob_shape=(757, 1, 2), yt=ok, yp=ok
[EURUSD H=1] GARCH: blob_shape=(758, 1, 2), yt=ok, yp=ok
[EURUSD H=1] actual=YES
[EURUSD H=1] Aligned series head (first 50 of 757 rows):
Actual Simple_MLP_pred Simple_KAN_pred Simple_LSTM_pred ITransformer_pred GARCH_pred
0.739859 0.344704 0.392549 0.597757 0.947548 0.527567
0.743822 0.445781 0.437255 0.740661 0.599173 0.54225
1.02363 0.450836 0.505398 0.705366 0.333677 0.560192
0.0199331 0.024966 0.0334082 0.0312601 0.0533302 0.542041
0.52303 0.357369 0.462449 0.454133 1.39534 0.523388
0.355217 0.462683 0.414474 0.585512 0.58706 0.514174
0.417086 0.452745 0.749411 0.533248 1.32186 0.499552
0.266624 0.516042 0.653394 0.541946 1.14001 0.490014
0.312901 0.452725 0.505506 0.442269 0.445612 0.480418
0.00443731 0.0217347 0.0203908 0.02228 0.0256355 0.463381
0.261753 0.319742 0.504155 0.244658 0.270375 0.450841
0.788937 0.415738 0.362864 0.281103 0.819658 0.451272
0.598342 0.49787 0.73489 0.564776 1.04084 0.455597
0.723261 0.479105 0.549063 0.610733 0.797211 0.448468
0.341589 0.33334 0.274472 0.65016 0.503877 0.434952
0.0137579 0.0460323 0.0391602 0.0253707 0.0344898 0.434445
0.29405 0.298564 0.17252 0.292281 0.451104 0.416651
0.513047 0.348563 0.319324 0.311541 0.876412 0.414602
0.252839 0.469047 0.489849 0.463047 0.978693 0.397076
0.305402 0.337187 0.505978 0.415025 0.737488 0.377731
0.226156 0.370188 0.166301 0.397258 0.225177 0.374779
0.00963577 0.0277823 0.0236111 0.0179488 0.0226416 0.364494
0.0784947 0.287787 0.317479 0.199479 0.45876 0.357774
0.199036 0.342416 0.260903 0.192285 0.492816 0.345893
0.171632 0.375328 0.214984 0.245775 0.396353 0.317617
0.201223 0.337522 0.321647 0.248536 0.240288 0.29398
0.353565 0.27527 0.18525 0.229204 0.339252 0.283016
0.0670925 0.0176184 0.0116163 0.0137525 0.0314016 0.286678
0.2403 0.219411 0.27959 0.194458 0.214966 0.255759
0.443555 0.367755 0.341669 0.274171 0.43017 0.232695
0.418858 0.471934 0.319654 0.446601 0.58385 0.315411
0.378402 0.305767 0.304753 0.464049 0.3852 0.32468
0.644091 0.386463 0.240297 0.437104 0.169225 0.337647
0.0152947 0.0437088 0.0250701 0.0271955 0.0398959 0.376331
0.272258 0.26435 0.182403 0.356306 0.285023 0.375311
0.23458 0.407319 0.303133 0.349715 0.459831 0.380742
0.249941 0.356184 0.43941 0.363532 0.341011 0.364146
0.705648 0.453441 0.30452 0.352722 0.344282 0.352639
0.293732 0.340372 0.165647 0.476151 0.124775 0.367588
0.00988965 0.0148931 0.0140561 0.0200907 0.0414277 0.35363
0.164063 0.234852 0.173574 0.227632 0.192007 0.336453
0.414405 0.378224 0.387932 0.229056 0.427337 0.328718
0.477121 0.320954 0.427589 0.356612 0.384118 0.317037
0.264156 0.333569 0.531094 0.415628 0.349086 0.300313
0.210154 0.383308 0.202868 0.349614 0.177211 0.288811
0.0136265 0.0248596 0.0125404 0.0164581 0.0414452 0.318718
0.265973 0.257019 0.22965 0.18229 0.227918 0.34367
0.234507 0.304722 0.326707 0.246448 0.414127 0.35147
0.284101 0.368278 0.261206 0.314976 0.249094 0.325053
0.226239 0.273946 0.238938 0.324761 0.266824 0.328159
[AAPL H=1] Simple_MLP: blob_shape=(404, 1, 2), yt=ok, yp=ok [AAPL H=1] Simple_KAN: blob_shape=(404, 1, 2), yt=ok, yp=ok [AAPL H=1] Simple_LSTM: blob_shape=(404, 1, 2), yt=ok, yp=ok [AAPL H=1] ITransformer: blob_shape=(404, 1, 2), yt=ok, yp=ok [AAPL H=1] GARCH: blob_shape=(405, 1, 2), yt=ok, yp=ok [AAPL H=1] actual=YES [AAPL H=1] Aligned series head (first 50 of 404 rows): Actual Simple_MLP_pred Simple_KAN_pred Simple_LSTM_pred ITransformer_pred GARCH_pred 0.934885 1.31932 1.18825 2.16446 2.42068 2.25613 1.65221 1.36129 1.17478 2.16442 2.42399 2.18043 3.00982 1.35182 1.22232 2.16444 2.42258 2.06684 7.17364 1.39072 1.41241 2.16458 2.42461 2.73913 2.32415 2.30522 2.51576 2.16505 2.42585 3.17974 1.8112 2.01698 1.96311 2.16512 2.4198 2.99427 2.83463 1.85423 1.7408 2.16504 2.42037 2.85262 2.29244 1.94267 1.99217 2.16502 2.42385 2.8749 1.60917 1.86194 1.8384 2.16497 2.41991 2.80186 1.9016 1.57238 1.59799 2.16487 2.41823 2.70591 2.02761 1.52467 1.56333 2.16481 2.4219 2.59906 0.882809 1.7291 1.66265 2.16477 2.42444 2.61229 1.75996 1.60188 1.39613 2.16466 2.42291 2.50785 1.93843 1.6561 1.41594 2.16463 2.42397 2.61455 1.30271 1.61721 1.41695 2.16463 2.42792 2.52174 1.66381 1.56498 1.32174 2.1646 2.42643 2.40765 1.7326 1.47644 1.33844 2.16459 2.42518 2.34043 1.90607 1.44459 1.36064 2.16459 2.42626 2.54542 2.70629 1.37841 1.30702 2.16461 2.42328 2.46568 1.98734 1.62479 1.46902 2.16469 2.42522 2.33922 4.17835 1.48164 1.48119 2.16471 2.42235 2.25764 2.23484 1.90315 1.99231 2.16489 2.42604 2.2737 2.5908 1.8621 1.807 2.16491 2.42951 2.19551 1.38316 1.94774 1.82614 2.16493 2.42298 2.11403 3.07923 1.73234 1.62413 2.16484 2.42563 2.02952 2.16603 2.09576 1.86391 2.16487 2.42146 2.04042 1.08108 1.92821 1.69196 2.16486 2.42662 1.97355 1.05538 1.54711 1.39432 2.16475 2.4253 1.8765 1.56663 1.45655 1.36753 2.16465 2.42512 1.82837 1.20165 1.44119 1.36993 2.1646 2.42613 1.74378 3.78232 1.34114 1.26677 2.16455 2.42153 1.72905 1.6213 1.5621 1.60529 2.16471 2.42245 1.64485 2.03959 1.56189 1.45785 2.16472 2.41814 1.6066 2.6352 1.57972 1.50498 2.16473 2.42432 1.55211 1.42274 1.82831 1.7305 2.16477 2.42128 1.46491 2.873 1.82371 1.58312 2.16473 2.41962 1.52697 1.51033 2.0945 1.7479 2.16478 2.42555 1.43632 2.09779 1.92797 1.59163 2.16474 2.42007 1.35289 4.03363 1.84557 1.6143 2.16473 2.42895 1.4147 2.80041 2.13607 2.04788 2.16488 2.42278 1.74222 2.38801 2.04364 1.91588 2.16495 2.42124 1.67716 2.0338 1.89298 1.75553 2.16495 2.42527 1.66687 2.19524 1.82104 1.68172 2.1649 2.42699 1.57648 11.9895 1.8133 1.69822 2.16486 2.42736 1.68032 3.66056 3.81002 4.04814 2.16561 2.42493 1.81217 1.3156 3.2519 3.25078 2.16574 2.42666 1.72771 0.963659 2.62364 2.34477 2.1655 2.43195 1.74278 1.08602 2.19714 2.19123 2.1652 2.42907 1.76822 1.29415 1.98163 1.89388 2.16494 2.42724 1.68725 0.888571 1.84856 1.59722 2.16477 2.42529 1.60781
[MSFT H=1] Simple_MLP: blob_shape=(404, 1, 2), yt=ok, yp=ok [MSFT H=1] Simple_KAN: blob_shape=(404, 1, 2), yt=ok, yp=ok [MSFT H=1] Simple_LSTM: blob_shape=(404, 1, 2), yt=ok, yp=ok [MSFT H=1] ITransformer: blob_shape=(404, 1, 2), yt=ok, yp=ok [MSFT H=1] GARCH: blob_shape=(405, 1, 2), yt=ok, yp=ok [MSFT H=1] actual=YES [MSFT H=1] Aligned series head (first 50 of 404 rows): Actual Simple_MLP_pred Simple_KAN_pred Simple_LSTM_pred ITransformer_pred GARCH_pred 1.62646 1.73961 1.93734 1.66613 2.11871 2.67696 1.38102 2.43071 1.8169 1.89004 1.99044 2.58327 1.45815 1.59753 1.95379 1.72952 1.86909 2.57634 1.91787 1.84023 1.74501 1.84685 1.95442 2.48749 1.81463 2.68787 2.21117 2.05102 1.98984 2.42536 1.90777 2.19743 1.95498 2.09034 1.9772 2.401 1.13204 2.38266 1.71237 2.17862 2.02099 2.35936 2.16207 1.35764 1.87681 1.77582 2.25256 2.45703 1.60526 2.02502 1.89853 2.15715 1.78992 2.42708 1.36873 2.27203 1.9503 1.9103 1.63877 2.37856 1.54924 1.81255 1.85017 1.82842 1.94209 2.60355 1.38923 1.78413 1.60312 1.83067 1.84541 2.51095 1.71635 1.91094 1.56495 1.73631 1.889 2.42624 7.14713 2.3961 1.78968 1.85113 1.69845 2.59071 2.33716 2.48456 2.01067 4.09917 2.32677 2.50098 2.91188 2.52499 1.84571 2.21298 2.24735 2.45191 2.05814 1.53432 1.88379 2.45571 1.79204 2.42617 2.23013 3.30244 1.75319 2.45042 1.94899 2.46523 2.9262 3.33622 1.75693 2.5458 1.80381 2.37413 2.44337 2.78813 1.59914 3.02481 2.18518 2.32784 3.72187 2.79149 1.65043 2.75773 1.90665 2.29773 2.39093 2.00911 1.64401 3.57372 2.11741 2.41964 3.38264 1.88123 1.65171 2.81986 2.07522 2.66366 1.96965 2.67584 1.77624 3.34941 1.81315 2.70796 5.97013 3.35572 1.78881 2.53327 2.08852 2.6235 3.19073 3.32474 1.93784 4.27986 2.00598 2.76685 1.62044 2.86234 1.84047 3.49473 2.16547 2.70262 1.86668 3.10489 1.88084 2.19354 1.77528 2.63664 1.46844 1.98705 2.001 2.25943 2.19713 2.65452 1.78506 3.50086 1.66713 2.07797 2.02741 2.5765 2.02385 3.07652 1.87267 2.18156 2.11874 2.54788 2.15305 1.72852 1.98298 2.2954 2.04137 2.54958 3.35866 2.60708 1.92345 2.44389 1.73794 2.45151 4.75959 1.84585 1.61317 3.32509 2.10025 2.38132 2.26205 2.59118 1.61836 4.26256 1.97186 2.27912 2.5589 2.0442 1.6595 2.73261 1.92347 2.27234 28.4576 2.31255 1.6303 2.67998 2.05848 2.19075 5.29249 3.8194 3.6099 5.11279 2.53918 2.25135 5.58633 3.2335 3.5162 4.08792 2.69083 2.5873 2.95631 2.94772 3.80559 2.34364 2.357 3.13951 2.9314 2.88633 3.11775 3.07119 2.91461 3.06237 2.15893 3.44504 2.74561 3.4766 2.14656 3.182 2.50397 2.72324 2.68655 2.81498 2.13434 3.0849 2.31466 4.21852 2.86412 2.82111 2.25579 3.19814 1.9771 1.78674 2.55908 2.72812 1.79123 3.10159 1.16435 2.56281 2.53566 2.46767 2.39854 3.05142 1.01889 3.19063 2.23055 1.94746 2.22295 2.97549 1.35823 2.40673 2.00302 1.76752 1.95855 2.94902 1.42899 2.99753 2.21365 1.80012 2.30735 2.85005 1.02177 2.28681 2.38538 1.80112 1.8933 2.78264
[GE H=1] Simple_MLP: blob_shape=(404, 1, 2), yt=ok, yp=ok [GE H=1] Simple_KAN: blob_shape=(404, 1, 2), yt=ok, yp=ok [GE H=1] Simple_LSTM: blob_shape=(404, 1, 2), yt=ok, yp=ok [GE H=1] ITransformer: blob_shape=(404, 1, 2), yt=ok, yp=ok [GE H=1] GARCH: blob_shape=(405, 1, 2), yt=ok, yp=ok [GE H=1] actual=YES [GE H=1] Aligned series head (first 50 of 404 rows): Actual Simple_MLP_pred Simple_KAN_pred Simple_LSTM_pred ITransformer_pred GARCH_pred 1.5135 2.5157 1.82701 4.74426 4.90177 3.49396 1.72313 2.55159 1.85295 4.74426 4.90144 3.41691 1.67254 2.35463 1.80839 4.74426 4.88897 3.39128 1.66078 2.33543 1.82691 4.74426 4.90376 3.28505 0.769403 2.43806 1.8188 4.74426 4.89402 3.2313 1.04217 2.46612 1.78896 4.74426 4.8961 3.17445 3.65719 2.37674 1.82424 4.74426 4.90225 3.41098 1.16201 2.48417 1.8419 4.74426 4.90733 3.35891 4.30547 2.3359 1.81841 4.74426 4.90449 3.34239 1.37445 2.3859 1.78913 4.74426 4.9037 3.40142 1.38507 2.36139 1.76857 4.74426 4.90355 3.2851 1.13075 2.39083 1.76484 4.74426 4.90507 3.26334 1.22114 2.4184 1.76038 4.74426 4.906 3.19684 1.99537 2.33616 1.74756 4.74426 4.89769 3.19972 2.85347 2.38805 1.77607 4.74426 4.89784 3.26661 0.960334 2.37291 1.78551 4.74426 4.91071 3.32725 2.10725 2.36867 1.769 4.74426 4.89743 3.23501 2.0909 2.37139 1.76954 4.74426 4.89969 3.27368 1.68286 2.43166 1.78114 4.74426 4.89575 3.23135 3.11206 2.4029 1.77335 4.74426 4.90125 3.25283 2.59462 2.4041 1.7987 4.74426 4.8961 3.32743 4.00565 2.35743 1.78398 4.74426 4.89996 3.3743 2.58083 2.40592 1.7918 4.74426 4.90413 3.3348 1.92091 2.44247 1.78737 4.74426 4.90249 3.31599 3.17544 2.43321 1.79314 4.74426 4.89897 3.23821 2.71117 2.49108 1.83302 4.74426 4.90278 3.4706 1.39353 2.41126 1.80224 4.74426 4.89609 3.44697 2.01672 2.37411 1.81047 4.74426 4.89689 3.39451 2.69781 2.36352 1.81523 4.74426 4.90401 3.40086 2.05652 2.43732 1.80734 4.74426 4.89975 3.46363 2.32011 2.36978 1.82103 4.74426 4.90351 3.48524 1.69183 2.40341 1.81571 4.74426 4.90487 3.35579 3.70289 2.36092 1.82401 4.74426 4.89737 3.27914 3.22004 2.37268 1.78951 4.74426 4.90397 3.46478 4.1739 2.41396 1.79158 4.74426 4.89754 3.357 3.9362 2.40835 1.79842 4.74426 4.89511 3.28149 20.9567 2.411 1.81224 4.74426 4.90337 3.21886 4.07076 2.69309 1.9758 4.74426 4.90697 5.02601 7.35977 2.58242 1.93576 4.74426 4.90033 4.97498 4.59998 2.6468 1.96365 4.74426 4.90717 4.87581 3.23707 2.62443 1.99464 4.74426 4.90922 4.83463 3.27846 2.65764 1.97042 4.74426 4.90432 5.04858 7.43036 2.60783 1.96397 4.74426 4.90245 4.72501 2.44681 2.59649 2.00117 4.74426 4.9075 4.49056 1.00844 2.51208 1.98914 4.74426 4.90105 4.27285 2.67284 2.53862 1.95182 4.74426 4.90102 4.08314 1.85009 2.55026 1.95059 4.74426 4.90479 4.22682 2.73625 2.54788 1.9373 4.74426 4.90338 4.03415 1.99492 2.6757 1.95216 4.74426 4.90712 4.01786 1.32531 2.535 1.9329 4.74426 4.90489 3.84376
[BAC H=1] Simple_MLP: blob_shape=(404, 1, 2), yt=ok, yp=ok [BAC H=1] Simple_KAN: blob_shape=(404, 1, 2), yt=ok, yp=ok [BAC H=1] Simple_LSTM: blob_shape=(404, 1, 2), yt=ok, yp=ok [BAC H=1] ITransformer: blob_shape=(404, 1, 2), yt=ok, yp=ok [BAC H=1] GARCH: blob_shape=(405, 1, 2), yt=ok, yp=ok [BAC H=1] actual=YES [BAC H=1] Aligned series head (first 50 of 404 rows): Actual Simple_MLP_pred Simple_KAN_pred Simple_LSTM_pred ITransformer_pred GARCH_pred 2.28991 2.02336 1.73527 1.5972 2.44942 2.73077 2.58025 2.06892 2.12154 2.11232 2.49127 2.75432 1.43685 2.04514 1.95678 2.30313 2.67354 2.74301 1.40532 2.01722 2.1163 1.78249 2.45183 2.65866 1.62484 1.94887 1.56064 1.74315 2.44583 2.61076 2.44658 2.07675 1.89847 1.82515 2.40728 2.59086 1.56274 2.10387 2.08499 2.20721 2.53411 2.49363 2.04251 1.98151 1.82326 1.82682 2.45975 2.87667 1.92693 2.06384 2.02819 2.03837 2.45052 2.61658 1.4117 1.97268 2.14076 1.99633 2.41124 2.64318 1.18859 1.98168 1.92861 1.75103 2.47713 2.69384 1.43173 2.04341 1.79974 1.63223 2.41634 2.47313 1.53413 2.06611 1.98964 1.71944 2.47113 2.3782 2.43259 2.07033 1.96343 1.76596 2.45636 2.30235 1.83194 2.06666 2.09387 2.17792 2.48818 2.69504 1.89469 2.00668 1.69005 1.94211 2.43554 2.77048 2.37737 1.99361 1.89902 1.96875 2.38906 2.53222 2.50765 2.02966 1.88654 2.19447 2.61292 2.72332 3.17295 2.15411 2.13127 2.2863 2.50527 2.53004 2.17531 2.05204 2.29708 2.5914 2.47628 2.58693 3.21212 2.11462 2.27001 2.20376 2.46161 2.46054 2.57597 1.99568 2.3459 2.63956 2.55376 3.25291 2.49883 2.03455 2.35397 2.4364 2.49827 4.13039 2.39942 1.99811 1.96501 2.40204 2.54944 3.33988 5.25542 2.07806 2.34025 2.35177 2.53058 2.93201 5.78329 2.2962 3.26226 3.26361 2.59309 2.63988 2.47379 2.44071 4.14277 3.67952 2.58013 2.59445 2.97723 2.26007 2.94199 2.67419 2.54209 3.56252 2.52012 2.28593 2.84612 2.84614 2.57675 2.98333 10.1661 2.38063 2.62345 2.60599 2.62695 2.68306 3.21457 3.0891 4.46423 4.34188 2.69702 2.49628 8.54499 2.58295 3.60336 3.22415 2.64541 2.48623 4.49535 3.01345 5.30069 4.64887 2.87203 3.20644 4.60281 3.07994 4.95801 4.13997 2.73257 2.95488 3.86461 3.05331 3.85276 4.04535 2.7279 2.85277 3.30898 2.76997 3.79204 3.73373 2.72473 3.36461 2.6722 2.80854 3.99353 3.42756 2.75782 3.98841 2.11101 2.39098 3.59044 3.03333 2.55986 3.29871 4.7242 2.24275 2.54688 2.61789 2.61351 2.9143 3.81735 2.94404 4.33871 3.53741 2.62013 3.39596 2.94351 2.53607 3.77452 3.45136 2.60619 4.76045 2.42652 2.68931 3.13097 3.08957 2.69582 4.46498 2.96869 2.2135 3.19559 2.73903 2.57388 4.6013 2.88004 2.65854 3.27197 2.90853 2.67116 3.68426 7.20205 2.2828 2.7364 2.86048 2.61076 6.28214 3.87685 2.95067 4.56107 4.00392 2.79416 6.06831 2.27628 2.56683 3.99099 3.58366 2.69483 4.53792 2.08444 2.41245 3.28472 2.78562 2.64155 3.65512 3.13418 2.16144 2.46892 2.48914 2.64696 3.19519 3.92102 2.35349 3.09289 2.90845 2.66943 3.03577
[C H=1] Simple_MLP: blob_shape=(404, 1, 2), yt=ok, yp=ok [C H=1] Simple_KAN: blob_shape=(404, 1, 2), yt=ok, yp=ok [C H=1] Simple_LSTM: blob_shape=(404, 1, 2), yt=ok, yp=ok [C H=1] ITransformer: blob_shape=(404, 1, 2), yt=ok, yp=ok [C H=1] GARCH: blob_shape=(405, 1, 2), yt=ok, yp=ok [C H=1] actual=YES [C H=1] Aligned series head (first 50 of 404 rows): Actual Simple_MLP_pred Simple_KAN_pred Simple_LSTM_pred ITransformer_pred GARCH_pred 1.4361 2.05949 1.81032 1.77662 2.85181 2.58234 2.4089 2.13617 1.73451 1.8528 2.72087 2.58922 1.70682 1.97474 1.85658 2.10131 2.7283 2.54011 1.3802 2.03162 1.93442 1.95772 2.53394 2.46833 1.42651 1.8034 1.69791 1.86546 2.74602 2.61937 2.28623 1.60093 1.68659 1.86196 2.66537 2.51988 3.09043 2.00873 2.24761 2.07131 2.53103 2.45639 3.47712 2.28061 2.34615 2.3464 2.77401 3.15532 2.10921 2.43519 2.38761 2.53311 3.04835 3.17288 3.05691 1.79762 2.04992 2.15512 2.50703 3.2325 1.32975 2.1153 2.5041 2.39922 2.5995 3.03437 1.00699 2.21268 2.05861 1.9418 2.76617 2.8002 1.56839 1.82169 1.97584 1.812 2.75113 2.66095 1.79976 1.75211 1.68473 1.90672 2.76134 2.59247 3.40343 1.68992 1.89359 1.96826 2.678 2.64991 2.66085 1.69591 1.8453 2.43565 2.76671 2.8276 3.50115 1.71656 1.84266 2.29401 2.53176 2.68283 2.93494 1.78966 2.00558 2.55749 2.74792 2.8742 3.77812 1.92783 2.16165 2.43403 2.81849 2.72687 2.79098 2.52679 2.7315 2.71071 2.87076 2.89362 2.48685 2.64495 2.61462 2.44295 2.58238 2.73748 1.80238 2.16618 2.35058 2.31291 2.85845 2.76145 4.05301 2.21246 2.2845 2.08715 2.87287 2.82942 2.247 2.57034 2.37631 2.74863 2.80395 2.695 3.71379 2.05942 2.28896 2.27083 2.91996 2.59339 4.11532 2.6942 2.49875 2.68286 2.72655 2.80695 3.12293 2.4156 2.66076 2.90458 2.8436 2.68858 2.66388 2.33024 2.56119 2.62207 2.85725 2.87006 5.69437 2.06756 2.55189 2.43296 2.7587 2.71265 9.77504 2.88279 3.21622 3.4128 2.8106 2.58721 4.82663 4.88662 4.3103 4.69949 2.90636 2.50811 5.00513 3.39613 4.04973 3.81661 2.88156 2.54929 4.18801 3.81033 4.2351 3.76236 2.85919 2.51673 2.87579 3.23046 4.02031 3.52093 2.92248 2.70154 3.95956 2.92423 3.4419 2.99555 2.86596 2.65958 2.62006 2.66915 3.1512 3.30196 2.80821 2.6259 1.79606 2.3638 2.91654 2.83876 2.84444 2.8674 2.26218 1.96569 2.46923 2.44145 2.85634 2.69901 6.96561 2.25966 2.43855 2.49864 2.81789 2.61602 3.21371 2.98949 3.21443 4.04928 2.83325 2.88442 3.60813 2.43714 2.97473 3.12445 2.91287 3.40004 2.14357 2.80448 3.03632 3.13934 2.91315 3.39642 2.83844 2.47278 2.83792 2.59734 2.84376 3.26715 2.47451 2.11743 2.54071 2.73929 2.75065 2.98448 5.06039 2.53872 2.50641 2.60048 2.88165 4.55295 2.61845 2.86881 2.91839 3.47909 2.88161 4.37708 1.97985 2.38972 2.81659 2.75621 2.9485 3.76624 2.24 2.37572 2.51994 2.41292 2.70812 3.31658 5.28614 2.22935 2.63937 2.41857 2.80805 3.01539 1.59823 2.78158 3.12055 3.45286 2.91193 2.83713
[BTCUSDT H=1] Simple_MLP: blob_shape=(480, 1, 2), yt=ok, yp=ok [BTCUSDT H=1] Simple_KAN: blob_shape=(480, 1, 2), yt=ok, yp=ok [BTCUSDT H=1] Simple_LSTM: blob_shape=(480, 1, 2), yt=ok, yp=ok [BTCUSDT H=1] ITransformer: blob_shape=(480, 1, 2), yt=ok, yp=ok [BTCUSDT H=1] GARCH: blob_shape=(481, 1, 2), yt=ok, yp=ok [BTCUSDT H=1] actual=YES [BTCUSDT H=1] Aligned series head (first 50 of 480 rows): Actual Simple_MLP_pred Simple_KAN_pred Simple_LSTM_pred ITransformer_pred GARCH_pred 6.92748 4.22565 4.10645 5.10271 6.41839 15.4974 1.26781 2.53354 2.39846 5.22677 6.85443 8.51014 0.752504 1.78268 1.95099 3.08665 6.33748 7.38625 10.0004 1.94661 4.10667 2.65069 6.29678 6.29784 8.45084 6.71705 10.4203 6.45995 7.01173 25.0203 5.32244 4.95906 7.3401 5.44647 6.36781 9.79193 2.80991 3.91033 4.94077 3.87555 6.41213 6.91373 5.32016 1.93833 2.76152 2.91372 6.26131 6.5617 1.27538 1.15078 0.976106 4.02572 6.22643 18.5139 3.3705 1.161 1.47332 2.66683 6.11175 7.57246 4.30039 2.70718 2.80168 3.09955 6.45395 6.46378 4.5337 3.63901 4.29719 3.25724 6.30434 11.047 6.04033 2.61462 2.48211 3.14142 6.37096 7.17486 4.38553 3.02479 2.99407 3.65192 6.48342 6.85069 3.19392 2.1905 3.3258 3.06988 6.1683 6.55304 0.828641 1.43063 1.24625 2.7115 6.4729 5.96814 1.72163 1.34968 1.50251 2.12012 6.0891 6.23868 3.29458 2.23288 4.67267 2.25291 6.37708 6.89264 2.52403 2.47752 6.42169 2.63118 6.2018 6.04899 3.66699 3.9244 4.74214 2.23734 6.42311 6.43847 3.61632 3.08895 3.06748 2.3893 6.20587 10.8539 8.21189 3.36716 4.40629 2.36401 6.15734 8.84327 2.21355 2.0948 1.53782 4.4513 5.83558 16.0553 2.72723 1.55792 1.4317 2.31031 6.45938 7.62243 6.91185 2.95095 2.99416 2.32257 6.24184 6.70523 7.1909 5.44351 4.18534 4.25197 6.57445 12.2698 6.48986 5.28778 4.08057 4.41681 6.4879 6.69666 6.82806 4.18417 5.70952 4.04379 6.3712 15.8625 4.04652 4.48997 4.52335 4.26598 6.32422 7.10354 3.11243 1.63272 1.44161 3.32494 5.77862 6.58045 2.5171 2.04086 1.76629 3.07009 5.72366 7.24674 2.75687 2.45268 3.18061 2.88489 5.99425 6.82247 6.87132 4.49213 8.17688 2.83494 6.06518 7.08597 4.43353 4.77557 6.34874 4.35616 6.34793 6.88433 4.92989 3.85498 5.4101 3.18841 6.61877 6.99391 3.08011 1.93112 2.61154 3.20835 5.65607 7.55735 1.10511 1.47487 0.878115 2.66455 6.2181 7.4489 1.37997 1.34808 1.55578 2.169 5.75637 7.52075 6.66109 1.86834 2.54087 2.17931 6.08828 6.40363 13.9372 5.71324 7.84822 4.19853 6.47281 22.1983 26.0966 9.03072 9.91963 6.71274 6.21498 19.8492 18.2381 12.6697 14.0678 9.86168 6.31501 61.544 8.3292 9.65288 10.5323 9.43658 6.88901 14.8119 2.68908 3.19934 4.82262 6.44326 6.57973 9.30873 3.12688 2.71336 4.49923 4.63277 6.23692 7.2574 23.8375 4.86806 8.23276 5.40988 6.3905 8.01182 50.1271 12.6143 22.3594 11.946 6.82711 44.5863 25.8546 19.1892 33.43 20.0921 7.33057 46.8263 9.41359 16.2393 18.7977 15.5296 7.02566 18.0017 16.7212 8.70803 10.8569 9.34476 7.12477 7.66735
[GOLD H=1] Simple_MLP: blob_shape=(1107, 1, 2), yt=ok, yp=ok [GOLD H=1] Simple_KAN: blob_shape=(1107, 1, 2), yt=ok, yp=ok [GOLD H=1] Simple_LSTM: blob_shape=(1107, 1, 2), yt=ok, yp=ok [GOLD H=1] ITransformer: blob_shape=(1107, 1, 2), yt=ok, yp=ok [GOLD H=1] GARCH: blob_shape=(1108, 1, 2), yt=ok, yp=ok [GOLD H=1] actual=YES [GOLD H=1] Aligned series head (first 50 of 1107 rows): Actual Simple_MLP_pred Simple_KAN_pred Simple_LSTM_pred ITransformer_pred GARCH_pred 0.983207 0.422219 0.23986 0.571752 0.208339 0.685881 0.0725466 0.431938 0.0207378 0.0498391 0.236039 0.680604 0.365228 0.428538 0.378341 0.539055 0.180121 0.658805 0.675363 0.604657 0.674003 0.596529 0.242638 0.639727 1.19577 0.382046 0.752431 0.74542 0.165433 0.638002 0.396614 0.619541 0.533796 0.858823 0.306933 0.723233 0.569428 0.414257 0.272703 0.674106 0.329168 0.718785 0.0394832 0.581621 0.348653 0.0488642 0.366324 0.779457 0.540289 0.394882 0.295553 0.460947 0.231783 0.76829 0.645375 0.431192 0.491561 0.594452 0.215055 0.765542 0.596069 0.567287 0.517611 0.699126 0.256235 0.74924 0.496734 0.457365 0.507105 0.632185 0.241731 0.743014 0.986594 0.380398 0.453661 0.546185 0.186387 0.730231 0.0170706 0.0477885 0.0228274 0.0441966 0.169985 0.729788 0.388559 0.503325 0.338773 0.464354 0.233847 0.727037 0.616087 0.494338 0.524899 0.547338 0.195596 0.738527 0.603893 0.511328 0.591502 0.696161 0.253194 0.758962 0.739671 0.45313 0.521339 0.64251 0.220937 0.764986 0.832053 0.475963 0.474508 0.647189 0.220221 0.773268 0.0110286 0.0358669 0.0225909 0.0456166 0.196558 0.810121 0.506412 0.491655 0.33213 0.47311 0.20611 0.786416 0.392005 0.441145 0.473468 0.607242 0.236699 0.655122 0.80787 0.541264 0.520275 0.664098 0.228026 0.800724 0.535457 0.472579 0.604974 0.669592 0.233856 0.829303 0.837414 0.496366 0.439857 0.590027 0.206479 0.858639 0.0220968 0.0442664 0.0173706 0.0427142 0.181072 0.89187 0.498523 0.488293 0.390587 0.449317 0.236574 0.823867 0.472977 0.427078 0.399705 0.587656 0.207432 0.805548 1.61389 0.505716 0.470363 0.680659 0.234607 0.835249 0.641823 0.555861 0.971117 0.892287 0.228295 0.859374 0.600193 0.529455 0.56877 0.703771 0.248334 0.894679 0.0226182 0.0747826 0.0285722 0.0438857 0.205992 0.837805 0.411008 0.537367 0.422029 0.465519 0.19087 0.787663 0.743739 0.432418 0.493226 0.554816 0.232542 0.807656 0.369762 0.578067 0.607623 0.708233 0.262809 0.824197 0.57285 0.448126 0.568941 0.572638 0.26489 0.801787 1.20635 0.417208 0.533148 0.561875 0.196172 0.824994 0.0659951 0.0432381 0.0272632 0.0460365 0.184604 0.804215 1.33651 0.485215 0.40125 0.506112 0.233862 0.816732 0.970258 0.556679 0.915238 0.920184 0.228134 0.996608 0.553171 0.661553 0.771861 0.950451 0.27655 1.00121 0.193903 0.509757 0.660033 0.805476 0.243418 0.919109 1.26963 0.435644 0.442191 0.541587 0.286094 0.911807 0.222961 0.102008 0.0309558 0.0638977 0.20381 0.873841 0.483934 0.522787 0.520647 0.710576 0.248931 0.875288 1.3403 0.428168 0.564224 0.718952 0.284976 0.834209 1.06602 0.74433 0.881867 0.876445 0.249181 0.86112 0.692459 0.242818 0.517915 0.902223 0.23706 0.886151 0.757949 0.521484 0.435654 0.7749 0.197776 0.856254 0.0295473 0.114105 0.0548944 0.0586249 0.238353 0.835811
[SP500 H=1] Simple_MLP: blob_shape=(694, 1, 2), yt=ok, yp=ok [SP500 H=1] Simple_KAN: blob_shape=(694, 1, 2), yt=ok, yp=ok [SP500 H=1] Simple_LSTM: blob_shape=(694, 1, 2), yt=ok, yp=ok [SP500 H=1] ITransformer: blob_shape=(694, 1, 2), yt=ok, yp=ok [SP500 H=1] GARCH: blob_shape=(695, 1, 2), yt=ok, yp=ok [SP500 H=1] actual=YES [SP500 H=1] Aligned series head (first 50 of 694 rows): Actual Simple_MLP_pred Simple_KAN_pred Simple_LSTM_pred ITransformer_pred GARCH_pred 1.0351 1.09275 1.18393 1.55687 0.394359 0.799467 1.03491 1.80206 1.3492 1.08629 0.422129 0.796043 0.818223 1.54565 1.0302 0.897907 0.396443 0.809952 0.0217497 0.045146 0.3984 0.0767051 0.365226 0.757815 0.502587 0.269231 0.582129 0.760349 0.354194 0.704544 2.13502 0.890721 0.512209 0.672992 0.347545 0.820297 0.903485 2.20735 1.04372 1.57803 0.377749 0.765318 0.898743 0.903324 1.13008 0.958117 0.43054 0.72972 0.904151 0.568081 0.737492 0.823811 0.385761 0.906819 0.0405714 0.0321466 0.2803 0.0693383 0.311338 0.852453 0.113815 0.334695 0.628761 0.804945 0.336537 0.796007 0.60793 0.820503 0.793502 0.402075 0.326805 0.742223 0.92855 0.655194 0.706013 0.523316 0.391597 0.894725 0.794209 0.699873 0.781745 0.788148 0.381961 0.834901 0.910428 0.478748 0.496577 0.624998 0.350679 0.782076 0.0104435 0.0656389 0.201243 0.0478977 0.334912 0.795224 0.610551 0.16914 0.401153 0.684136 0.3342 0.741294 0.532918 0.841029 0.945009 0.639523 0.366766 0.708376 0.832645 0.635857 0.881733 0.668783 0.34164 0.705748 0.902649 0.898833 0.879433 0.737364 0.361889 0.666399 0.540703 0.551887 0.660904 0.72802 0.315444 0.646531 0.0320152 0.0265908 0.119123 0.0430875 0.314461 0.854385 0.44739 0.510448 0.404193 0.412501 0.333362 0.805102 1.17379 0.574764 0.761066 0.489611 0.350494 0.763888 0.784725 1.34082 0.875697 1.01457 0.367478 0.887074 1.19858 1.01257 0.906342 0.7192 0.338609 0.836034 3.1834 0.551616 0.599364 0.97001 0.330019 1.06951 1.95774 0.0475945 0.500037 0.213057 0.323595 1.11032 5.83359 1.33327 1.14272 1.69194 0.340549 1.15077 1.91438 4.60719 1.89317 3.38312 0.490163 1.17816 2.82295 3.51623 1.78432 2.36367 0.427311 1.27191 1.97836 3.05436 1.80666 2.40322 0.485227 1.22346 1.67848 1.67403 1.72195 1.6257 0.423387 1.39471 0.231827 0.581981 1.21457 1.58846 0.422853 1.37889 1.99306 1.20666 1.47822 0.88852 0.448867 1.31051 0.595525 1.55252 1.33087 1.35574 0.417849 1.23252 2.0958 1.25003 1.16848 1.06767 0.429391 1.2776 1.44715 1.61855 1.19434 1.61631 0.432833 1.36154 1.8779 1.32908 1.15891 1.09801 0.400154 1.27896 0.0836623 0.0554083 0.599128 0.190105 0.366675 1.21785 0.723351 1.22303 1.27175 1.21298 0.426686 1.15966 0.463055 0.791861 0.843901 1.00817 0.368699 1.09123 0.331959 0.93101 1.08082 0.923874 0.430165 1.02859 0.355102 0.592384 0.816007 0.567945 0.356518 1.04923 0.359515 0.699186 0.686743 0.237356 0.388032 1.03438 0.0698865 0.155076 0.120673 0.0445153 0.355785 1.09259 0.418851 0.417567 0.626095 0.38103 0.340955 1.02865 0.479569 0.614998 0.507444 0.552917 0.338702 0.988313 0.468942 0.757813 0.605931 0.603309 0.416061 0.937865 0.278331 0.442242 0.546351 0.496965 0.319164 0.897153
Results on models with more inputs¶
In [823]:
load_data_object_11_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_11.pkl")
with open(load_data_object_11_file_path, "rb") as f:
structured_data_dict_11 = pickle.load(f)
print("Data dictionary 11 loaded successfully.")
X_price_map, X_time_map, y_map = {}, {}, {}
missing = []
for t in tickers:
if t not in structured_data_dict_11:
missing.append(t)
continue
entry = structured_data_dict_11[t]
Xp = entry.get("X_other", None)
Xt = entry.get("X_time", None)
Y = entry.get("y", None)
if Xp is None or Y is None:
print(f"[WARN] {t}: missing {'X_other' if Xp is None else ''}"
f"{' and ' if (Xp is None and Y is None) else ''}"
f"{'y' if Y is None else ''} → skipping.")
continue
Xp = np.asarray(Xp, dtype=float)
Xt = None if Xt is None else np.asarray(Xt, dtype=float)
Y = np.asarray(Y, dtype=float)
if Xp.ndim != 3 or Y.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_other={Xp.shape}, y={Y.shape} → skipping.")
continue
if Xt is not None and Xt.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_time={Xt.shape} → setting to None.")
Xt = None
X_price_map[t] = Xp
X_time_map[t] = Xt
y_map[t] = Y
if missing:
print(f"[INFO] Missing tickers in data dict (skipped): {missing}")
multi_input_results_store = {}
MULTI_INPUT_BASE_SAVE_DIR = os.path.join(root_folder, objects_relative_path, "multi_input_results_saved_object")
os.makedirs(MULTI_INPUT_BASE_SAVE_DIR, exist_ok=True)
mutiple_inputs_models = ["Simple_KAN"]
_ = run_all_models_for_all(
tickers=final_tickers,
horizons=final_horizons,
model_list=mutiple_inputs_models,
X_price_map=X_price_map,
X_time_map=X_time_map,
y_map=y_map,
base_save_dir=MULTI_INPUT_BASE_SAVE_DIR,
fixed_params=FIXED_PARAMS_MERGED,
common_params=COMMON,
model_io=MODEL_IO,
multitask_models=MULTI_TASK_MODELS,
param_grids=None,
results_store=multi_input_results_store,
overwrite=False
)
Data dictionary 11 loaded successfully. === AAPL | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714034 Min value: -10.367763820153055 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.13326641523511 Min value: -2.354189421011421 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.5738558883507405 Min value: -10.367763820153055 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.906984328061974 Min value: -1.6295009124362931 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.9402408075062174 Min value: -10.367763820153055 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.8950185841626346 Min value: -2.119931409321887 Epoch 1: Train mse = 0.9633 | Val mse = 0.6433 Epoch 2: Train mse = 0.7840 | Val mse = 0.5237 Epoch 3: Train mse = 0.6934 | Val mse = 1.0252 Epoch 4: Train mse = 0.6236 | Val mse = 0.8990 Epoch 5: Train mse = 0.5647 | Val mse = 1.3106 Epoch 6: Train mse = 0.5361 | Val mse = 1.5061 Epoch 7: Train mse = 0.4902 | Val mse = 1.6780 Epoch 8: Train mse = 0.4502 | Val mse = 2.0699 Epoch 9: Train mse = 0.4115 | Val mse = 2.2300 Epoch 10: Train mse = 0.3920 | Val mse = 2.6834 Epoch 11: Train mse = 0.3711 | Val mse = 2.2590 Epoch 12: Train mse = 0.3381 | Val mse = 2.5199 Epoch 13: Train mse = 0.3218 | Val mse = 3.0646 Epoch 14: Train mse = 0.2947 | Val mse = 2.8705 Epoch 15: Train mse = 0.2788 | Val mse = 2.7621 Epoch 16: Train mse = 0.2621 | Val mse = 2.9090 Epoch 17: Train mse = 0.2421 | Val mse = 3.5889 Epoch 18: Train mse = 0.2289 | Val mse = 3.4238 Epoch 19: Train mse = 0.2233 | Val mse = 3.7965 Epoch 20: Train mse = 0.2078 | Val mse = 3.3491 Epoch 21: Train mse = 0.1950 | Val mse = 3.5481 Epoch 22: Train mse = 0.1733 | Val mse = 3.4025 Epoch 23: Train mse = 0.1689 | Val mse = 3.9105 Epoch 24: Train mse = 0.1560 | Val mse = 4.1867 Epoch 25: Train mse = 0.1427 | Val mse = 4.6966 Epoch 26: Train mse = 0.1383 | Val mse = 4.7542 Epoch 27: Train mse = 0.1436 | Val mse = 4.5108 Epoch 28: Train mse = 0.1210 | Val mse = 3.8529 Epoch 29: Train mse = 0.1113 | Val mse = 3.4801 Epoch 30: Train mse = 0.0962 | Val mse = 3.6740 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.46251029 1 day(s) RMSE : 8.91392386 1 day(s) R2 : -0.06377748 1 day(s) Pearson r : -0.09094064 1 day(s) QLIKE : 0.57576874 full horizon MAE : 2.46251029 full horizon RMSE : 8.91392386 full horizon R2 : -0.06377748 full horizon Pearson r : -0.09094064 full horizon QLIKE : 0.57576874 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/AAPL/Simple_KAN_H1.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=1.15621, max=2.02448 === AAPL | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714034 Min value: -10.367763820153055 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.140231129276094 Min value: -2.358314580069225 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.5738558883507405 Min value: -10.367763820153055 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.907938252663739 Min value: -1.632926464574096 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.9402408075062174 Min value: -10.367763820153055 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.896926346992255 Min value: -2.123830418076347 Epoch 1: Train mse = 0.9487 | Val mse = 0.9255 Epoch 2: Train mse = 0.7751 | Val mse = 0.4594 Epoch 3: Train mse = 0.7121 | Val mse = 1.2533 Epoch 4: Train mse = 0.6457 | Val mse = 1.2468 Epoch 5: Train mse = 0.5652 | Val mse = 1.5448 Epoch 6: Train mse = 0.5087 | Val mse = 2.1187 Epoch 7: Train mse = 0.4671 | Val mse = 2.8582 Epoch 8: Train mse = 0.4413 | Val mse = 2.4983 Epoch 9: Train mse = 0.4452 | Val mse = 1.8795 Epoch 10: Train mse = 0.4194 | Val mse = 2.3950 Epoch 11: Train mse = 0.4043 | Val mse = 2.3962 Epoch 12: Train mse = 0.3834 | Val mse = 1.9825 Epoch 13: Train mse = 0.3717 | Val mse = 2.5739 Epoch 14: Train mse = 0.3576 | Val mse = 2.3845 Epoch 15: Train mse = 0.3487 | Val mse = 2.3354 Epoch 16: Train mse = 0.3379 | Val mse = 2.6594 Epoch 17: Train mse = 0.3321 | Val mse = 2.2352 Epoch 18: Train mse = 0.3302 | Val mse = 2.3320 Epoch 19: Train mse = 0.3207 | Val mse = 2.5188 Epoch 20: Train mse = 0.3164 | Val mse = 2.5334 Epoch 21: Train mse = 0.3035 | Val mse = 2.2577 Epoch 22: Train mse = 0.2929 | Val mse = 2.4497 Epoch 23: Train mse = 0.2912 | Val mse = 2.2135 Epoch 24: Train mse = 0.2831 | Val mse = 2.0641 Epoch 25: Train mse = 0.2717 | Val mse = 2.0751 Epoch 26: Train mse = 0.2629 | Val mse = 2.2712 Epoch 27: Train mse = 0.2542 | Val mse = 2.1547 Epoch 28: Train mse = 0.2444 | Val mse = 2.3112 Epoch 29: Train mse = 0.2377 | Val mse = 2.2295 Epoch 30: Train mse = 0.2297 | Val mse = 2.3981 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.42054486 1 day(s) RMSE : 8.88848184 1 day(s) R2 : -0.05771370 1 day(s) Pearson r : 0.02753822 1 day(s) QLIKE : 0.53149894 3 day(s) MAE : 2.48963717 3 day(s) RMSE : 9.13267811 3 day(s) R2 : -0.05539978 3 day(s) Pearson r : 0.02733509 3 day(s) QLIKE : 0.54992576 5 day(s) MAE : 2.52338940 5 day(s) RMSE : 9.19584237 5 day(s) R2 : -0.05824183 5 day(s) Pearson r : 0.00822495 5 day(s) QLIKE : 0.55751983 full horizon MAE : 2.52338940 full horizon RMSE : 9.19584237 full horizon R2 : -0.05824183 full horizon Pearson r : 0.00822495 full horizon QLIKE : 0.55751983 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/AAPL/Simple_KAN_H5.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=1.20653, max=1.82501 === AAPL | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714034 Min value: -10.367763820153055 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.14985814599714 Min value: -2.3634631558377808 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.5738558883507405 Min value: -10.367763820153055 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.909556794039435 Min value: -1.6371429190585964 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.9402408075062174 Min value: -10.367763820153055 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.8998157347365474 Min value: -2.1286776824868885 Epoch 1: Train mse = 0.9547 | Val mse = 1.1972 Epoch 2: Train mse = 0.8281 | Val mse = 0.4714 Epoch 3: Train mse = 0.7662 | Val mse = 0.9059 Epoch 4: Train mse = 0.6882 | Val mse = 1.6323 Epoch 5: Train mse = 0.6384 | Val mse = 1.7941 Epoch 6: Train mse = 0.5806 | Val mse = 1.4152 Epoch 7: Train mse = 0.5398 | Val mse = 1.7945 Epoch 8: Train mse = 0.5054 | Val mse = 1.8864 Epoch 9: Train mse = 0.4825 | Val mse = 2.4531 Epoch 10: Train mse = 0.4532 | Val mse = 2.5392 Epoch 11: Train mse = 0.4356 | Val mse = 2.5550 Epoch 12: Train mse = 0.4249 | Val mse = 2.4789 Epoch 13: Train mse = 0.4135 | Val mse = 2.8994 Epoch 14: Train mse = 0.4054 | Val mse = 2.7287 Epoch 15: Train mse = 0.3949 | Val mse = 2.6257 Epoch 16: Train mse = 0.3810 | Val mse = 2.3369 Epoch 17: Train mse = 0.3769 | Val mse = 2.4368 Epoch 18: Train mse = 0.3644 | Val mse = 2.4593 Epoch 19: Train mse = 0.3490 | Val mse = 2.5198 Epoch 20: Train mse = 0.3404 | Val mse = 2.3580 Epoch 21: Train mse = 0.3291 | Val mse = 2.3953 Epoch 22: Train mse = 0.3214 | Val mse = 2.4326 Epoch 23: Train mse = 0.3189 | Val mse = 2.6236 Epoch 24: Train mse = 0.3098 | Val mse = 2.4413 Epoch 25: Train mse = 0.3020 | Val mse = 2.3883 Epoch 26: Train mse = 0.2987 | Val mse = 2.2625 Epoch 27: Train mse = 0.2929 | Val mse = 2.3990 Epoch 28: Train mse = 0.2877 | Val mse = 2.3672 Epoch 29: Train mse = 0.2800 | Val mse = 2.2357 Epoch 30: Train mse = 0.2748 | Val mse = 2.3463 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.45663669 1 day(s) RMSE : 8.90652078 1 day(s) R2 : -0.06201126 1 day(s) Pearson r : -0.02446050 1 day(s) QLIKE : 0.54500584 3 day(s) MAE : 2.50791224 3 day(s) RMSE : 9.13810199 3 day(s) R2 : -0.05665375 3 day(s) Pearson r : -0.01859557 3 day(s) QLIKE : 0.56133092 5 day(s) MAE : 2.52533396 5 day(s) RMSE : 9.18665284 5 day(s) R2 : -0.05612786 5 day(s) Pearson r : -0.02795649 5 day(s) QLIKE : 0.56631284 10 day(s) MAE : 2.56554233 10 day(s) RMSE : 9.24094562 10 day(s) R2 : -0.05985769 10 day(s) Pearson r : -0.04688197 10 day(s) QLIKE : 0.57715424 full horizon MAE : 2.56554233 full horizon RMSE : 9.24094562 full horizon R2 : -0.05985769 full horizon Pearson r : -0.04688197 full horizon QLIKE : 0.57715424 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/AAPL/Simple_KAN_H10.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=1.06221, max=1.94837 === AAPL | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.099874372714034 Min value: -10.367763820153055 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.17404540383974 Min value: -2.37751047680313 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.5738558883507405 Min value: -10.367763820153055 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9130206404899854 Min value: -1.6487782037743786 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.9402408075062174 Min value: -10.367763820153055 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.906568131251477 Min value: -2.14194530457574 Epoch 1: Train mse = 0.9645 | Val mse = 1.1664 Epoch 2: Train mse = 0.8362 | Val mse = 0.5216 Epoch 3: Train mse = 0.7714 | Val mse = 0.8549 Epoch 4: Train mse = 0.7125 | Val mse = 1.5340 Epoch 5: Train mse = 0.6542 | Val mse = 1.2953 Epoch 6: Train mse = 0.6011 | Val mse = 1.9058 Epoch 7: Train mse = 0.5590 | Val mse = 2.4031 Epoch 8: Train mse = 0.5326 | Val mse = 2.6154 Epoch 9: Train mse = 0.5270 | Val mse = 2.1960 Epoch 10: Train mse = 0.5055 | Val mse = 2.6145 Epoch 11: Train mse = 0.4870 | Val mse = 2.3444 Epoch 12: Train mse = 0.4686 | Val mse = 2.1335 Epoch 13: Train mse = 0.4490 | Val mse = 2.4713 Epoch 14: Train mse = 0.4331 | Val mse = 2.1391 Epoch 15: Train mse = 0.4198 | Val mse = 2.1328 Epoch 16: Train mse = 0.4052 | Val mse = 2.1963 Epoch 17: Train mse = 0.4027 | Val mse = 1.9930 Epoch 18: Train mse = 0.3914 | Val mse = 1.9027 Epoch 19: Train mse = 0.3854 | Val mse = 1.9867 Epoch 20: Train mse = 0.3787 | Val mse = 1.9238 Epoch 21: Train mse = 0.3702 | Val mse = 1.8438 Epoch 22: Train mse = 0.3587 | Val mse = 2.0834 Epoch 23: Train mse = 0.3536 | Val mse = 1.8663 Epoch 24: Train mse = 0.3429 | Val mse = 2.1530 Epoch 25: Train mse = 0.3356 | Val mse = 1.9702 Epoch 26: Train mse = 0.3320 | Val mse = 2.0708 Epoch 27: Train mse = 0.3304 | Val mse = 2.1160 Epoch 28: Train mse = 0.3254 | Val mse = 2.3419 Epoch 29: Train mse = 0.3175 | Val mse = 2.0250 Epoch 30: Train mse = 0.3120 | Val mse = 2.1874 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.41009828 1 day(s) RMSE : 8.86153059 1 day(s) R2 : -0.05130912 1 day(s) Pearson r : -0.07825994 1 day(s) QLIKE : 0.55783030 3 day(s) MAE : 2.49315904 3 day(s) RMSE : 9.12224975 3 day(s) R2 : -0.05299089 3 day(s) Pearson r : -0.06100949 3 day(s) QLIKE : 0.57087987 5 day(s) MAE : 2.51891002 5 day(s) RMSE : 9.17800031 5 day(s) R2 : -0.05413935 5 day(s) Pearson r : -0.07083658 5 day(s) QLIKE : 0.57605450 10 day(s) MAE : 2.55767078 10 day(s) RMSE : 9.22998776 10 day(s) R2 : -0.05734564 10 day(s) Pearson r : -0.07705607 10 day(s) QLIKE : 0.58552514 20 day(s) MAE : 2.60693213 20 day(s) RMSE : 9.26869046 20 day(s) R2 : -0.06169602 20 day(s) Pearson r : -0.10400603 20 day(s) QLIKE : 0.59590654 full horizon MAE : 2.60693213 full horizon RMSE : 9.26869046 full horizon R2 : -0.06169602 full horizon Pearson r : -0.10400603 full horizon QLIKE : 0.59590654 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/AAPL/Simple_KAN_H20.pkl Saved y_true min=0.314451, max=112.408 Saved y_pred min=1.16321, max=2.06414 === MSFT | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.662699184664 Min value: -6.772192238786855 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.2181198470171735 Min value: -2.80514498305857 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.716447710866394 Min value: -4.319177175086081 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.894759677334685 Min value: -1.3150487901568384 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.94429083559171 Min value: -6.772192238873022 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.930619914036178 Min value: -2.329935051243564 Epoch 1: Train mse = 0.9464 | Val mse = 2.5508 Epoch 2: Train mse = 0.7457 | Val mse = 0.5342 Epoch 3: Train mse = 0.6407 | Val mse = 0.5153 Epoch 4: Train mse = 0.5298 | Val mse = 0.8944 Epoch 5: Train mse = 0.4840 | Val mse = 0.6620 Epoch 6: Train mse = 0.4487 | Val mse = 0.8374 Epoch 7: Train mse = 0.4112 | Val mse = 0.9100 Epoch 8: Train mse = 0.3752 | Val mse = 0.7831 Epoch 9: Train mse = 0.3474 | Val mse = 1.2324 Epoch 10: Train mse = 0.3080 | Val mse = 1.4500 Epoch 11: Train mse = 0.2795 | Val mse = 1.2074 Epoch 12: Train mse = 0.2580 | Val mse = 1.3631 Epoch 13: Train mse = 0.2404 | Val mse = 1.3818 Epoch 14: Train mse = 0.2135 | Val mse = 1.8920 Epoch 15: Train mse = 0.1937 | Val mse = 1.5696 Epoch 16: Train mse = 0.1708 | Val mse = 2.0351 Epoch 17: Train mse = 0.1426 | Val mse = 1.7540 Epoch 18: Train mse = 0.1313 | Val mse = 1.7492 Epoch 19: Train mse = 0.1160 | Val mse = 1.9255 Epoch 20: Train mse = 0.1157 | Val mse = 2.0014 Epoch 21: Train mse = 0.0972 | Val mse = 1.4587 Epoch 22: Train mse = 0.0818 | Val mse = 2.0170 Epoch 23: Train mse = 0.0719 | Val mse = 1.9197 Epoch 24: Train mse = 0.0689 | Val mse = 1.7806 Epoch 25: Train mse = 0.0528 | Val mse = 1.8706 Epoch 26: Train mse = 0.0421 | Val mse = 2.4418 Epoch 27: Train mse = 0.0371 | Val mse = 2.0068 Epoch 28: Train mse = 0.0361 | Val mse = 2.0571 Epoch 29: Train mse = 0.0269 | Val mse = 2.1904 Epoch 30: Train mse = 0.0254 | Val mse = 2.0178 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.28082293 1 day(s) RMSE : 6.67158377 1 day(s) R2 : 0.00928429 1 day(s) Pearson r : 0.11014010 1 day(s) QLIKE : 0.44375689 full horizon MAE : 2.28082293 full horizon RMSE : 6.67158377 full horizon R2 : 0.00928429 full horizon Pearson r : 0.11014010 full horizon QLIKE : 0.44375689 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/MSFT/Simple_KAN_H1.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=1.00914, max=5.44325 === MSFT | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.662699184664 Min value: -6.772192238786855 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.21838735918136 Min value: -2.8084790413953242 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.716447710866394 Min value: -4.319177175086081 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.8943485628110612 Min value: -1.3176187186164257 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.94429083559171 Min value: -6.772192238873022 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.9307399945851973 Min value: -2.33302541920076 Epoch 1: Train mse = 0.9502 | Val mse = 1.1441 Epoch 2: Train mse = 0.7008 | Val mse = 0.6693 Epoch 3: Train mse = 0.6086 | Val mse = 0.8942 Epoch 4: Train mse = 0.5342 | Val mse = 1.2981 Epoch 5: Train mse = 0.5076 | Val mse = 0.8888 Epoch 6: Train mse = 0.4534 | Val mse = 0.9899 Epoch 7: Train mse = 0.4244 | Val mse = 1.4743 Epoch 8: Train mse = 0.3910 | Val mse = 1.1263 Epoch 9: Train mse = 0.3627 | Val mse = 1.0273 Epoch 10: Train mse = 0.3383 | Val mse = 1.4879 Epoch 11: Train mse = 0.3164 | Val mse = 1.0815 Epoch 12: Train mse = 0.3047 | Val mse = 1.3079 Epoch 13: Train mse = 0.2929 | Val mse = 1.6047 Epoch 14: Train mse = 0.2809 | Val mse = 1.3470 Epoch 15: Train mse = 0.2754 | Val mse = 1.3393 Epoch 16: Train mse = 0.2714 | Val mse = 1.3723 Epoch 17: Train mse = 0.2702 | Val mse = 1.5451 Epoch 18: Train mse = 0.2637 | Val mse = 1.3617 Epoch 19: Train mse = 0.2584 | Val mse = 1.2530 Epoch 20: Train mse = 0.2532 | Val mse = 1.2399 Epoch 21: Train mse = 0.2478 | Val mse = 1.4583 Epoch 22: Train mse = 0.2383 | Val mse = 1.4773 Epoch 23: Train mse = 0.2249 | Val mse = 1.3162 Epoch 24: Train mse = 0.2157 | Val mse = 1.4645 Epoch 25: Train mse = 0.2153 | Val mse = 1.3412 Epoch 26: Train mse = 0.1968 | Val mse = 1.6725 Epoch 27: Train mse = 0.1862 | Val mse = 1.4568 Epoch 28: Train mse = 0.1759 | Val mse = 1.7216 Epoch 29: Train mse = 0.1715 | Val mse = 1.4031 Epoch 30: Train mse = 0.1665 | Val mse = 1.5695 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.39517245 1 day(s) RMSE : 6.67166341 1 day(s) R2 : 0.00926064 1 day(s) Pearson r : 0.12178587 1 day(s) QLIKE : 0.46470677 3 day(s) MAE : 2.35933426 3 day(s) RMSE : 6.69124439 3 day(s) R2 : 0.00336994 3 day(s) Pearson r : 0.10083813 3 day(s) QLIKE : 0.47463995 5 day(s) MAE : 2.34231772 5 day(s) RMSE : 6.70674921 5 day(s) R2 : -0.00114183 5 day(s) Pearson r : 0.08674715 5 day(s) QLIKE : 0.47954383 full horizon MAE : 2.34231772 full horizon RMSE : 6.70674921 full horizon R2 : -0.00114183 full horizon Pearson r : 0.08674715 full horizon QLIKE : 0.47954383 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/MSFT/Simple_KAN_H5.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=0.880445, max=8.53311 === MSFT | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.662699184664 Min value: -6.772192238786855 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.2208777591762505 Min value: -2.8143642605456183 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.716447710866394 Min value: -4.319177175086081 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.8952607849867555 Min value: -1.3217269183173053 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.94429083559171 Min value: -6.772192238873022 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.9328875355250505 Min value: -2.3383439250775 Epoch 1: Train mse = 0.9649 | Val mse = 0.7150 Epoch 2: Train mse = 0.8071 | Val mse = 1.8943 Epoch 3: Train mse = 0.6883 | Val mse = 1.5450 Epoch 4: Train mse = 0.5950 | Val mse = 1.0970 Epoch 5: Train mse = 0.5474 | Val mse = 0.8891 Epoch 6: Train mse = 0.4975 | Val mse = 1.1555 Epoch 7: Train mse = 0.4541 | Val mse = 0.9412 Epoch 8: Train mse = 0.4257 | Val mse = 0.9446 Epoch 9: Train mse = 0.4030 | Val mse = 0.9475 Epoch 10: Train mse = 0.3829 | Val mse = 0.9147 Epoch 11: Train mse = 0.3704 | Val mse = 0.6103 Epoch 12: Train mse = 0.3619 | Val mse = 0.8391 Epoch 13: Train mse = 0.3527 | Val mse = 0.7281 Epoch 14: Train mse = 0.3455 | Val mse = 0.8450 Epoch 15: Train mse = 0.3429 | Val mse = 0.8047 Epoch 16: Train mse = 0.3372 | Val mse = 0.8783 Epoch 17: Train mse = 0.3295 | Val mse = 0.7924 Epoch 18: Train mse = 0.3153 | Val mse = 0.9629 Epoch 19: Train mse = 0.2980 | Val mse = 0.9318 Epoch 20: Train mse = 0.2962 | Val mse = 0.9906 Epoch 21: Train mse = 0.3006 | Val mse = 0.8321 Epoch 22: Train mse = 0.2851 | Val mse = 0.9577 Epoch 23: Train mse = 0.2732 | Val mse = 1.0359 Epoch 24: Train mse = 0.2626 | Val mse = 1.0197 Epoch 25: Train mse = 0.2585 | Val mse = 1.0168 Epoch 26: Train mse = 0.2512 | Val mse = 1.1794 Epoch 27: Train mse = 0.2458 | Val mse = 1.0681 Epoch 28: Train mse = 0.2412 | Val mse = 1.0768 Epoch 29: Train mse = 0.2356 | Val mse = 1.0413 Epoch 30: Train mse = 0.2304 | Val mse = 1.0863 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.18464942 1 day(s) RMSE : 6.76204032 1 day(s) R2 : -0.01776304 1 day(s) Pearson r : 0.06196167 1 day(s) QLIKE : 0.48083198 3 day(s) MAE : 2.20958431 3 day(s) RMSE : 6.78536804 3 day(s) R2 : -0.02486583 3 day(s) Pearson r : 0.05046515 3 day(s) QLIKE : 0.48511247 5 day(s) MAE : 2.22938742 5 day(s) RMSE : 6.80333859 5 day(s) R2 : -0.03018600 5 day(s) Pearson r : 0.04213740 5 day(s) QLIKE : 0.48734728 10 day(s) MAE : 2.25188535 10 day(s) RMSE : 6.82283430 10 day(s) R2 : -0.03602270 10 day(s) Pearson r : 0.03085607 10 day(s) QLIKE : 0.48842507 full horizon MAE : 2.25188535 full horizon RMSE : 6.82283430 full horizon R2 : -0.03602270 full horizon Pearson r : 0.03085607 full horizon QLIKE : 0.48842507 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/MSFT/Simple_KAN_H10.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=0.662595, max=6.48753 === MSFT | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.662699184664 Min value: -6.772192238786855 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.2181890995511795 Min value: -2.821740645483995 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.716447710866394 Min value: -4.319177175086081 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.891688839722294 Min value: -1.3281087285369817 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.94429083559171 Min value: -6.772192238873022 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.930006982137461 Min value: -2.3454031279530607 Epoch 1: Train mse = 0.9781 | Val mse = 0.6091 Epoch 2: Train mse = 0.8436 | Val mse = 4.4170 Epoch 3: Train mse = 0.7147 | Val mse = 0.7821 Epoch 4: Train mse = 0.6239 | Val mse = 1.0126 Epoch 5: Train mse = 0.5683 | Val mse = 1.0559 Epoch 6: Train mse = 0.5242 | Val mse = 0.8372 Epoch 7: Train mse = 0.4846 | Val mse = 0.7542 Epoch 8: Train mse = 0.4596 | Val mse = 0.9093 Epoch 9: Train mse = 0.4356 | Val mse = 0.8865 Epoch 10: Train mse = 0.4234 | Val mse = 0.8513 Epoch 11: Train mse = 0.4051 | Val mse = 0.7554 Epoch 12: Train mse = 0.3872 | Val mse = 0.8865 Epoch 13: Train mse = 0.3731 | Val mse = 0.8635 Epoch 14: Train mse = 0.3687 | Val mse = 0.8921 Epoch 15: Train mse = 0.3573 | Val mse = 0.8523 Epoch 16: Train mse = 0.3518 | Val mse = 0.8725 Epoch 17: Train mse = 0.3441 | Val mse = 0.9754 Epoch 18: Train mse = 0.3397 | Val mse = 0.8796 Epoch 19: Train mse = 0.3353 | Val mse = 0.8349 Epoch 20: Train mse = 0.3315 | Val mse = 0.9204 Epoch 21: Train mse = 0.3261 | Val mse = 0.8646 Epoch 22: Train mse = 0.3193 | Val mse = 0.7979 Epoch 23: Train mse = 0.3116 | Val mse = 0.8948 Epoch 24: Train mse = 0.3044 | Val mse = 0.8677 Epoch 25: Train mse = 0.2979 | Val mse = 0.9164 Epoch 26: Train mse = 0.2904 | Val mse = 0.9578 Epoch 27: Train mse = 0.2848 | Val mse = 0.8726 Epoch 28: Train mse = 0.2796 | Val mse = 0.9947 Epoch 29: Train mse = 0.2743 | Val mse = 0.8908 Epoch 30: Train mse = 0.2688 | Val mse = 0.9186 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.03240446 1 day(s) RMSE : 6.84342837 1 day(s) R2 : -0.04241011 1 day(s) Pearson r : -0.03372010 1 day(s) QLIKE : 0.51434567 3 day(s) MAE : 2.03964118 3 day(s) RMSE : 6.85342951 3 day(s) R2 : -0.04552903 3 day(s) Pearson r : -0.03462839 3 day(s) QLIKE : 0.51522089 5 day(s) MAE : 2.04262208 5 day(s) RMSE : 6.84705644 5 day(s) R2 : -0.04346837 5 day(s) Pearson r : -0.03516538 5 day(s) QLIKE : 0.51127029 10 day(s) MAE : 2.05778336 10 day(s) RMSE : 6.84888201 10 day(s) R2 : -0.04394830 10 day(s) Pearson r : -0.04058281 10 day(s) QLIKE : 0.51113703 20 day(s) MAE : 2.13008206 20 day(s) RMSE : 7.05893875 20 day(s) R2 : -0.04496904 20 day(s) Pearson r : -0.04710252 20 day(s) QLIKE : 0.52423121 full horizon MAE : 2.13008206 full horizon RMSE : 7.05893875 full horizon R2 : -0.04496904 full horizon Pearson r : -0.04710252 full horizon QLIKE : 0.52423121 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/MSFT/Simple_KAN_H20.pkl Saved y_true min=0.27301, max=89.8516 Saved y_pred min=0.985229, max=3.18312 === GE | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238581 Min value: -4.100074765876385 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.030029259880504 Min value: -2.652656432671519 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.0235417533877262 Min value: -1.87462522032935 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.0718644033108644 Min value: -2.366307284351862 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.323746204771034 Min value: -1.87462522032935 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.646703896552383 Min value: -2.148420825574933 Epoch 1: Train mse = 0.9410 | Val mse = 0.6786 Epoch 2: Train mse = 0.7224 | Val mse = 0.5602 Epoch 3: Train mse = 0.6152 | Val mse = 0.7667 Epoch 4: Train mse = 0.5263 | Val mse = 0.6078 Epoch 5: Train mse = 0.4804 | Val mse = 0.4625 Epoch 6: Train mse = 0.4456 | Val mse = 0.3906 Epoch 7: Train mse = 0.4190 | Val mse = 0.5069 Epoch 8: Train mse = 0.4022 | Val mse = 0.4571 Epoch 9: Train mse = 0.3708 | Val mse = 0.4933 Epoch 10: Train mse = 0.3642 | Val mse = 0.4336 Epoch 11: Train mse = 0.3474 | Val mse = 0.4905 Epoch 12: Train mse = 0.3493 | Val mse = 0.5008 Epoch 13: Train mse = 0.3180 | Val mse = 0.4648 Epoch 14: Train mse = 0.3008 | Val mse = 0.4591 Epoch 15: Train mse = 0.2878 | Val mse = 0.5308 Epoch 16: Train mse = 0.2766 | Val mse = 0.4545 Epoch 17: Train mse = 0.2492 | Val mse = 0.5336 Epoch 18: Train mse = 0.2345 | Val mse = 0.4284 Epoch 19: Train mse = 0.2272 | Val mse = 0.4690 Epoch 20: Train mse = 0.2090 | Val mse = 0.4289 Epoch 21: Train mse = 0.2015 | Val mse = 0.4400 Epoch 22: Train mse = 0.1953 | Val mse = 0.4167 Epoch 23: Train mse = 0.1762 | Val mse = 0.4283 Epoch 24: Train mse = 0.2122 | Val mse = 0.5048 Epoch 25: Train mse = 0.1591 | Val mse = 0.4499 Epoch 26: Train mse = 0.1594 | Val mse = 0.5424 Epoch 27: Train mse = 0.1497 | Val mse = 0.4571 Epoch 28: Train mse = 0.1242 | Val mse = 0.5251 Epoch 29: Train mse = 0.1358 | Val mse = 0.4462 Epoch 30: Train mse = 0.1482 | Val mse = 0.5236 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.04590246 1 day(s) RMSE : 27.43741760 1 day(s) R2 : -0.00081928 1 day(s) Pearson r : 0.13423973 1 day(s) QLIKE : 0.58339518 full horizon MAE : 4.04590246 full horizon RMSE : 27.43741760 full horizon R2 : -0.00081928 full horizon Pearson r : 0.13423973 full horizon QLIKE : 0.58339518 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/GE/Simple_KAN_H1.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=1.85714, max=9.97556 === GE | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238581 Min value: -4.100074765876385 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.05628901565786 Min value: -2.663641411446664 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.0235417533877262 Min value: -1.87462522032935 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.0759413314511908 Min value: -2.3763793738908188 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.323746204771034 Min value: -1.87462522032935 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.658989486293715 Min value: -2.1577982868808543 Epoch 1: Train mse = 0.9629 | Val mse = 0.5906 Epoch 2: Train mse = 0.7561 | Val mse = 0.3987 Epoch 3: Train mse = 0.6301 | Val mse = 0.9765 Epoch 4: Train mse = 0.5596 | Val mse = 0.7188 Epoch 5: Train mse = 0.5111 | Val mse = 0.5055 Epoch 6: Train mse = 0.4801 | Val mse = 0.3715 Epoch 7: Train mse = 0.4401 | Val mse = 0.3961 Epoch 8: Train mse = 0.4199 | Val mse = 0.4076 Epoch 9: Train mse = 0.4025 | Val mse = 0.4717 Epoch 10: Train mse = 0.3889 | Val mse = 0.4160 Epoch 11: Train mse = 0.3753 | Val mse = 0.5222 Epoch 12: Train mse = 0.3641 | Val mse = 0.4751 Epoch 13: Train mse = 0.3566 | Val mse = 0.5707 Epoch 14: Train mse = 0.3474 | Val mse = 0.4556 Epoch 15: Train mse = 0.3441 | Val mse = 0.5275 Epoch 16: Train mse = 0.3360 | Val mse = 0.4332 Epoch 17: Train mse = 0.3272 | Val mse = 0.4181 Epoch 18: Train mse = 0.3227 | Val mse = 0.4339 Epoch 19: Train mse = 0.3175 | Val mse = 0.4431 Epoch 20: Train mse = 0.3101 | Val mse = 0.4240 Epoch 21: Train mse = 0.3103 | Val mse = 0.4123 Epoch 22: Train mse = 0.3007 | Val mse = 0.4144 Epoch 23: Train mse = 0.2946 | Val mse = 0.4292 Epoch 24: Train mse = 0.2861 | Val mse = 0.4319 Epoch 25: Train mse = 0.2779 | Val mse = 0.4351 Epoch 26: Train mse = 0.2687 | Val mse = 0.4280 Epoch 27: Train mse = 0.2608 | Val mse = 0.4454 Epoch 28: Train mse = 0.2555 | Val mse = 0.4499 Epoch 29: Train mse = 0.2492 | Val mse = 0.4546 Epoch 30: Train mse = 0.2417 | Val mse = 0.4499 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.24708471 1 day(s) RMSE : 27.60476089 1 day(s) R2 : -0.01306469 1 day(s) Pearson r : 0.14212663 1 day(s) QLIKE : 0.57748728 3 day(s) MAE : 4.23087124 3 day(s) RMSE : 27.59444237 3 day(s) R2 : -0.01236573 3 day(s) Pearson r : 0.14604028 3 day(s) QLIKE : 0.57622837 5 day(s) MAE : 4.23786027 5 day(s) RMSE : 27.59368417 5 day(s) R2 : -0.01236807 5 day(s) Pearson r : 0.15063285 5 day(s) QLIKE : 0.57304989 full horizon MAE : 4.23786027 full horizon RMSE : 27.59368417 full horizon R2 : -0.01236807 full horizon Pearson r : 0.15063285 full horizon QLIKE : 0.57304989 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/GE/Simple_KAN_H5.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=1.62657, max=4.22927 === GE | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238581 Min value: -4.100074765876385 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.080595466809852 Min value: -2.674432668646892 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.0235417533877262 Min value: -1.87462522032935 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.079343715915996 Min value: -2.3863103666656302 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.323746204771034 Min value: -1.87462522032935 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.670127332275317 Min value: -2.167074694264489 Epoch 1: Train mse = 0.9680 | Val mse = 0.5740 Epoch 2: Train mse = 0.7850 | Val mse = 0.5834 Epoch 3: Train mse = 0.6456 | Val mse = 1.2520 Epoch 4: Train mse = 0.5724 | Val mse = 0.9381 Epoch 5: Train mse = 0.5357 | Val mse = 0.6365 Epoch 6: Train mse = 0.4889 | Val mse = 0.6580 Epoch 7: Train mse = 0.4622 | Val mse = 0.5036 Epoch 8: Train mse = 0.4453 | Val mse = 0.6822 Epoch 9: Train mse = 0.4328 | Val mse = 0.8089 Epoch 10: Train mse = 0.4248 | Val mse = 0.7605 Epoch 11: Train mse = 0.4140 | Val mse = 0.7149 Epoch 12: Train mse = 0.4111 | Val mse = 0.7975 Epoch 13: Train mse = 0.4023 | Val mse = 0.7534 Epoch 14: Train mse = 0.3938 | Val mse = 0.8222 Epoch 15: Train mse = 0.3894 | Val mse = 0.8373 Epoch 16: Train mse = 0.3851 | Val mse = 0.8241 Epoch 17: Train mse = 0.3782 | Val mse = 0.8749 Epoch 18: Train mse = 0.3759 | Val mse = 0.8395 Epoch 19: Train mse = 0.3703 | Val mse = 0.9839 Epoch 20: Train mse = 0.3576 | Val mse = 0.8961 Epoch 21: Train mse = 0.3457 | Val mse = 1.0443 Epoch 22: Train mse = 0.3497 | Val mse = 0.9105 Epoch 23: Train mse = 0.3431 | Val mse = 1.1360 Epoch 24: Train mse = 0.3280 | Val mse = 1.0146 Epoch 25: Train mse = 0.3187 | Val mse = 1.1245 Epoch 26: Train mse = 0.3126 | Val mse = 1.0759 Epoch 27: Train mse = 0.3060 | Val mse = 1.1044 Epoch 28: Train mse = 0.3021 | Val mse = 1.0541 Epoch 29: Train mse = 0.2958 | Val mse = 1.0889 Epoch 30: Train mse = 0.2938 | Val mse = 1.0899 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.20590176 1 day(s) RMSE : 27.47946687 1 day(s) R2 : -0.00388925 1 day(s) Pearson r : 0.12597910 1 day(s) QLIKE : 0.61697216 3 day(s) MAE : 4.22101293 3 day(s) RMSE : 27.47473113 3 day(s) R2 : -0.00360101 3 day(s) Pearson r : 0.12351563 3 day(s) QLIKE : 0.61664799 5 day(s) MAE : 4.23225229 5 day(s) RMSE : 27.47225148 5 day(s) R2 : -0.00347734 5 day(s) Pearson r : 0.12285672 5 day(s) QLIKE : 0.61666400 10 day(s) MAE : 4.23866564 10 day(s) RMSE : 27.47212966 10 day(s) R2 : -0.00332118 10 day(s) Pearson r : 0.13007834 10 day(s) QLIKE : 0.60829894 full horizon MAE : 4.23866564 full horizon RMSE : 27.47212966 full horizon R2 : -0.00332118 full horizon Pearson r : 0.13007834 full horizon QLIKE : 0.60829894 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/GE/Simple_KAN_H10.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=2.64359, max=6.20741 === GE | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 38.10128934238581 Min value: -4.100074765876385 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.120224031597557 Min value: -2.692943196140936 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.0235417533877262 Min value: -1.87462522032935 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.084344850046585 Min value: -2.4033958725510236 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.323746204771034 Min value: -1.87462522032935 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.6879422007515705 Min value: -2.183075884328406 Epoch 1: Train mse = 0.9705 | Val mse = 0.6144 Epoch 2: Train mse = 0.7905 | Val mse = 0.9188 Epoch 3: Train mse = 0.6566 | Val mse = 1.4962 Epoch 4: Train mse = 0.5961 | Val mse = 0.9559 Epoch 5: Train mse = 0.5474 | Val mse = 0.5920 Epoch 6: Train mse = 0.5096 | Val mse = 0.5143 Epoch 7: Train mse = 0.4898 | Val mse = 0.4915 Epoch 8: Train mse = 0.4767 | Val mse = 0.6006 Epoch 9: Train mse = 0.4672 | Val mse = 0.6780 Epoch 10: Train mse = 0.4622 | Val mse = 0.6727 Epoch 11: Train mse = 0.4523 | Val mse = 0.8075 Epoch 12: Train mse = 0.4423 | Val mse = 0.8084 Epoch 13: Train mse = 0.4291 | Val mse = 0.8396 Epoch 14: Train mse = 0.4199 | Val mse = 0.8741 Epoch 15: Train mse = 0.4085 | Val mse = 0.8999 Epoch 16: Train mse = 0.4008 | Val mse = 0.9463 Epoch 17: Train mse = 0.3913 | Val mse = 0.8384 Epoch 18: Train mse = 0.3895 | Val mse = 0.9680 Epoch 19: Train mse = 0.3846 | Val mse = 0.9535 Epoch 20: Train mse = 0.3802 | Val mse = 0.8934 Epoch 21: Train mse = 0.3771 | Val mse = 1.0385 Epoch 22: Train mse = 0.3705 | Val mse = 0.9482 Epoch 23: Train mse = 0.3668 | Val mse = 0.9912 Epoch 24: Train mse = 0.3606 | Val mse = 1.0019 Epoch 25: Train mse = 0.3554 | Val mse = 1.0029 Epoch 26: Train mse = 0.3460 | Val mse = 1.0395 Epoch 27: Train mse = 0.3381 | Val mse = 0.9882 Epoch 28: Train mse = 0.3346 | Val mse = 1.1618 Epoch 29: Train mse = 0.3298 | Val mse = 1.2447 Epoch 30: Train mse = 0.3291 | Val mse = 1.0515 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 4.21736126 1 day(s) RMSE : 27.53810045 1 day(s) R2 : -0.00817786 1 day(s) Pearson r : 0.09776800 1 day(s) QLIKE : 0.62401058 3 day(s) MAE : 4.20902362 3 day(s) RMSE : 27.53323054 3 day(s) R2 : -0.00787931 3 day(s) Pearson r : 0.10069852 3 day(s) QLIKE : 0.62283444 5 day(s) MAE : 4.20775943 5 day(s) RMSE : 27.53910717 5 day(s) R2 : -0.00836735 5 day(s) Pearson r : 0.10054168 5 day(s) QLIKE : 0.61966181 10 day(s) MAE : 4.22512756 10 day(s) RMSE : 27.54050710 10 day(s) R2 : -0.00832188 10 day(s) Pearson r : 0.10440397 10 day(s) QLIKE : 0.61560962 20 day(s) MAE : 4.25221882 20 day(s) RMSE : 27.54054181 20 day(s) R2 : -0.00827107 20 day(s) Pearson r : 0.10410960 20 day(s) QLIKE : 0.61601795 full horizon MAE : 4.25221882 full horizon RMSE : 27.54054181 full horizon R2 : -0.00827107 full horizon Pearson r : 0.10410960 full horizon QLIKE : 0.61601795 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/GE/Simple_KAN_H20.pkl Saved y_true min=0.591065, max=527.531 Saved y_pred min=1.94896, max=4.96888 === BAC | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179623 Min value: -4.428701459937723 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.10774278654742 Min value: -3.152671373228803 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166819 Min value: -3.0829742668180926 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.2542888447212466 Min value: -1.29240155253667 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787336 Min value: -2.9204727182396413 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.207108669727663 Min value: -1.9461535420618146 Epoch 1: Train mse = 0.9185 | Val mse = 0.5658 Epoch 2: Train mse = 0.6551 | Val mse = 0.5616 Epoch 3: Train mse = 0.5806 | Val mse = 0.5121 Epoch 4: Train mse = 0.4857 | Val mse = 0.5967 Epoch 5: Train mse = 0.4428 | Val mse = 0.5164 Epoch 6: Train mse = 0.3837 | Val mse = 0.5586 Epoch 7: Train mse = 0.3526 | Val mse = 0.5082 Epoch 8: Train mse = 0.3162 | Val mse = 0.5049 Epoch 9: Train mse = 0.2933 | Val mse = 0.5536 Epoch 10: Train mse = 0.2582 | Val mse = 0.5059 Epoch 11: Train mse = 0.2264 | Val mse = 0.5179 Epoch 12: Train mse = 0.2062 | Val mse = 0.5261 Epoch 13: Train mse = 0.1746 | Val mse = 0.5370 Epoch 14: Train mse = 0.1588 | Val mse = 0.5450 Epoch 15: Train mse = 0.1413 | Val mse = 0.6004 Epoch 16: Train mse = 0.1311 | Val mse = 0.5800 Epoch 17: Train mse = 0.1148 | Val mse = 0.5654 Epoch 18: Train mse = 0.0964 | Val mse = 0.5505 Epoch 19: Train mse = 0.0782 | Val mse = 0.5939 Epoch 20: Train mse = 0.0673 | Val mse = 0.5888 Epoch 21: Train mse = 0.0611 | Val mse = 0.6118 Epoch 22: Train mse = 0.0508 | Val mse = 0.6469 Epoch 23: Train mse = 0.0425 | Val mse = 0.6224 Epoch 24: Train mse = 0.0383 | Val mse = 0.5722 Epoch 25: Train mse = 0.0343 | Val mse = 0.5931 Epoch 26: Train mse = 0.0281 | Val mse = 0.5905 Epoch 27: Train mse = 0.0232 | Val mse = 0.6016 Epoch 28: Train mse = 0.0219 | Val mse = 0.5788 Epoch 29: Train mse = 0.0198 | Val mse = 0.5937 Epoch 30: Train mse = 0.0182 | Val mse = 0.6169 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.27445327 1 day(s) RMSE : 7.56234546 1 day(s) R2 : 0.05094868 1 day(s) Pearson r : 0.25017738 1 day(s) QLIKE : 0.45358636 full horizon MAE : 2.27445327 full horizon RMSE : 7.56234546 full horizon R2 : 0.05094868 full horizon Pearson r : 0.25017738 full horizon QLIKE : 0.45358636 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/BAC/Simple_KAN_H1.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=1.32094, max=20.2467 === BAC | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179623 Min value: -4.428701459937723 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.106232115580275 Min value: -3.150569092336922 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166819 Min value: -3.0829742668180926 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.253588840112375 Min value: -1.2911129191419919 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787336 Min value: -2.9204727182396413 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.205991919409497 Min value: -1.9445789696790647 Epoch 1: Train mse = 0.9293 | Val mse = 0.5409 Epoch 2: Train mse = 0.7837 | Val mse = 0.5272 Epoch 3: Train mse = 0.7330 | Val mse = 0.5312 Epoch 4: Train mse = 0.6196 | Val mse = 0.5598 Epoch 5: Train mse = 0.5311 | Val mse = 0.5623 Epoch 6: Train mse = 0.4512 | Val mse = 0.5919 Epoch 7: Train mse = 0.4150 | Val mse = 0.5974 Epoch 8: Train mse = 0.3812 | Val mse = 0.6078 Epoch 9: Train mse = 0.3529 | Val mse = 0.6767 Epoch 10: Train mse = 0.3329 | Val mse = 0.6162 Epoch 11: Train mse = 0.3124 | Val mse = 0.6635 Epoch 12: Train mse = 0.3009 | Val mse = 0.6474 Epoch 13: Train mse = 0.2931 | Val mse = 0.6731 Epoch 14: Train mse = 0.2897 | Val mse = 0.6829 Epoch 15: Train mse = 0.2823 | Val mse = 0.7044 Epoch 16: Train mse = 0.2792 | Val mse = 0.7185 Epoch 17: Train mse = 0.2735 | Val mse = 0.6711 Epoch 18: Train mse = 0.2688 | Val mse = 0.7078 Epoch 19: Train mse = 0.2598 | Val mse = 0.6834 Epoch 20: Train mse = 0.2504 | Val mse = 0.7043 Epoch 21: Train mse = 0.2377 | Val mse = 0.7014 Epoch 22: Train mse = 0.2310 | Val mse = 0.7058 Epoch 23: Train mse = 0.2167 | Val mse = 0.6943 Epoch 24: Train mse = 0.2063 | Val mse = 0.6833 Epoch 25: Train mse = 0.2009 | Val mse = 0.7173 Epoch 26: Train mse = 0.1917 | Val mse = 0.6873 Epoch 27: Train mse = 0.1858 | Val mse = 0.7285 Epoch 28: Train mse = 0.1808 | Val mse = 0.6665 Epoch 29: Train mse = 0.1744 | Val mse = 0.7173 Epoch 30: Train mse = 0.1694 | Val mse = 0.6883 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.60878762 1 day(s) RMSE : 7.81880219 1 day(s) R2 : -0.01451185 1 day(s) Pearson r : -0.03943716 1 day(s) QLIKE : 0.45910379 3 day(s) MAE : 2.61973922 3 day(s) RMSE : 7.83520593 3 day(s) R2 : -0.01812267 3 day(s) Pearson r : -0.06794995 3 day(s) QLIKE : 0.46911633 5 day(s) MAE : 2.58892064 5 day(s) RMSE : 7.84042421 5 day(s) R2 : -0.01916429 5 day(s) Pearson r : -0.08762327 5 day(s) QLIKE : 0.47056519 full horizon MAE : 2.58892064 full horizon RMSE : 7.84042421 full horizon R2 : -0.01916429 full horizon Pearson r : -0.08762327 full horizon QLIKE : 0.47056519 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/BAC/Simple_KAN_H5.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=2.40163, max=4.49188 === BAC | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179623 Min value: -4.428701459937723 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.1031963312484585 Min value: -3.149203897636293 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166819 Min value: -3.0829742668180926 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.251540537979127 Min value: -1.2907388380002232 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787336 Min value: -2.9204727182396413 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.203435974440135 Min value: -1.9438565829024124 Epoch 1: Train mse = 0.9502 | Val mse = 0.5285 Epoch 2: Train mse = 0.8322 | Val mse = 0.5014 Epoch 3: Train mse = 0.7407 | Val mse = 0.5385 Epoch 4: Train mse = 0.6383 | Val mse = 0.5257 Epoch 5: Train mse = 0.5570 | Val mse = 0.5362 Epoch 6: Train mse = 0.4914 | Val mse = 0.6212 Epoch 7: Train mse = 0.4489 | Val mse = 0.5836 Epoch 8: Train mse = 0.4176 | Val mse = 0.6386 Epoch 9: Train mse = 0.4026 | Val mse = 0.6267 Epoch 10: Train mse = 0.3860 | Val mse = 0.6575 Epoch 11: Train mse = 0.3732 | Val mse = 0.6458 Epoch 12: Train mse = 0.3651 | Val mse = 0.6440 Epoch 13: Train mse = 0.3559 | Val mse = 0.6788 Epoch 14: Train mse = 0.3543 | Val mse = 0.6340 Epoch 15: Train mse = 0.3478 | Val mse = 0.6412 Epoch 16: Train mse = 0.3378 | Val mse = 0.6284 Epoch 17: Train mse = 0.3234 | Val mse = 0.6312 Epoch 18: Train mse = 0.3067 | Val mse = 0.6310 Epoch 19: Train mse = 0.2929 | Val mse = 0.6419 Epoch 20: Train mse = 0.2898 | Val mse = 0.6535 Epoch 21: Train mse = 0.2798 | Val mse = 0.6718 Epoch 22: Train mse = 0.2750 | Val mse = 0.6334 Epoch 23: Train mse = 0.2714 | Val mse = 0.6637 Epoch 24: Train mse = 0.2662 | Val mse = 0.6437 Epoch 25: Train mse = 0.2624 | Val mse = 0.6471 Epoch 26: Train mse = 0.2587 | Val mse = 0.6489 Epoch 27: Train mse = 0.2546 | Val mse = 0.6679 Epoch 28: Train mse = 0.2483 | Val mse = 0.6549 Epoch 29: Train mse = 0.2403 | Val mse = 0.6520 Epoch 30: Train mse = 0.2338 | Val mse = 0.6808 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.31748362 1 day(s) RMSE : 7.83325846 1 day(s) R2 : -0.01826680 1 day(s) Pearson r : -0.03387885 1 day(s) QLIKE : 0.44796140 3 day(s) MAE : 2.34117782 3 day(s) RMSE : 7.83949416 3 day(s) R2 : -0.01923742 3 day(s) Pearson r : -0.07027134 3 day(s) QLIKE : 0.45258718 5 day(s) MAE : 2.35382966 5 day(s) RMSE : 7.84426726 5 day(s) R2 : -0.02016364 5 day(s) Pearson r : -0.09473055 5 day(s) QLIKE : 0.45552393 10 day(s) MAE : 2.36923141 10 day(s) RMSE : 7.85983761 10 day(s) R2 : -0.02412039 10 day(s) Pearson r : -0.12158014 10 day(s) QLIKE : 0.46455460 full horizon MAE : 2.36923141 full horizon RMSE : 7.85983761 full horizon R2 : -0.02412039 full horizon Pearson r : -0.12158014 full horizon QLIKE : 0.46455460 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/BAC/Simple_KAN_H10.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=2.1105, max=4.2876 === BAC | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.142355974179623 Min value: -4.428701459937723 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.103268362353564 Min value: -3.1487890186062457 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.800014951166819 Min value: -3.0829742668180926 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.251689496563368 Min value: -1.290401169341898 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.111754968787336 Min value: -2.9204727182396413 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.203545386302683 Min value: -1.9434917803124587 Epoch 1: Train mse = 0.9693 | Val mse = 0.5318 Epoch 2: Train mse = 0.8190 | Val mse = 0.5002 Epoch 3: Train mse = 0.7102 | Val mse = 0.5215 Epoch 4: Train mse = 0.6144 | Val mse = 0.4884 Epoch 5: Train mse = 0.5539 | Val mse = 0.5026 Epoch 6: Train mse = 0.5059 | Val mse = 0.5230 Epoch 7: Train mse = 0.4766 | Val mse = 0.5340 Epoch 8: Train mse = 0.4548 | Val mse = 0.6238 Epoch 9: Train mse = 0.4327 | Val mse = 0.5285 Epoch 10: Train mse = 0.4170 | Val mse = 0.5310 Epoch 11: Train mse = 0.4030 | Val mse = 0.5327 Epoch 12: Train mse = 0.3917 | Val mse = 0.5153 Epoch 13: Train mse = 0.3798 | Val mse = 0.4978 Epoch 14: Train mse = 0.3720 | Val mse = 0.5167 Epoch 15: Train mse = 0.3664 | Val mse = 0.5103 Epoch 16: Train mse = 0.3597 | Val mse = 0.5268 Epoch 17: Train mse = 0.3536 | Val mse = 0.5250 Epoch 18: Train mse = 0.3472 | Val mse = 0.5241 Epoch 19: Train mse = 0.3422 | Val mse = 0.5223 Epoch 20: Train mse = 0.3380 | Val mse = 0.5271 Epoch 21: Train mse = 0.3327 | Val mse = 0.5215 Epoch 22: Train mse = 0.3270 | Val mse = 0.5322 Epoch 23: Train mse = 0.3200 | Val mse = 0.5216 Epoch 24: Train mse = 0.3093 | Val mse = 0.5325 Epoch 25: Train mse = 0.3005 | Val mse = 0.5479 Epoch 26: Train mse = 0.2919 | Val mse = 0.5603 Epoch 27: Train mse = 0.2855 | Val mse = 0.5522 Epoch 28: Train mse = 0.2773 | Val mse = 0.5644 Epoch 29: Train mse = 0.2703 | Val mse = 0.5530 Epoch 30: Train mse = 0.2629 | Val mse = 0.5649 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.51626768 1 day(s) RMSE : 7.95222584 1 day(s) R2 : -0.04943147 1 day(s) Pearson r : -0.05234440 1 day(s) QLIKE : 0.51950806 3 day(s) MAE : 2.50709765 3 day(s) RMSE : 7.96761254 3 day(s) R2 : -0.05282379 3 day(s) Pearson r : -0.07138328 3 day(s) QLIKE : 0.53274623 5 day(s) MAE : 2.50733997 5 day(s) RMSE : 7.97805936 5 day(s) R2 : -0.05526031 5 day(s) Pearson r : -0.08341426 5 day(s) QLIKE : 0.53895782 10 day(s) MAE : 2.56712265 10 day(s) RMSE : 8.01364516 10 day(s) R2 : -0.06459417 10 day(s) Pearson r : -0.09603595 10 day(s) QLIKE : 0.55911839 20 day(s) MAE : 2.61654886 20 day(s) RMSE : 8.02643004 20 day(s) R2 : -0.06834558 20 day(s) Pearson r : -0.09043413 20 day(s) QLIKE : 0.56487918 full horizon MAE : 2.61654886 full horizon RMSE : 8.02643004 full horizon R2 : -0.06834558 full horizon Pearson r : -0.09043413 full horizon QLIKE : 0.56487918 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/BAC/Simple_KAN_H20.pkl Saved y_true min=0.587715, max=111.472 Saved y_pred min=1.5241, max=6.89023 === C | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.80914068636715 Min value: -5.781033963264048 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.116803264609633 Min value: -2.5790852911299234 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.316307789702823 Min value: -3.321376796599313 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.6474166417153135 Min value: -1.663048077620065 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.453119269597156 Min value: -3.4619875412520558 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.113246907180982 Min value: -2.058904827000899 Epoch 1: Train mse = 0.9151 | Val mse = 1.1496 Epoch 2: Train mse = 0.7381 | Val mse = 1.0338 Epoch 3: Train mse = 0.6327 | Val mse = 1.3866 Epoch 4: Train mse = 0.5482 | Val mse = 0.4775 Epoch 5: Train mse = 0.4725 | Val mse = 1.3352 Epoch 6: Train mse = 0.4143 | Val mse = 1.3612 Epoch 7: Train mse = 0.3874 | Val mse = 0.8092 Epoch 8: Train mse = 0.3482 | Val mse = 1.5984 Epoch 9: Train mse = 0.3087 | Val mse = 1.0738 Epoch 10: Train mse = 0.2843 | Val mse = 1.1910 Epoch 11: Train mse = 0.2551 | Val mse = 1.2608 Epoch 12: Train mse = 0.2337 | Val mse = 1.1709 Epoch 13: Train mse = 0.2130 | Val mse = 0.9140 Epoch 14: Train mse = 0.1953 | Val mse = 1.2065 Epoch 15: Train mse = 0.1734 | Val mse = 0.8229 Epoch 16: Train mse = 0.1647 | Val mse = 1.2021 Epoch 17: Train mse = 0.1399 | Val mse = 1.2290 Epoch 18: Train mse = 0.1363 | Val mse = 1.5384 Epoch 19: Train mse = 0.1240 | Val mse = 0.8120 Epoch 20: Train mse = 0.1183 | Val mse = 1.3908 Epoch 21: Train mse = 0.1120 | Val mse = 1.1610 Epoch 22: Train mse = 0.1023 | Val mse = 1.0829 Epoch 23: Train mse = 0.0892 | Val mse = 0.8880 Epoch 24: Train mse = 0.0759 | Val mse = 0.9996 Epoch 25: Train mse = 0.0751 | Val mse = 0.7573 Epoch 26: Train mse = 0.0729 | Val mse = 0.9482 Epoch 27: Train mse = 0.0621 | Val mse = 0.8507 Epoch 28: Train mse = 0.0569 | Val mse = 0.9297 Epoch 29: Train mse = 0.0470 | Val mse = 0.8220 Epoch 30: Train mse = 0.0524 | Val mse = 0.8560 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.83079705 1 day(s) RMSE : 9.35238622 1 day(s) R2 : 0.01828693 1 day(s) Pearson r : 0.26113963 1 day(s) QLIKE : 0.37579545 full horizon MAE : 2.83079705 full horizon RMSE : 9.35238622 full horizon R2 : 0.01828693 full horizon Pearson r : 0.26113963 full horizon QLIKE : 0.37579545 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/C/Simple_KAN_H1.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=1.90879, max=9.36335 === C | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.80914068636715 Min value: -5.781033963264048 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.113808938949674 Min value: -2.5779301635708127 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.316307789702823 Min value: -3.321376796599313 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.6457537548096046 Min value: -1.6623868571045473 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.453119269597156 Min value: -3.4619875412520558 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.110793676954993 Min value: -2.05803016926483 Epoch 1: Train mse = 0.9275 | Val mse = 0.5884 Epoch 2: Train mse = 0.7303 | Val mse = 0.9053 Epoch 3: Train mse = 0.6666 | Val mse = 1.4772 Epoch 4: Train mse = 0.5606 | Val mse = 0.7820 Epoch 5: Train mse = 0.5037 | Val mse = 0.9060 Epoch 6: Train mse = 0.4530 | Val mse = 1.6608 Epoch 7: Train mse = 0.4056 | Val mse = 0.9117 Epoch 8: Train mse = 0.3724 | Val mse = 1.9222 Epoch 9: Train mse = 0.3433 | Val mse = 1.3655 Epoch 10: Train mse = 0.3233 | Val mse = 1.6783 Epoch 11: Train mse = 0.3052 | Val mse = 2.2643 Epoch 12: Train mse = 0.2922 | Val mse = 1.0609 Epoch 13: Train mse = 0.2840 | Val mse = 1.6263 Epoch 14: Train mse = 0.2745 | Val mse = 1.1839 Epoch 15: Train mse = 0.2683 | Val mse = 1.8262 Epoch 16: Train mse = 0.2631 | Val mse = 1.2358 Epoch 17: Train mse = 0.2584 | Val mse = 1.4218 Epoch 18: Train mse = 0.2533 | Val mse = 1.2947 Epoch 19: Train mse = 0.2506 | Val mse = 1.5033 Epoch 20: Train mse = 0.2466 | Val mse = 1.3009 Epoch 21: Train mse = 0.2443 | Val mse = 1.4638 Epoch 22: Train mse = 0.2403 | Val mse = 1.3434 Epoch 23: Train mse = 0.2346 | Val mse = 1.3118 Epoch 24: Train mse = 0.2253 | Val mse = 1.5624 Epoch 25: Train mse = 0.2195 | Val mse = 1.4337 Epoch 26: Train mse = 0.2105 | Val mse = 1.3632 Epoch 27: Train mse = 0.2057 | Val mse = 1.5391 Epoch 28: Train mse = 0.1960 | Val mse = 1.4077 Epoch 29: Train mse = 0.1874 | Val mse = 1.5848 Epoch 30: Train mse = 0.1799 | Val mse = 1.5070 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.78352390 1 day(s) RMSE : 9.62483433 1 day(s) R2 : -0.03974354 1 day(s) Pearson r : 0.03226020 1 day(s) QLIKE : 0.42717548 3 day(s) MAE : 2.84387901 3 day(s) RMSE : 9.71131439 3 day(s) R2 : -0.05335032 3 day(s) Pearson r : 0.00300381 3 day(s) QLIKE : 0.43839667 5 day(s) MAE : 2.89243953 5 day(s) RMSE : 9.75856211 5 day(s) R2 : -0.06023890 5 day(s) Pearson r : -0.02124450 5 day(s) QLIKE : 0.44838879 full horizon MAE : 2.89243953 full horizon RMSE : 9.75856211 full horizon R2 : -0.06023890 full horizon Pearson r : -0.02124450 full horizon QLIKE : 0.44838879 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/C/Simple_KAN_H5.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=1.54577, max=3.4438 === C | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.80914068636715 Min value: -5.781033963264048 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.111052537752696 Min value: -2.577782485226596 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.316307789702823 Min value: -3.321376796599313 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.6439291882044227 Min value: -1.662584849687275 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.453119269597156 Min value: -3.4619875412520558 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.1084159724432565 Min value: -2.0580787834517045 Epoch 1: Train mse = 0.9498 | Val mse = 0.4963 Epoch 2: Train mse = 0.7451 | Val mse = 0.7568 Epoch 3: Train mse = 0.6458 | Val mse = 1.3818 Epoch 4: Train mse = 0.5698 | Val mse = 0.9882 Epoch 5: Train mse = 0.5189 | Val mse = 0.9378 Epoch 6: Train mse = 0.4716 | Val mse = 0.9955 Epoch 7: Train mse = 0.4273 | Val mse = 0.9364 Epoch 8: Train mse = 0.3957 | Val mse = 0.8568 Epoch 9: Train mse = 0.3706 | Val mse = 0.9264 Epoch 10: Train mse = 0.3554 | Val mse = 0.8946 Epoch 11: Train mse = 0.3431 | Val mse = 1.1963 Epoch 12: Train mse = 0.3335 | Val mse = 1.1366 Epoch 13: Train mse = 0.3252 | Val mse = 1.0034 Epoch 14: Train mse = 0.3200 | Val mse = 0.9859 Epoch 15: Train mse = 0.3125 | Val mse = 1.1691 Epoch 16: Train mse = 0.3063 | Val mse = 0.9269 Epoch 17: Train mse = 0.2983 | Val mse = 0.9436 Epoch 18: Train mse = 0.2875 | Val mse = 0.9321 Epoch 19: Train mse = 0.2794 | Val mse = 0.8994 Epoch 20: Train mse = 0.2727 | Val mse = 0.8556 Epoch 21: Train mse = 0.2670 | Val mse = 1.0359 Epoch 22: Train mse = 0.2584 | Val mse = 0.8588 Epoch 23: Train mse = 0.2536 | Val mse = 1.0431 Epoch 24: Train mse = 0.2474 | Val mse = 0.9746 Epoch 25: Train mse = 0.2457 | Val mse = 0.9282 Epoch 26: Train mse = 0.2412 | Val mse = 0.9610 Epoch 27: Train mse = 0.2376 | Val mse = 1.0133 Epoch 28: Train mse = 0.2334 | Val mse = 0.9658 Epoch 29: Train mse = 0.2261 | Val mse = 1.0515 Epoch 30: Train mse = 0.2202 | Val mse = 0.9481 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.77694165 1 day(s) RMSE : 9.58895289 1 day(s) R2 : -0.03200565 1 day(s) Pearson r : 0.06546580 1 day(s) QLIKE : 0.42251283 3 day(s) MAE : 2.81694502 3 day(s) RMSE : 9.66956987 3 day(s) R2 : -0.04431403 3 day(s) Pearson r : 0.03239317 3 day(s) QLIKE : 0.43079135 5 day(s) MAE : 2.85472443 5 day(s) RMSE : 9.70901185 5 day(s) R2 : -0.04949925 5 day(s) Pearson r : 0.01375720 5 day(s) QLIKE : 0.43665767 10 day(s) MAE : 2.88060415 10 day(s) RMSE : 9.72220655 10 day(s) R2 : -0.04989743 10 day(s) Pearson r : -0.01297184 10 day(s) QLIKE : 0.44105410 full horizon MAE : 2.88060415 full horizon RMSE : 9.72220655 full horizon R2 : -0.04989743 full horizon Pearson r : -0.01297184 full horizon QLIKE : 0.44105410 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/C/Simple_KAN_H10.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=1.83327, max=3.46701 === C | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2018 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1453, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 21.80914068636715 Min value: -5.781033963264048 Checking X_time_train_core: Shape: (1453, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.800915059933457 Checking y_train_core (log_mse scaled): Shape: (1453, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 5.111456003225553 Min value: -2.5798794252140986 Checking X_price_val: Shape: (161, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.316307789702823 Min value: -3.321376796599313 Checking X_time_val: Shape: (161, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5949981610828599 Checking y_val (log_mse scaled): Shape: (161, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.643530346347736 Min value: -1.664384167842813 Checking X_price_test: Shape: (404, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.453119269597156 Min value: -3.4619875412520558 Checking X_time_test: Shape: (404, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.481940465687973 Min value: -1.5797626144150565 Checking y_test (log_mse scaled): Shape: (404, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.1084933809861415 Min value: -2.0600067160447444 Epoch 1: Train mse = 0.9689 | Val mse = 0.4977 Epoch 2: Train mse = 0.7691 | Val mse = 0.6931 Epoch 3: Train mse = 0.6687 | Val mse = 1.2204 Epoch 4: Train mse = 0.6067 | Val mse = 1.1469 Epoch 5: Train mse = 0.5584 | Val mse = 0.8185 Epoch 6: Train mse = 0.5161 | Val mse = 0.5392 Epoch 7: Train mse = 0.4671 | Val mse = 0.6711 Epoch 8: Train mse = 0.4340 | Val mse = 0.5831 Epoch 9: Train mse = 0.4072 | Val mse = 0.8267 Epoch 10: Train mse = 0.3882 | Val mse = 0.7627 Epoch 11: Train mse = 0.3697 | Val mse = 0.7614 Epoch 12: Train mse = 0.3582 | Val mse = 0.8653 Epoch 13: Train mse = 0.3472 | Val mse = 0.8639 Epoch 14: Train mse = 0.3395 | Val mse = 0.8501 Epoch 15: Train mse = 0.3327 | Val mse = 0.9073 Epoch 16: Train mse = 0.3243 | Val mse = 0.8720 Epoch 17: Train mse = 0.3184 | Val mse = 0.9416 Epoch 18: Train mse = 0.3135 | Val mse = 0.8608 Epoch 19: Train mse = 0.3086 | Val mse = 0.8853 Epoch 20: Train mse = 0.3039 | Val mse = 0.8199 Epoch 21: Train mse = 0.2986 | Val mse = 0.8661 Epoch 22: Train mse = 0.2917 | Val mse = 0.8122 Epoch 23: Train mse = 0.2857 | Val mse = 0.8362 Epoch 24: Train mse = 0.2780 | Val mse = 0.8154 Epoch 25: Train mse = 0.2728 | Val mse = 0.8950 Epoch 26: Train mse = 0.2676 | Val mse = 0.8521 Epoch 27: Train mse = 0.2616 | Val mse = 0.9180 Epoch 28: Train mse = 0.2574 | Val mse = 0.9415 Epoch 29: Train mse = 0.2506 | Val mse = 0.8795 Epoch 30: Train mse = 0.2444 | Val mse = 0.9014 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 2.78140564 1 day(s) RMSE : 9.59291763 1 day(s) R2 : -0.03285924 1 day(s) Pearson r : 0.05776588 1 day(s) QLIKE : 0.42271778 3 day(s) MAE : 2.81806295 3 day(s) RMSE : 9.65244549 3 day(s) R2 : -0.04061844 3 day(s) Pearson r : 0.03229386 3 day(s) QLIKE : 0.43053256 5 day(s) MAE : 2.84849761 5 day(s) RMSE : 9.66906269 5 day(s) R2 : -0.04088038 5 day(s) Pearson r : 0.01926510 5 day(s) QLIKE : 0.43472971 10 day(s) MAE : 2.87875176 10 day(s) RMSE : 9.68831433 10 day(s) R2 : -0.04259017 10 day(s) Pearson r : 0.00166947 10 day(s) QLIKE : 0.43814978 20 day(s) MAE : 2.90065640 20 day(s) RMSE : 9.70582751 20 day(s) R2 : -0.04553400 20 day(s) Pearson r : -0.02452333 20 day(s) QLIKE : 0.44104095 full horizon MAE : 2.90065640 full horizon RMSE : 9.70582751 full horizon R2 : -0.04553400 full horizon Pearson r : -0.02452333 full horizon QLIKE : 0.44104095 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/C/Simple_KAN_H20.pkl Saved y_true min=0.525001, max=134.647 Saved y_pred min=1.89499, max=3.39094 === BTCUSDT | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2399 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514246752 Min value: -9.895452801091551 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.551349524903821 Min value: -3.6053341677678 Checking X_price_val: Shape: (192, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.53568548101405 Min value: -9.62541592027249 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.344819091237707 Min value: -4.24132665249135 Checking X_price_test: Shape: (480, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.53568548101405 Min value: -9.62541592027249 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.58025865739623 Min value: -2.56748600221835 Epoch 1: Train mse = 0.9189 | Val mse = 1.0587 Epoch 2: Train mse = 0.7187 | Val mse = 1.4851 Epoch 3: Train mse = 0.6062 | Val mse = 1.3982 Epoch 4: Train mse = 0.5183 | Val mse = 0.9387 Epoch 5: Train mse = 0.4804 | Val mse = 0.9768 Epoch 6: Train mse = 0.4085 | Val mse = 1.6773 Epoch 7: Train mse = 0.3750 | Val mse = 1.0119 Epoch 8: Train mse = 0.3322 | Val mse = 0.9030 Epoch 9: Train mse = 0.3115 | Val mse = 1.0684 Epoch 10: Train mse = 0.2805 | Val mse = 1.3078 Epoch 11: Train mse = 0.2567 | Val mse = 0.9353 Epoch 12: Train mse = 0.2333 | Val mse = 1.3199 Epoch 13: Train mse = 0.2077 | Val mse = 0.8679 Epoch 14: Train mse = 0.2075 | Val mse = 0.9122 Epoch 15: Train mse = 0.1659 | Val mse = 0.8736 Epoch 16: Train mse = 0.1555 | Val mse = 0.9306 Epoch 17: Train mse = 0.1284 | Val mse = 0.9109 Epoch 18: Train mse = 0.1129 | Val mse = 1.1447 Epoch 19: Train mse = 0.0979 | Val mse = 1.4625 Epoch 20: Train mse = 0.0950 | Val mse = 3.3611 Epoch 21: Train mse = 0.0922 | Val mse = 1.4194 Epoch 22: Train mse = 0.0851 | Val mse = 1.3289 Epoch 23: Train mse = 0.0731 | Val mse = 1.1190 Epoch 24: Train mse = 0.0743 | Val mse = 1.6237 Epoch 25: Train mse = 0.0663 | Val mse = 1.2205 Epoch 26: Train mse = 0.0687 | Val mse = 1.1981 Epoch 27: Train mse = 0.0572 | Val mse = 2.0278 Epoch 28: Train mse = 0.0508 | Val mse = 1.7927 Epoch 29: Train mse = 0.0512 | Val mse = 1.3616 Epoch 30: Train mse = 0.0413 | Val mse = 1.8037 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 8.13860075 1 day(s) RMSE : 12.57733751 1 day(s) R2 : -0.87468978 1 day(s) Pearson r : 0.02130714 1 day(s) QLIKE : 0.85029948 full horizon MAE : 8.13860075 full horizon RMSE : 12.57733751 full horizon R2 : -0.87468978 full horizon Pearson r : 0.02130714 full horizon QLIKE : 0.85029948 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/BTCUSDT/Simple_KAN_H1.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=0.820745, max=54.7658 === BTCUSDT | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2399 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514246752 Min value: -9.895452801091551 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.554196812284146 Min value: -3.6087233634991427 Checking X_price_val: Shape: (192, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.53568548101405 Min value: -9.62541592027249 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3459792971639226 Min value: -4.245202118935102 Checking X_price_test: Shape: (480, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.53568548101405 Min value: -9.62541592027249 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.58159887703065 Min value: -2.5700816741597805 Epoch 1: Train mse = 0.9677 | Val mse = 6.2523 Epoch 2: Train mse = 0.7805 | Val mse = 1.3760 Epoch 3: Train mse = 0.6701 | Val mse = 1.6338 Epoch 4: Train mse = 0.5894 | Val mse = 1.0724 Epoch 5: Train mse = 0.5146 | Val mse = 1.0500 Epoch 6: Train mse = 0.4678 | Val mse = 1.1254 Epoch 7: Train mse = 0.4228 | Val mse = 1.6355 Epoch 8: Train mse = 0.3853 | Val mse = 1.0510 Epoch 9: Train mse = 0.3534 | Val mse = 1.2063 Epoch 10: Train mse = 0.3325 | Val mse = 1.3071 Epoch 11: Train mse = 0.3256 | Val mse = 1.4087 Epoch 12: Train mse = 0.3128 | Val mse = 1.0918 Epoch 13: Train mse = 0.3033 | Val mse = 1.1759 Epoch 14: Train mse = 0.2979 | Val mse = 1.3591 Epoch 15: Train mse = 0.2857 | Val mse = 1.3379 Epoch 16: Train mse = 0.2812 | Val mse = 1.1855 Epoch 17: Train mse = 0.2693 | Val mse = 1.2081 Epoch 18: Train mse = 0.2541 | Val mse = 1.2354 Epoch 19: Train mse = 0.2438 | Val mse = 1.3161 Epoch 20: Train mse = 0.2311 | Val mse = 1.6134 Epoch 21: Train mse = 0.2163 | Val mse = 1.3484 Epoch 22: Train mse = 0.2090 | Val mse = 1.4041 Epoch 23: Train mse = 0.2035 | Val mse = 1.9105 Epoch 24: Train mse = 0.1945 | Val mse = 2.0005 Epoch 25: Train mse = 0.1844 | Val mse = 1.7955 Epoch 26: Train mse = 0.1766 | Val mse = 1.7927 Epoch 27: Train mse = 0.1773 | Val mse = 1.4153 Epoch 28: Train mse = 0.1711 | Val mse = 1.4700 Epoch 29: Train mse = 0.1599 | Val mse = 1.5141 Epoch 30: Train mse = 0.1566 | Val mse = 1.6103 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 5.82345109 1 day(s) RMSE : 10.19799445 1 day(s) R2 : -0.23248467 1 day(s) Pearson r : -0.00185861 1 day(s) QLIKE : 0.53901923 3 day(s) MAE : 5.75563338 3 day(s) RMSE : 10.12990938 3 day(s) R2 : -0.21591418 3 day(s) Pearson r : -0.00802635 3 day(s) QLIKE : 0.52295491 5 day(s) MAE : 5.69730542 5 day(s) RMSE : 10.11771105 5 day(s) R2 : -0.21336792 5 day(s) Pearson r : -0.02628184 5 day(s) QLIKE : 0.52210323 full horizon MAE : 5.69730542 full horizon RMSE : 10.11771105 full horizon R2 : -0.21336792 full horizon Pearson r : -0.02628184 full horizon QLIKE : 0.52210323 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/BTCUSDT/Simple_KAN_H5.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=2.12485, max=23.8841 === BTCUSDT | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2399 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514246752 Min value: -9.895452801091551 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.556915178048552 Min value: -3.6122923012225283 Checking X_price_val: Shape: (192, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.53568548101405 Min value: -9.62541592027249 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.346996833643509 Min value: -4.249261289934421 Checking X_price_test: Shape: (480, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.53568548101405 Min value: -9.62541592027249 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.582797894129229 Min value: -2.572850621760107 Epoch 1: Train mse = 0.9658 | Val mse = 3.4370 Epoch 2: Train mse = 0.7845 | Val mse = 1.2931 Epoch 3: Train mse = 0.6722 | Val mse = 1.8873 Epoch 4: Train mse = 0.5908 | Val mse = 1.0717 Epoch 5: Train mse = 0.5288 | Val mse = 0.9507 Epoch 6: Train mse = 0.4723 | Val mse = 1.0121 Epoch 7: Train mse = 0.4370 | Val mse = 0.9643 Epoch 8: Train mse = 0.4155 | Val mse = 1.0443 Epoch 9: Train mse = 0.3877 | Val mse = 1.1398 Epoch 10: Train mse = 0.3777 | Val mse = 1.0652 Epoch 11: Train mse = 0.3694 | Val mse = 1.0284 Epoch 12: Train mse = 0.3562 | Val mse = 1.0542 Epoch 13: Train mse = 0.3431 | Val mse = 1.0767 Epoch 14: Train mse = 0.3324 | Val mse = 1.1218 Epoch 15: Train mse = 0.3162 | Val mse = 1.1898 Epoch 16: Train mse = 0.3070 | Val mse = 1.0945 Epoch 17: Train mse = 0.2978 | Val mse = 1.0523 Epoch 18: Train mse = 0.2974 | Val mse = 1.0863 Epoch 19: Train mse = 0.2848 | Val mse = 1.0983 Epoch 20: Train mse = 0.2799 | Val mse = 1.0485 Epoch 21: Train mse = 0.2742 | Val mse = 1.1146 Epoch 22: Train mse = 0.2665 | Val mse = 1.0721 Epoch 23: Train mse = 0.2624 | Val mse = 1.0946 Epoch 24: Train mse = 0.2559 | Val mse = 1.1149 Epoch 25: Train mse = 0.2478 | Val mse = 1.1033 Epoch 26: Train mse = 0.2411 | Val mse = 1.1029 Epoch 27: Train mse = 0.2341 | Val mse = 1.2052 Epoch 28: Train mse = 0.2231 | Val mse = 1.0931 Epoch 29: Train mse = 0.2163 | Val mse = 1.0855 Epoch 30: Train mse = 0.2110 | Val mse = 1.1289 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 6.62188432 1 day(s) RMSE : 10.51097248 1 day(s) R2 : -0.30929582 1 day(s) Pearson r : -0.03208937 1 day(s) QLIKE : 0.61023793 3 day(s) MAE : 6.58596168 3 day(s) RMSE : 10.49964708 3 day(s) R2 : -0.30629482 3 day(s) Pearson r : -0.04335959 3 day(s) QLIKE : 0.61183922 5 day(s) MAE : 6.65578975 5 day(s) RMSE : 10.60236257 5 day(s) R2 : -0.33239584 5 day(s) Pearson r : -0.05840230 5 day(s) QLIKE : 0.61850224 10 day(s) MAE : 6.72108002 10 day(s) RMSE : 10.79516623 10 day(s) R2 : -0.38096322 10 day(s) Pearson r : -0.08484265 10 day(s) QLIKE : 0.65167496 full horizon MAE : 6.72108002 full horizon RMSE : 10.79516623 full horizon R2 : -0.38096322 full horizon Pearson r : -0.08484265 full horizon QLIKE : 0.65167496 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/BTCUSDT/Simple_KAN_H10.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=1.66643, max=24.4854 === BTCUSDT | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 2399 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (1727, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 27.572355514246752 Min value: -9.895452801091551 Checking X_time_train_core: Shape: (1727, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.8178490491226165 Checking y_train_core (log_mse scaled): Shape: (1727, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 4.564043013270673 Min value: -3.62021477149946 Checking X_price_val: Shape: (192, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.53568548101405 Min value: -9.62541592027249 Checking X_time_val: Shape: (192, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_val (log_mse scaled): Shape: (192, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3500532890780983 Min value: -4.258357261790038 Checking X_price_test: Shape: (480, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 15.53568548101405 Min value: -9.62541592027249 Checking X_time_test: Shape: (480, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.510852258333365 Min value: -1.5280954648836782 Checking y_test (log_mse scaled): Shape: (480, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.5862887708961586 Min value: -2.578858106420128 Epoch 1: Train mse = 0.9458 | Val mse = 4.8254 Epoch 2: Train mse = 0.7860 | Val mse = 1.1387 Epoch 3: Train mse = 0.6754 | Val mse = 1.3654 Epoch 4: Train mse = 0.6001 | Val mse = 1.7691 Epoch 5: Train mse = 0.5405 | Val mse = 1.1357 Epoch 6: Train mse = 0.4982 | Val mse = 1.2155 Epoch 7: Train mse = 0.4747 | Val mse = 1.2795 Epoch 8: Train mse = 0.4565 | Val mse = 1.0976 Epoch 9: Train mse = 0.4231 | Val mse = 1.1916 Epoch 10: Train mse = 0.4059 | Val mse = 1.1063 Epoch 11: Train mse = 0.3941 | Val mse = 1.0082 Epoch 12: Train mse = 0.3809 | Val mse = 1.0510 Epoch 13: Train mse = 0.3677 | Val mse = 1.2347 Epoch 14: Train mse = 0.3551 | Val mse = 1.3544 Epoch 15: Train mse = 0.3415 | Val mse = 1.2495 Epoch 16: Train mse = 0.3319 | Val mse = 1.3614 Epoch 17: Train mse = 0.3188 | Val mse = 1.5409 Epoch 18: Train mse = 0.3193 | Val mse = 1.4874 Epoch 19: Train mse = 0.3136 | Val mse = 1.5253 Epoch 20: Train mse = 0.3055 | Val mse = 1.6405 Epoch 21: Train mse = 0.3038 | Val mse = 1.6023 Epoch 22: Train mse = 0.2966 | Val mse = 1.6163 Epoch 23: Train mse = 0.2911 | Val mse = 1.5803 Epoch 24: Train mse = 0.2860 | Val mse = 1.5717 Epoch 25: Train mse = 0.2819 | Val mse = 1.4083 Epoch 26: Train mse = 0.2757 | Val mse = 1.4354 Epoch 27: Train mse = 0.2720 | Val mse = 1.3041 Epoch 28: Train mse = 0.2646 | Val mse = 1.4788 Epoch 29: Train mse = 0.2602 | Val mse = 1.4300 Epoch 30: Train mse = 0.2565 | Val mse = 1.4941 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 5.69053419 1 day(s) RMSE : 10.09117056 1 day(s) R2 : -0.20679937 1 day(s) Pearson r : -0.01686821 1 day(s) QLIKE : 0.73154797 3 day(s) MAE : 5.74919656 3 day(s) RMSE : 10.16137748 3 day(s) R2 : -0.22348028 3 day(s) Pearson r : -0.01178129 3 day(s) QLIKE : 0.83362848 5 day(s) MAE : 5.87922295 5 day(s) RMSE : 10.26529354 5 day(s) R2 : -0.24902379 5 day(s) Pearson r : -0.02503625 5 day(s) QLIKE : 0.89063744 10 day(s) MAE : 6.61004228 10 day(s) RMSE : 11.20251047 10 day(s) R2 : -0.48714790 10 day(s) Pearson r : -0.05121636 10 day(s) QLIKE : 0.95305829 20 day(s) MAE : 10.31175398 20 day(s) RMSE : 17.94454262 20 day(s) R2 : -2.81717106 20 day(s) Pearson r : -0.04537579 20 day(s) QLIKE : 0.99695651 full horizon MAE : 10.31175398 full horizon RMSE : 17.94454262 full horizon R2 : -2.81717106 full horizon Pearson r : -0.04537579 full horizon QLIKE : 0.99695651 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/BTCUSDT/Simple_KAN_H20.pkl Saved y_true min=0.505764, max=131.49 Saved y_pred min=0.435099, max=97.4616 === EURUSD | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 32.883394272790085 Min value: -3.1812809750988515 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9963965101732293 Min value: -3.718602223017603 Checking X_price_val: Shape: (302, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 41.477005683820344 Min value: -2.633090998920803 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.319059579642504 Min value: -2.975928211620611 Checking X_price_test: Shape: (757, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144347 Min value: -2.633090998920803 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3834322546316056 Min value: -5.349969967608034 Epoch 1: Train mse = 0.9459 | Val mse = 0.8360 Epoch 2: Train mse = 0.7697 | Val mse = 0.7617 Epoch 3: Train mse = 0.5724 | Val mse = 0.7264 Epoch 4: Train mse = 0.4153 | Val mse = 0.6392 Epoch 5: Train mse = 0.3164 | Val mse = 0.6767 Epoch 6: Train mse = 0.3055 | Val mse = 0.5418 Epoch 7: Train mse = 0.2527 | Val mse = 0.5381 Epoch 8: Train mse = 0.2394 | Val mse = 0.5073 Epoch 9: Train mse = 0.2163 | Val mse = 0.4973 Epoch 10: Train mse = 0.2011 | Val mse = 0.4775 Epoch 11: Train mse = 0.1659 | Val mse = 0.4836 Epoch 12: Train mse = 0.1304 | Val mse = 0.5109 Epoch 13: Train mse = 0.1147 | Val mse = 0.5105 Epoch 14: Train mse = 0.1055 | Val mse = 0.4988 Epoch 15: Train mse = 0.0901 | Val mse = 0.5236 Epoch 16: Train mse = 0.0886 | Val mse = 0.5516 Epoch 17: Train mse = 0.0764 | Val mse = 0.5573 Epoch 18: Train mse = 0.0556 | Val mse = 0.5686 Epoch 19: Train mse = 0.0529 | Val mse = 0.5741 Epoch 20: Train mse = 0.0479 | Val mse = 0.5439 Epoch 21: Train mse = 0.0379 | Val mse = 0.5840 Epoch 22: Train mse = 0.0350 | Val mse = 0.5568 Epoch 23: Train mse = 0.0292 | Val mse = 0.5499 Epoch 24: Train mse = 0.0229 | Val mse = 0.5257 Epoch 25: Train mse = 0.0224 | Val mse = 0.5352 Epoch 26: Train mse = 0.0223 | Val mse = 0.5238 Epoch 27: Train mse = 0.0172 | Val mse = 0.5264 Epoch 28: Train mse = 0.0143 | Val mse = 0.5169 Epoch 29: Train mse = 0.0109 | Val mse = 0.5408 Epoch 30: Train mse = 0.0097 | Val mse = 0.5212 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.09491428 1 day(s) RMSE : 0.17759531 1 day(s) R2 : 0.30898839 1 day(s) Pearson r : 0.57442306 1 day(s) QLIKE : 0.38893317 full horizon MAE : 0.09491428 full horizon RMSE : 0.17759531 full horizon R2 : 0.30898839 full horizon Pearson r : 0.57442306 full horizon QLIKE : 0.38893317 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/EURUSD/Simple_KAN_H1.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00485749, max=0.683331 === EURUSD | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 32.883394272790085 Min value: -3.1812809750988515 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.9970127449111077 Min value: -3.720451582171488 Checking X_price_val: Shape: (302, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 41.477005683820344 Min value: -2.633090998920803 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.319427111757353 Min value: -2.9775048778703237 Checking X_price_test: Shape: (757, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144347 Min value: -2.633090998920803 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3838234229198263 Min value: -5.352418327671095 Epoch 1: Train mse = 0.9411 | Val mse = 0.9225 Epoch 2: Train mse = 0.8420 | Val mse = 0.8960 Epoch 3: Train mse = 0.7137 | Val mse = 0.8350 Epoch 4: Train mse = 0.5723 | Val mse = 0.7393 Epoch 5: Train mse = 0.4703 | Val mse = 0.5958 Epoch 6: Train mse = 0.3415 | Val mse = 0.5983 Epoch 7: Train mse = 0.2796 | Val mse = 0.5707 Epoch 8: Train mse = 0.2439 | Val mse = 0.5406 Epoch 9: Train mse = 0.2137 | Val mse = 0.5143 Epoch 10: Train mse = 0.1918 | Val mse = 0.5300 Epoch 11: Train mse = 0.1731 | Val mse = 0.5599 Epoch 12: Train mse = 0.1539 | Val mse = 0.5394 Epoch 13: Train mse = 0.1429 | Val mse = 0.5588 Epoch 14: Train mse = 0.1303 | Val mse = 0.5631 Epoch 15: Train mse = 0.1162 | Val mse = 0.5572 Epoch 16: Train mse = 0.1032 | Val mse = 0.5684 Epoch 17: Train mse = 0.0952 | Val mse = 0.5893 Epoch 18: Train mse = 0.0900 | Val mse = 0.5946 Epoch 19: Train mse = 0.0809 | Val mse = 0.5732 Epoch 20: Train mse = 0.0739 | Val mse = 0.5945 Epoch 21: Train mse = 0.0691 | Val mse = 0.5812 Epoch 22: Train mse = 0.0606 | Val mse = 0.5809 Epoch 23: Train mse = 0.0542 | Val mse = 0.5938 Epoch 24: Train mse = 0.0492 | Val mse = 0.5887 Epoch 25: Train mse = 0.0441 | Val mse = 0.6032 Epoch 26: Train mse = 0.0399 | Val mse = 0.6002 Epoch 27: Train mse = 0.0376 | Val mse = 0.6090 Epoch 28: Train mse = 0.0344 | Val mse = 0.6110 Epoch 29: Train mse = 0.0303 | Val mse = 0.6045 Epoch 30: Train mse = 0.0272 | Val mse = 0.6093 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.10499429 1 day(s) RMSE : 0.17949415 1 day(s) R2 : 0.29413286 1 day(s) Pearson r : 0.54864316 1 day(s) QLIKE : 0.41010425 3 day(s) MAE : 0.10573220 3 day(s) RMSE : 0.18795305 3 day(s) R2 : 0.22006446 3 day(s) Pearson r : 0.47889539 3 day(s) QLIKE : 0.47402730 5 day(s) MAE : 0.10652936 5 day(s) RMSE : 0.18927968 5 day(s) R2 : 0.20002715 5 day(s) Pearson r : 0.46411943 5 day(s) QLIKE : 0.51436557 full horizon MAE : 0.10652936 full horizon RMSE : 0.18927968 full horizon R2 : 0.20002715 full horizon Pearson r : 0.46411943 full horizon QLIKE : 0.51436557 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/EURUSD/Simple_KAN_H5.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00265448, max=0.942698 === EURUSD | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 32.883394272790085 Min value: -3.1812809750988515 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.997326788566986 Min value: -3.719996871136433 Checking X_price_val: Shape: (302, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 41.477005683820344 Min value: -2.633090998920803 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.319755344427202 Min value: -2.977065724545838 Checking X_price_test: Shape: (757, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144347 Min value: -2.633090998920803 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.384150307095678 Min value: -5.351929442359286 Epoch 1: Train mse = 0.9569 | Val mse = 1.0307 Epoch 2: Train mse = 0.8781 | Val mse = 0.8811 Epoch 3: Train mse = 0.7903 | Val mse = 0.8662 Epoch 4: Train mse = 0.6464 | Val mse = 0.7907 Epoch 5: Train mse = 0.5335 | Val mse = 0.7208 Epoch 6: Train mse = 0.4160 | Val mse = 0.6152 Epoch 7: Train mse = 0.3265 | Val mse = 0.6118 Epoch 8: Train mse = 0.2705 | Val mse = 0.5728 Epoch 9: Train mse = 0.2379 | Val mse = 0.5968 Epoch 10: Train mse = 0.2122 | Val mse = 0.5843 Epoch 11: Train mse = 0.1966 | Val mse = 0.5778 Epoch 12: Train mse = 0.1839 | Val mse = 0.5702 Epoch 13: Train mse = 0.1756 | Val mse = 0.5900 Epoch 14: Train mse = 0.1702 | Val mse = 0.5793 Epoch 15: Train mse = 0.1596 | Val mse = 0.5835 Epoch 16: Train mse = 0.1480 | Val mse = 0.5771 Epoch 17: Train mse = 0.1415 | Val mse = 0.5870 Epoch 18: Train mse = 0.1354 | Val mse = 0.5739 Epoch 19: Train mse = 0.1296 | Val mse = 0.5890 Epoch 20: Train mse = 0.1235 | Val mse = 0.5853 Epoch 21: Train mse = 0.1181 | Val mse = 0.6008 Epoch 22: Train mse = 0.1130 | Val mse = 0.5975 Epoch 23: Train mse = 0.1029 | Val mse = 0.6108 Epoch 24: Train mse = 0.0982 | Val mse = 0.6086 Epoch 25: Train mse = 0.0888 | Val mse = 0.6154 Epoch 26: Train mse = 0.0796 | Val mse = 0.6266 Epoch 27: Train mse = 0.0752 | Val mse = 0.6241 Epoch 28: Train mse = 0.0657 | Val mse = 0.6472 Epoch 29: Train mse = 0.0628 | Val mse = 0.6408 Epoch 30: Train mse = 0.0582 | Val mse = 0.6627 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.11183751 1 day(s) RMSE : 0.19050185 1 day(s) R2 : 0.20490183 1 day(s) Pearson r : 0.48022368 1 day(s) QLIKE : 0.47743373 3 day(s) MAE : 0.11470704 3 day(s) RMSE : 0.19613978 3 day(s) R2 : 0.15064089 3 day(s) Pearson r : 0.44405882 3 day(s) QLIKE : 0.53790745 5 day(s) MAE : 0.11531436 5 day(s) RMSE : 0.19692296 5 day(s) R2 : 0.13411546 5 day(s) Pearson r : 0.43731184 5 day(s) QLIKE : 0.58499440 10 day(s) MAE : 0.11878762 10 day(s) RMSE : 0.20179227 10 day(s) R2 : 0.08208551 10 day(s) Pearson r : 0.40963187 10 day(s) QLIKE : 0.57625799 full horizon MAE : 0.11878762 full horizon RMSE : 0.20179227 full horizon R2 : 0.08208551 full horizon Pearson r : 0.40963187 full horizon QLIKE : 0.57625799 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/EURUSD/Simple_KAN_H10.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00293483, max=1.00033 === EURUSD | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3782 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2723, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 32.883394272790085 Min value: -3.1812809750988515 Checking X_time_train_core: Shape: (2723, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.7683468970942753 Checking y_train_core (log_mse scaled): Shape: (2723, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.998957169490066 Min value: -3.7200073694899434 Checking X_price_val: Shape: (302, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 41.477005683820344 Min value: -2.633090998920803 Checking X_time_val: Shape: (302, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_val (log_mse scaled): Shape: (302, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3212202110774225 Min value: -2.9768947428361368 Checking X_price_test: Shape: (757, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 8.131631151144347 Min value: -2.633090998920803 Checking X_time_test: Shape: (757, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.474527252926688 Min value: -1.4974014475059652 Checking y_test (log_mse scaled): Shape: (757, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3856309038737162 Min value: -5.3523385822659755 Epoch 1: Train mse = 0.9524 | Val mse = 0.9543 Epoch 2: Train mse = 0.8726 | Val mse = 0.8987 Epoch 3: Train mse = 0.7890 | Val mse = 0.8489 Epoch 4: Train mse = 0.6637 | Val mse = 0.8009 Epoch 5: Train mse = 0.5576 | Val mse = 0.7242 Epoch 6: Train mse = 0.4237 | Val mse = 0.6625 Epoch 7: Train mse = 0.3252 | Val mse = 0.6084 Epoch 8: Train mse = 0.2840 | Val mse = 0.5212 Epoch 9: Train mse = 0.2493 | Val mse = 0.5130 Epoch 10: Train mse = 0.2284 | Val mse = 0.5078 Epoch 11: Train mse = 0.2171 | Val mse = 0.5289 Epoch 12: Train mse = 0.2072 | Val mse = 0.5303 Epoch 13: Train mse = 0.1975 | Val mse = 0.5304 Epoch 14: Train mse = 0.1908 | Val mse = 0.5427 Epoch 15: Train mse = 0.1826 | Val mse = 0.5379 Epoch 16: Train mse = 0.1740 | Val mse = 0.5590 Epoch 17: Train mse = 0.1662 | Val mse = 0.5621 Epoch 18: Train mse = 0.1591 | Val mse = 0.5605 Epoch 19: Train mse = 0.1543 | Val mse = 0.5729 Epoch 20: Train mse = 0.1474 | Val mse = 0.5717 Epoch 21: Train mse = 0.1435 | Val mse = 0.5753 Epoch 22: Train mse = 0.1382 | Val mse = 0.5779 Epoch 23: Train mse = 0.1325 | Val mse = 0.5711 Epoch 24: Train mse = 0.1300 | Val mse = 0.5705 Epoch 25: Train mse = 0.1267 | Val mse = 0.5908 Epoch 26: Train mse = 0.1222 | Val mse = 0.5701 Epoch 27: Train mse = 0.1191 | Val mse = 0.5794 Epoch 28: Train mse = 0.1116 | Val mse = 0.5986 Epoch 29: Train mse = 0.1093 | Val mse = 0.6016 Epoch 30: Train mse = 0.1039 | Val mse = 0.5950 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.12204112 1 day(s) RMSE : 0.20650889 1 day(s) R2 : 0.06567093 1 day(s) Pearson r : 0.39679037 1 day(s) QLIKE : 0.49416424 3 day(s) MAE : 0.12352359 3 day(s) RMSE : 0.20620768 3 day(s) R2 : 0.06120739 3 day(s) Pearson r : 0.39463298 3 day(s) QLIKE : 0.51977512 5 day(s) MAE : 0.12439277 5 day(s) RMSE : 0.20655070 5 day(s) R2 : 0.04737798 5 day(s) Pearson r : 0.38707727 5 day(s) QLIKE : 0.53100790 10 day(s) MAE : 0.12520048 10 day(s) RMSE : 0.20732000 10 day(s) R2 : 0.03110753 10 day(s) Pearson r : 0.37850840 10 day(s) QLIKE : 0.53271209 20 day(s) MAE : 0.12970765 20 day(s) RMSE : 0.20972222 20 day(s) R2 : -0.00316927 20 day(s) Pearson r : 0.37101763 20 day(s) QLIKE : 0.54586285 full horizon MAE : 0.12970765 full horizon RMSE : 0.20972222 full horizon R2 : -0.00316927 full horizon Pearson r : 0.37101763 full horizon QLIKE : 0.54586285 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/EURUSD/Simple_KAN_H20.pkl Saved y_true min=9.56189e-05, max=2.66457 Saved y_pred min=0.00379213, max=0.880942 === GOLD | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 5534 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 24.476276295980508 Min value: -4.11251005137408 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.28323490000094 Min value: -5.250298730157779 Checking X_price_val: Shape: (443, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722397423 Min value: -4.11251005137408 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.7902797110229713 Min value: -2.119473437475616 Checking X_price_test: Shape: (1107, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698434 Min value: -4.11251005137408 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5405778347636028 Min value: -2.3606736150182797 Epoch 1: Train mse = 0.9784 | Val mse = 0.4572 Epoch 2: Train mse = 0.8045 | Val mse = 0.7247 Epoch 3: Train mse = 0.6376 | Val mse = 0.4796 Epoch 4: Train mse = 0.4392 | Val mse = 0.4131 Epoch 5: Train mse = 0.3412 | Val mse = 0.4602 Epoch 6: Train mse = 0.3062 | Val mse = 0.3424 Epoch 7: Train mse = 0.2533 | Val mse = 0.3736 Epoch 8: Train mse = 0.2235 | Val mse = 0.3980 Epoch 9: Train mse = 0.1953 | Val mse = 0.3878 Epoch 10: Train mse = 0.1777 | Val mse = 0.3359 Epoch 11: Train mse = 0.1515 | Val mse = 0.3563 Epoch 12: Train mse = 0.1231 | Val mse = 0.3724 Epoch 13: Train mse = 0.1011 | Val mse = 0.3815 Epoch 14: Train mse = 0.0881 | Val mse = 0.3751 Epoch 15: Train mse = 0.0776 | Val mse = 0.3500 Epoch 16: Train mse = 0.0688 | Val mse = 0.3938 Epoch 17: Train mse = 0.0593 | Val mse = 0.4051 Epoch 18: Train mse = 0.0544 | Val mse = 0.3742 Epoch 19: Train mse = 0.0500 | Val mse = 0.3877 Epoch 20: Train mse = 0.0464 | Val mse = 0.4668 Epoch 21: Train mse = 0.0412 | Val mse = 0.3768 Epoch 22: Train mse = 0.0330 | Val mse = 0.3312 Epoch 23: Train mse = 0.0278 | Val mse = 0.3658 Epoch 24: Train mse = 0.0263 | Val mse = 0.3383 Epoch 25: Train mse = 0.0223 | Val mse = 0.3207 Epoch 26: Train mse = 0.0209 | Val mse = 0.3694 Epoch 27: Train mse = 0.0186 | Val mse = 0.3008 Epoch 28: Train mse = 0.0151 | Val mse = 0.4344 Epoch 29: Train mse = 0.0128 | Val mse = 0.3296 Epoch 30: Train mse = 0.0110 | Val mse = 0.3886 Epoch 31: Train mse = 0.0098 | Val mse = 0.3715 Epoch 32: Train mse = 0.0083 | Val mse = 0.3299 Epoch 33: Train mse = 0.0063 | Val mse = 0.3950 Epoch 34: Train mse = 0.0058 | Val mse = 0.3434 Epoch 35: Train mse = 0.0047 | Val mse = 0.3443 Epoch 36: Train mse = 0.0037 | Val mse = 0.3667 Epoch 37: Train mse = 0.0035 | Val mse = 0.3648 Early stopping triggered at epoch 37. Parameters used in the single-fit model: input_dim: 420 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.38676369 1 day(s) RMSE : 0.66744985 1 day(s) R2 : -0.03737743 1 day(s) Pearson r : 0.31833814 1 day(s) QLIKE : 0.94085391 full horizon MAE : 0.38676369 full horizon RMSE : 0.66744985 full horizon R2 : -0.03737743 full horizon Pearson r : 0.31833814 full horizon QLIKE : 0.94085391 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/GOLD/Simple_KAN_H1.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.00151417, max=4.90651 === GOLD | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 5534 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 24.476276295980508 Min value: -4.11251005137408 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.283677745863046 Min value: -5.250623802304629 Checking X_price_val: Shape: (443, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722397423 Min value: -4.11251005137408 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.7906723083316343 Min value: -2.1194793742414912 Checking X_price_test: Shape: (1107, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698434 Min value: -4.11251005137408 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5409449791338357 Min value: -2.3607041381163065 Epoch 1: Train mse = 0.9525 | Val mse = 0.4721 Epoch 2: Train mse = 0.8112 | Val mse = 0.8140 Epoch 3: Train mse = 0.6319 | Val mse = 0.6563 Epoch 4: Train mse = 0.4541 | Val mse = 0.6282 Epoch 5: Train mse = 0.3632 | Val mse = 0.5353 Epoch 6: Train mse = 0.2906 | Val mse = 0.5832 Epoch 7: Train mse = 0.2380 | Val mse = 0.6210 Epoch 8: Train mse = 0.2086 | Val mse = 0.6716 Epoch 9: Train mse = 0.1781 | Val mse = 0.5720 Epoch 10: Train mse = 0.1600 | Val mse = 0.5357 Epoch 11: Train mse = 0.1378 | Val mse = 0.5577 Epoch 12: Train mse = 0.1202 | Val mse = 0.5714 Epoch 13: Train mse = 0.1147 | Val mse = 0.6073 Epoch 14: Train mse = 0.1019 | Val mse = 0.6614 Epoch 15: Train mse = 0.0929 | Val mse = 0.5657 Epoch 16: Train mse = 0.0842 | Val mse = 0.5773 Epoch 17: Train mse = 0.0758 | Val mse = 0.5916 Epoch 18: Train mse = 0.0713 | Val mse = 0.5440 Epoch 19: Train mse = 0.0651 | Val mse = 0.5866 Epoch 20: Train mse = 0.0605 | Val mse = 0.5377 Epoch 21: Train mse = 0.0595 | Val mse = 0.7307 Epoch 22: Train mse = 0.0631 | Val mse = 0.5537 Epoch 23: Train mse = 0.0585 | Val mse = 0.5791 Epoch 24: Train mse = 0.0539 | Val mse = 0.5696 Epoch 25: Train mse = 0.0500 | Val mse = 0.5718 Epoch 26: Train mse = 0.0454 | Val mse = 0.5980 Epoch 27: Train mse = 0.0425 | Val mse = 0.6870 Epoch 28: Train mse = 0.0378 | Val mse = 0.6056 Epoch 29: Train mse = 0.0334 | Val mse = 0.5737 Epoch 30: Train mse = 0.0295 | Val mse = 0.6576 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.43261017 1 day(s) RMSE : 0.72301273 1 day(s) R2 : -0.21728257 1 day(s) Pearson r : -0.00454430 1 day(s) QLIKE : 0.60429656 3 day(s) MAE : 0.43293494 3 day(s) RMSE : 0.72236446 3 day(s) R2 : -0.21408600 3 day(s) Pearson r : 0.00375034 3 day(s) QLIKE : 0.66262816 5 day(s) MAE : 0.43836621 5 day(s) RMSE : 0.72398674 5 day(s) R2 : -0.21607568 5 day(s) Pearson r : 0.03062642 5 day(s) QLIKE : 0.70005148 full horizon MAE : 0.43836621 full horizon RMSE : 0.72398674 full horizon R2 : -0.21607568 full horizon Pearson r : 0.03062642 full horizon QLIKE : 0.70005148 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/GOLD/Simple_KAN_H5.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.0887005, max=1.09069 === GOLD | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 5534 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 24.476276295980508 Min value: -4.11251005137408 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.2851543622719985 Min value: -5.254244390683224 Checking X_price_val: Shape: (443, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722397423 Min value: -4.11251005137408 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.7918153902171272 Min value: -2.1209816396053798 Checking X_price_test: Shape: (1107, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698434 Min value: -4.11251005137408 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5419191122017333 Min value: -2.3623696000304566 Epoch 1: Train mse = 0.9589 | Val mse = 0.4969 Epoch 2: Train mse = 0.8574 | Val mse = 0.5821 Epoch 3: Train mse = 0.6787 | Val mse = 0.6394 Epoch 4: Train mse = 0.5145 | Val mse = 0.5872 Epoch 5: Train mse = 0.3981 | Val mse = 0.5317 Epoch 6: Train mse = 0.3156 | Val mse = 0.5342 Epoch 7: Train mse = 0.2587 | Val mse = 0.5327 Epoch 8: Train mse = 0.2275 | Val mse = 0.5322 Epoch 9: Train mse = 0.2015 | Val mse = 0.6051 Epoch 10: Train mse = 0.1746 | Val mse = 0.5668 Epoch 11: Train mse = 0.1521 | Val mse = 0.6129 Epoch 12: Train mse = 0.1420 | Val mse = 0.5687 Epoch 13: Train mse = 0.1331 | Val mse = 0.6146 Epoch 14: Train mse = 0.1212 | Val mse = 0.6169 Epoch 15: Train mse = 0.1150 | Val mse = 0.5946 Epoch 16: Train mse = 0.1081 | Val mse = 0.6037 Epoch 17: Train mse = 0.1038 | Val mse = 0.5944 Epoch 18: Train mse = 0.1001 | Val mse = 0.6333 Epoch 19: Train mse = 0.0960 | Val mse = 0.5982 Epoch 20: Train mse = 0.0893 | Val mse = 0.6201 Epoch 21: Train mse = 0.0866 | Val mse = 0.5753 Epoch 22: Train mse = 0.0816 | Val mse = 0.6402 Epoch 23: Train mse = 0.0761 | Val mse = 0.5879 Epoch 24: Train mse = 0.0714 | Val mse = 0.6175 Epoch 25: Train mse = 0.0672 | Val mse = 0.6377 Epoch 26: Train mse = 0.0658 | Val mse = 0.6018 Epoch 27: Train mse = 0.0621 | Val mse = 0.5868 Epoch 28: Train mse = 0.0582 | Val mse = 0.6277 Epoch 29: Train mse = 0.0550 | Val mse = 0.5891 Epoch 30: Train mse = 0.0512 | Val mse = 0.6193 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.44191034 1 day(s) RMSE : 0.73093938 1 day(s) R2 : -0.24411991 1 day(s) Pearson r : 0.02979442 1 day(s) QLIKE : 0.56731346 3 day(s) MAE : 0.44417002 3 day(s) RMSE : 0.73278460 3 day(s) R2 : -0.24936512 3 day(s) Pearson r : 0.03331294 3 day(s) QLIKE : 0.60051266 5 day(s) MAE : 0.45005571 5 day(s) RMSE : 0.73767078 5 day(s) R2 : -0.26248013 5 day(s) Pearson r : 0.04255246 5 day(s) QLIKE : 0.62789751 10 day(s) MAE : 0.45072328 10 day(s) RMSE : 0.74178540 10 day(s) R2 : -0.26236337 10 day(s) Pearson r : 0.03562448 10 day(s) QLIKE : 0.62136987 full horizon MAE : 0.45072328 full horizon RMSE : 0.74178540 full horizon R2 : -0.26236337 full horizon Pearson r : 0.03562448 full horizon QLIKE : 0.62136987 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/GOLD/Simple_KAN_H10.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.121399, max=0.825749 === GOLD | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 5534 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (3984, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 24.476276295980508 Min value: -4.11251005137408 Checking X_time_train_core: Shape: (3984, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.678060596158252 Checking y_train_core (log_mse scaled): Shape: (3984, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.2870228653696634 Min value: -5.257736685381005 Checking X_price_val: Shape: (443, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 11.324782722397423 Min value: -4.11251005137408 Checking X_time_val: Shape: (443, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_val (log_mse scaled): Shape: (443, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.793333110638287 Min value: -2.1222460659281195 Checking X_price_test: Shape: (1107, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 6.413509896698434 Min value: -4.11251005137408 Checking X_time_test: Shape: (1107, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.869694456765018 Min value: -1.4237179058716 Checking y_test (log_mse scaled): Shape: (1107, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 1.5432591469134391 Min value: -2.3638056623268993 Epoch 1: Train mse = 0.9766 | Val mse = 0.4948 Epoch 2: Train mse = 0.8864 | Val mse = 0.6731 Epoch 3: Train mse = 0.7294 | Val mse = 0.5712 Epoch 4: Train mse = 0.5773 | Val mse = 0.6383 Epoch 5: Train mse = 0.4666 | Val mse = 0.6148 Epoch 6: Train mse = 0.3710 | Val mse = 0.6312 Epoch 7: Train mse = 0.3175 | Val mse = 0.5935 Epoch 8: Train mse = 0.2746 | Val mse = 0.6203 Epoch 9: Train mse = 0.2331 | Val mse = 0.6467 Epoch 10: Train mse = 0.2072 | Val mse = 0.6262 Epoch 11: Train mse = 0.1859 | Val mse = 0.5922 Epoch 12: Train mse = 0.1712 | Val mse = 0.6188 Epoch 13: Train mse = 0.1723 | Val mse = 0.6207 Epoch 14: Train mse = 0.1675 | Val mse = 0.6273 Epoch 15: Train mse = 0.1580 | Val mse = 0.6050 Epoch 16: Train mse = 0.1494 | Val mse = 0.6433 Epoch 17: Train mse = 0.1408 | Val mse = 0.5747 Epoch 18: Train mse = 0.1357 | Val mse = 0.6503 Epoch 19: Train mse = 0.1315 | Val mse = 0.5805 Epoch 20: Train mse = 0.1260 | Val mse = 0.5947 Epoch 21: Train mse = 0.1212 | Val mse = 0.5726 Epoch 22: Train mse = 0.1165 | Val mse = 0.5743 Epoch 23: Train mse = 0.1131 | Val mse = 0.6246 Epoch 24: Train mse = 0.1110 | Val mse = 0.5669 Epoch 25: Train mse = 0.1097 | Val mse = 0.6082 Epoch 26: Train mse = 0.1036 | Val mse = 0.6203 Epoch 27: Train mse = 0.0979 | Val mse = 0.6229 Epoch 28: Train mse = 0.0964 | Val mse = 0.5830 Epoch 29: Train mse = 0.0925 | Val mse = 0.5905 Epoch 30: Train mse = 0.0870 | Val mse = 0.6088 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.43970117 1 day(s) RMSE : 0.72863810 1 day(s) R2 : -0.23629828 1 day(s) Pearson r : 0.01155985 1 day(s) QLIKE : 0.57540255 3 day(s) MAE : 0.44337336 3 day(s) RMSE : 0.73552009 3 day(s) R2 : -0.25871032 3 day(s) Pearson r : -0.00536045 3 day(s) QLIKE : 0.60369680 5 day(s) MAE : 0.45063159 5 day(s) RMSE : 0.74342924 5 day(s) R2 : -0.28226760 5 day(s) Pearson r : -0.01386635 5 day(s) QLIKE : 0.61461152 10 day(s) MAE : 0.45420411 10 day(s) RMSE : 0.74866485 10 day(s) R2 : -0.28588669 10 day(s) Pearson r : -0.01666642 10 day(s) QLIKE : 0.63781097 20 day(s) MAE : 0.46120219 20 day(s) RMSE : 0.75868553 20 day(s) R2 : -0.30104890 20 day(s) Pearson r : -0.03180900 20 day(s) QLIKE : 0.64321047 full horizon MAE : 0.46120219 full horizon RMSE : 0.75868553 full horizon R2 : -0.30104890 full horizon Pearson r : -0.03180900 full horizon QLIKE : 0.64321047 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/GOLD/Simple_KAN_H20.pkl Saved y_true min=0.00380168, max=7.43162 Saved y_pred min=0.0981536, max=0.778884 === SP500 | H=1 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3466 Time steps for y: 1 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790614 Min value: -4.314392989382238 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.598103080699805 Min value: -18.186962127218408 Checking X_price_val: Shape: (277, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.189039765537131 Min value: -4.286988967386013 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3850009093345803 Min value: -2.4181466917899535 Checking X_price_test: Shape: (694, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.658768610066629 Min value: -4.286988967386013 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 1, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.3795163284544953 Min value: -3.2441546953028633 Epoch 1: Train mse = 0.8908 | Val mse = 0.8763 Epoch 2: Train mse = 0.7782 | Val mse = 0.8084 Epoch 3: Train mse = 0.6866 | Val mse = 0.9164 Epoch 4: Train mse = 0.6588 | Val mse = 0.7592 Epoch 5: Train mse = 0.6028 | Val mse = 0.7244 Epoch 6: Train mse = 0.5428 | Val mse = 0.6834 Epoch 7: Train mse = 0.4798 | Val mse = 0.6800 Epoch 8: Train mse = 0.4355 | Val mse = 0.6871 Epoch 9: Train mse = 0.3652 | Val mse = 0.6724 Epoch 10: Train mse = 0.3424 | Val mse = 0.6625 Epoch 11: Train mse = 0.4242 | Val mse = 0.5855 Epoch 12: Train mse = 0.3358 | Val mse = 0.6492 Epoch 13: Train mse = 0.2728 | Val mse = 0.6278 Epoch 14: Train mse = 0.2629 | Val mse = 0.6348 Epoch 15: Train mse = 0.2501 | Val mse = 0.6118 Epoch 16: Train mse = 0.2294 | Val mse = 0.6093 Epoch 17: Train mse = 0.1921 | Val mse = 0.6199 Epoch 18: Train mse = 0.1607 | Val mse = 0.6730 Epoch 19: Train mse = 0.1336 | Val mse = 0.6512 Epoch 20: Train mse = 0.1171 | Val mse = 0.6471 Epoch 21: Train mse = 0.1052 | Val mse = 0.6550 Epoch 22: Train mse = 0.0955 | Val mse = 0.6393 Epoch 23: Train mse = 0.0961 | Val mse = 0.6208 Epoch 24: Train mse = 0.0839 | Val mse = 0.6702 Epoch 25: Train mse = 0.0689 | Val mse = 0.6491 Epoch 26: Train mse = 0.0714 | Val mse = 0.6441 Epoch 27: Train mse = 0.0712 | Val mse = 0.6205 Epoch 28: Train mse = 0.0627 | Val mse = 0.6586 Epoch 29: Train mse = 0.0606 | Val mse = 0.6550 Epoch 30: Train mse = 0.0607 | Val mse = 0.6676 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 1 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.62289648 1 day(s) RMSE : 2.31978380 1 day(s) R2 : 0.32886702 1 day(s) Pearson r : 0.61070392 1 day(s) QLIKE : 0.82294709 full horizon MAE : 0.62289648 full horizon RMSE : 2.31978380 full horizon R2 : 0.32886702 full horizon Pearson r : 0.61070392 full horizon QLIKE : 0.82294709 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/SP500/Simple_KAN_H1.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.0213968, max=13.8523 === SP500 | H=5 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3466 Time steps for y: 5 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790614 Min value: -4.314392989382238 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.596907486751884 Min value: -18.180099645819357 Checking X_price_val: Shape: (277, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.189039765537131 Min value: -4.286988967386013 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.384254029613913 Min value: -2.417116935778614 Checking X_price_test: Shape: (694, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.658768610066629 Min value: -4.286988967386013 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 5, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.3784015875360525 Min value: -3.2428194072829677 Epoch 1: Train mse = 0.9361 | Val mse = 0.9957 Epoch 2: Train mse = 0.8169 | Val mse = 0.8201 Epoch 3: Train mse = 0.7190 | Val mse = 0.8749 Epoch 4: Train mse = 0.6708 | Val mse = 0.8321 Epoch 5: Train mse = 0.6311 | Val mse = 0.8035 Epoch 6: Train mse = 0.5932 | Val mse = 0.8114 Epoch 7: Train mse = 0.5594 | Val mse = 0.8312 Epoch 8: Train mse = 0.5406 | Val mse = 0.8448 Epoch 9: Train mse = 0.5248 | Val mse = 0.8343 Epoch 10: Train mse = 0.5125 | Val mse = 0.8385 Epoch 11: Train mse = 0.5060 | Val mse = 0.8241 Epoch 12: Train mse = 0.4982 | Val mse = 0.8376 Epoch 13: Train mse = 0.4906 | Val mse = 0.8404 Epoch 14: Train mse = 0.4852 | Val mse = 0.8650 Epoch 15: Train mse = 0.4827 | Val mse = 0.8399 Epoch 16: Train mse = 0.4658 | Val mse = 0.8324 Epoch 17: Train mse = 0.4411 | Val mse = 0.8371 Epoch 18: Train mse = 0.4649 | Val mse = 0.8082 Epoch 19: Train mse = 0.4412 | Val mse = 0.8415 Epoch 20: Train mse = 0.4444 | Val mse = 0.8566 Epoch 21: Train mse = 0.4096 | Val mse = 0.8062 Epoch 22: Train mse = 0.3951 | Val mse = 0.8017 Epoch 23: Train mse = 0.3737 | Val mse = 0.7897 Epoch 24: Train mse = 0.3519 | Val mse = 0.8465 Epoch 25: Train mse = 0.3547 | Val mse = 0.8191 Epoch 26: Train mse = 0.3332 | Val mse = 0.8089 Epoch 27: Train mse = 0.2995 | Val mse = 0.7937 Epoch 28: Train mse = 0.2826 | Val mse = 0.8056 Epoch 29: Train mse = 0.2542 | Val mse = 0.8447 Epoch 30: Train mse = 0.2545 | Val mse = 0.7772 Epoch 31: Train mse = 0.2417 | Val mse = 0.7948 Epoch 32: Train mse = 0.2263 | Val mse = 0.7456 Epoch 33: Train mse = 0.1993 | Val mse = 0.8570 Epoch 34: Train mse = 0.1721 | Val mse = 0.7951 Epoch 35: Train mse = 0.1551 | Val mse = 0.7959 Epoch 36: Train mse = 0.1445 | Val mse = 0.8074 Epoch 37: Train mse = 0.1279 | Val mse = 0.8564 Epoch 38: Train mse = 0.1195 | Val mse = 0.8855 Epoch 39: Train mse = 0.1140 | Val mse = 0.8472 Epoch 40: Train mse = 0.1134 | Val mse = 0.8272 Epoch 41: Train mse = 0.1131 | Val mse = 0.8438 Epoch 42: Train mse = 0.1287 | Val mse = 0.8360 Early stopping triggered at epoch 42. Parameters used in the single-fit model: input_dim: 420 output_dim: 5 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.76411568 1 day(s) RMSE : 2.69848498 1 day(s) R2 : 0.09185841 1 day(s) Pearson r : 0.35945335 1 day(s) QLIKE : 1.10441525 3 day(s) MAE : 0.79637018 3 day(s) RMSE : 2.74813004 3 day(s) R2 : 0.05818171 3 day(s) Pearson r : 0.25270709 3 day(s) QLIKE : 1.16567777 5 day(s) MAE : 0.81248715 5 day(s) RMSE : 2.78149061 5 day(s) R2 : 0.03523203 5 day(s) Pearson r : 0.18920904 5 day(s) QLIKE : 1.14960550 full horizon MAE : 0.81248715 full horizon RMSE : 2.78149061 full horizon R2 : 0.03523203 full horizon Pearson r : 0.18920904 full horizon QLIKE : 1.14960550 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/SP500/Simple_KAN_H5.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.0114537, max=7.08836 === SP500 | H=10 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3466 Time steps for y: 10 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790614 Min value: -4.314392989382238 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.596947126941468 Min value: -18.18335453470548 Checking X_price_val: Shape: (277, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.189039765537131 Min value: -4.286988967386013 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.384110213828625 Min value: -2.417987125769155 Checking X_price_test: Shape: (694, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.658768610066629 Min value: -4.286988967386013 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 10, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.378408171115341 Min value: -3.2438145134653293 Epoch 1: Train mse = 0.9520 | Val mse = 0.9979 Epoch 2: Train mse = 0.8281 | Val mse = 0.8288 Epoch 3: Train mse = 0.7498 | Val mse = 0.8393 Epoch 4: Train mse = 0.7015 | Val mse = 0.8030 Epoch 5: Train mse = 0.6614 | Val mse = 0.8254 Epoch 6: Train mse = 0.6267 | Val mse = 0.8429 Epoch 7: Train mse = 0.5972 | Val mse = 0.8580 Epoch 8: Train mse = 0.5719 | Val mse = 0.8593 Epoch 9: Train mse = 0.5560 | Val mse = 0.8283 Epoch 10: Train mse = 0.5514 | Val mse = 0.8443 Epoch 11: Train mse = 0.5470 | Val mse = 0.8694 Epoch 12: Train mse = 0.5385 | Val mse = 0.8480 Epoch 13: Train mse = 0.5280 | Val mse = 0.8683 Epoch 14: Train mse = 0.5155 | Val mse = 0.8499 Epoch 15: Train mse = 0.5029 | Val mse = 0.8337 Epoch 16: Train mse = 0.4908 | Val mse = 0.8486 Epoch 17: Train mse = 0.4786 | Val mse = 0.8486 Epoch 18: Train mse = 0.4719 | Val mse = 0.8594 Epoch 19: Train mse = 0.4598 | Val mse = 0.8593 Epoch 20: Train mse = 0.4528 | Val mse = 0.8353 Epoch 21: Train mse = 0.4415 | Val mse = 0.8501 Epoch 22: Train mse = 0.4317 | Val mse = 0.8526 Epoch 23: Train mse = 0.4251 | Val mse = 0.8419 Epoch 24: Train mse = 0.4043 | Val mse = 0.8016 Epoch 25: Train mse = 0.3871 | Val mse = 0.7865 Epoch 26: Train mse = 0.3804 | Val mse = 0.8240 Epoch 27: Train mse = 0.3466 | Val mse = 0.8679 Epoch 28: Train mse = 0.3220 | Val mse = 0.8431 Epoch 29: Train mse = 0.2977 | Val mse = 0.8468 Epoch 30: Train mse = 0.2772 | Val mse = 0.8379 Epoch 31: Train mse = 0.2646 | Val mse = 0.7500 Epoch 32: Train mse = 0.2552 | Val mse = 0.8015 Epoch 33: Train mse = 0.2637 | Val mse = 0.7646 Epoch 34: Train mse = 0.2429 | Val mse = 0.8006 Epoch 35: Train mse = 0.2186 | Val mse = 0.8439 Epoch 36: Train mse = 0.1988 | Val mse = 0.8469 Epoch 37: Train mse = 0.1776 | Val mse = 0.8368 Epoch 38: Train mse = 0.1661 | Val mse = 0.8223 Epoch 39: Train mse = 0.1640 | Val mse = 0.8134 Epoch 40: Train mse = 0.1458 | Val mse = 0.8044 Epoch 41: Train mse = 0.1295 | Val mse = 0.8171 Early stopping triggered at epoch 41. Parameters used in the single-fit model: input_dim: 420 output_dim: 10 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.97352393 1 day(s) RMSE : 2.67704128 1 day(s) R2 : 0.10623428 1 day(s) Pearson r : 0.34506264 1 day(s) QLIKE : 1.10281049 3 day(s) MAE : 0.93998728 3 day(s) RMSE : 2.72166340 3 day(s) R2 : 0.07623525 3 day(s) Pearson r : 0.28456273 3 day(s) QLIKE : 1.18166441 5 day(s) MAE : 0.87641001 5 day(s) RMSE : 2.75760139 5 day(s) R2 : 0.05173294 5 day(s) Pearson r : 0.22862445 5 day(s) QLIKE : 1.09906847 10 day(s) MAE : 0.88862985 10 day(s) RMSE : 2.82881823 10 day(s) R2 : 0.00225638 10 day(s) Pearson r : 0.11271233 10 day(s) QLIKE : 1.30865029 full horizon MAE : 0.88862985 full horizon RMSE : 2.82881823 full horizon R2 : 0.00225638 full horizon Pearson r : 0.11271233 full horizon QLIKE : 1.30865029 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/SP500/Simple_KAN_H10.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.0175449, max=7.86207 === SP500 | H=20 | Simple_KAN (simple fit) | no_tasks=1 === [mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True Using device: mps Batch size for y: 3466 Time steps for y: 20 Features for y: 1 Starting training without CV: Checking X_price_train_core: Shape: (2495, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 20.304044071790614 Min value: -4.314392989382238 Checking X_time_train_core: Shape: (2495, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -2.0363820510485593 Checking y_train_core (log_mse scaled): Shape: (2495, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.5963112311058607 Min value: -18.187442810157716 Checking X_price_val: Shape: (277, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.189039765537131 Min value: -4.286988967386013 Checking X_time_val: Shape: (277, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_val (log_mse scaled): Shape: (277, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 2.3832820721048678 Min value: -2.446834413184202 Checking X_price_test: Shape: (694, 60, 7) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 14.658768610066629 Min value: -4.286988967386013 Checking X_time_test: Shape: (694, 60, 14) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 9.067132736068572 Min value: -1.7123832950667475 Checking y_test (log_mse scaled): Shape: (694, 20, 1) Contains NaN values: False Contains +inf values: False Contains -inf values: False Contains any infinities: False Max value: 3.377737634830384 Min value: -3.2455347334033506 Epoch 1: Train mse = 0.9629 | Val mse = 1.0620 Epoch 2: Train mse = 0.8668 | Val mse = 0.8460 Epoch 3: Train mse = 0.7696 | Val mse = 0.8445 Epoch 4: Train mse = 0.7263 | Val mse = 0.8153 Epoch 5: Train mse = 0.6806 | Val mse = 0.8712 Epoch 6: Train mse = 0.6451 | Val mse = 0.8437 Epoch 7: Train mse = 0.6207 | Val mse = 0.8691 Epoch 8: Train mse = 0.6055 | Val mse = 0.8659 Epoch 9: Train mse = 0.5934 | Val mse = 0.8665 Epoch 10: Train mse = 0.5821 | Val mse = 0.8730 Epoch 11: Train mse = 0.5648 | Val mse = 0.8869 Epoch 12: Train mse = 0.5604 | Val mse = 0.8649 Epoch 13: Train mse = 0.5536 | Val mse = 0.8866 Epoch 14: Train mse = 0.5462 | Val mse = 0.8971 Epoch 15: Train mse = 0.5376 | Val mse = 0.8937 Epoch 16: Train mse = 0.5303 | Val mse = 0.9095 Epoch 17: Train mse = 0.5205 | Val mse = 0.8949 Epoch 18: Train mse = 0.5116 | Val mse = 0.9077 Epoch 19: Train mse = 0.5042 | Val mse = 0.9151 Epoch 20: Train mse = 0.4981 | Val mse = 0.9253 Epoch 21: Train mse = 0.4918 | Val mse = 0.9183 Epoch 22: Train mse = 0.4876 | Val mse = 0.8835 Epoch 23: Train mse = 0.4767 | Val mse = 0.8970 Epoch 24: Train mse = 0.4686 | Val mse = 0.8912 Epoch 25: Train mse = 0.4659 | Val mse = 0.8799 Epoch 26: Train mse = 0.4513 | Val mse = 0.8766 Epoch 27: Train mse = 0.4362 | Val mse = 0.8940 Epoch 28: Train mse = 0.4251 | Val mse = 0.9264 Epoch 29: Train mse = 0.4168 | Val mse = 0.8710 Epoch 30: Train mse = 0.4029 | Val mse = 0.8455 Early stopping triggered at epoch 30. Parameters used in the single-fit model: input_dim: 420 output_dim: 20 hidden_layers: 3 dropout: 0.00000000 lr: 0.00500000 epochs: 50 batch_size: 512 device: mps verbose: True no_tasks: 1 knots: 10 spline_power: 5 l2_weight: 0.00001000 hidden_dim: 128 patience: 10 min_epochs: 30 min_delta: 0.00010000 loss_type: mse target_is_logvar: True nll_eps: 0.00000000 clamp_logvar_min: -20.00000000 clamp_logvar_max: 20.00000000 student_df: 5.00000000 Results for regular train/test evaluation (without CV): --- Task 1 --- 1 day(s) MAE : 0.67130056 1 day(s) RMSE : 2.67292606 1 day(s) R2 : 0.10898001 1 day(s) Pearson r : 0.44859876 1 day(s) QLIKE : 0.81602564 3 day(s) MAE : 0.69894441 3 day(s) RMSE : 2.70992888 3 day(s) R2 : 0.08418374 3 day(s) Pearson r : 0.35032378 3 day(s) QLIKE : 0.87512684 5 day(s) MAE : 0.70697149 5 day(s) RMSE : 2.75351676 5 day(s) R2 : 0.05454006 5 day(s) Pearson r : 0.25801164 5 day(s) QLIKE : 0.93956371 10 day(s) MAE : 0.72860408 10 day(s) RMSE : 2.80996414 10 day(s) R2 : 0.01551199 10 day(s) Pearson r : 0.13491425 10 day(s) QLIKE : 1.08517304 20 day(s) MAE : 0.73490099 20 day(s) RMSE : 2.82798793 20 day(s) R2 : 0.00296030 20 day(s) Pearson r : 0.08861047 20 day(s) QLIKE : 1.07393316 full horizon MAE : 0.73490099 full horizon RMSE : 2.82798793 full horizon R2 : 0.00296030 full horizon Pearson r : 0.08861047 full horizon QLIKE : 1.07393316 Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/multi_input_results_saved_object/SP500/Simple_KAN_H20.pkl Saved y_true min=0.00323949, max=53.2315 Saved y_pred min=0.19931, max=5.64792
In [824]:
multiple_input_frames = build_metric_frames(multi_input_results_store, outer_horizon="full", pretty_print=True)
export_metrics(multi_input_results_store, "results/metrics_multiple_inputs_all.csv", outer_horizon="full")
export_metrics(multi_input_results_store, "results/metrics_multiple_inputs_all.txt", outer_horizon="full")
=== AAPL | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.462510 2.523389 2.565542 2.606932
Pearson r -0.090941 0.008225 -0.046882 -0.104006
QLIKE 0.575769 0.557520 0.577154 0.595907
R2 -0.063777 -0.058242 -0.059858 -0.061696
RMSE 8.913924 9.195842 9.240946 9.268690
=== MSFT | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.280823 2.342318 2.251885 2.130082
Pearson r 0.110140 0.086747 0.030856 -0.047103
QLIKE 0.443757 0.479544 0.488425 0.524231
R2 0.009284 -0.001142 -0.036023 -0.044969
RMSE 6.671584 6.706749 6.822834 7.058939
=== GE | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.045902 4.237860 4.238666 4.252219
Pearson r 0.134240 0.150633 0.130078 0.104110
QLIKE 0.583395 0.573050 0.608299 0.616018
R2 -0.000819 -0.012368 -0.003321 -0.008271
RMSE 27.437418 27.593684 27.472130 27.540542
=== BAC | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.274453 2.588921 2.369231 2.616549
Pearson r 0.250177 -0.087623 -0.121580 -0.090434
QLIKE 0.453586 0.470565 0.464555 0.564879
R2 0.050949 -0.019164 -0.024120 -0.068346
RMSE 7.562345 7.840424 7.859838 8.026430
=== C | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.830797 2.892440 2.880604 2.900656
Pearson r 0.261140 -0.021245 -0.012972 -0.024523
QLIKE 0.375795 0.448389 0.441054 0.441041
R2 0.018287 -0.060239 -0.049897 -0.045534
RMSE 9.352386 9.758562 9.722207 9.705828
=== BTCUSDT | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 8.138601 5.697305 6.721080 10.311754
Pearson r 0.021307 -0.026282 -0.084843 -0.045376
QLIKE 0.850299 0.522103 0.651675 0.996957
R2 -0.874690 -0.213368 -0.380963 -2.817171
RMSE 12.577338 10.117711 10.795166 17.944543
=== EURUSD | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.094914 0.106529 0.118788 0.129708
Pearson r 0.574423 0.464119 0.409632 0.371018
QLIKE 0.388933 0.514366 0.576258 0.545863
R2 0.308988 0.200027 0.082086 -0.003169
RMSE 0.177595 0.189280 0.201792 0.209722
=== GOLD | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.386764 0.438366 0.450723 0.461202
Pearson r 0.318338 0.030626 0.035624 -0.031809
QLIKE 0.940854 0.700051 0.621370 0.643210
R2 -0.037377 -0.216076 -0.262363 -0.301049
RMSE 0.667450 0.723987 0.741785 0.758686
=== SP500 | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.622896 0.812487 0.888630 0.734901
Pearson r 0.610704 0.189209 0.112712 0.088610
QLIKE 0.822947 1.149605 1.308650 1.073933
R2 0.328867 0.035232 0.002256 0.002960
RMSE 2.319784 2.781491 2.828818 2.827988
Out[824]:
{'mode': 'text',
'path': '/Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/results/metrics_multiple_inputs_all.txt',
'sections': 9}
Results on modesl with more tasks¶
In [ ]:
import os
import pickle
import numpy as np
load_data_object_8_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_8.pkl")
with open(load_data_object_8_file_path, "rb") as f:
structured_data_dict_8 = pickle.load(f)
print("Data dictionary 8 loaded successfully.")
tickers = ["AAPL", "MSFT", "GE", "BAC", "C", "BTCUSDT", "EURUSD", "GOLD", "SP500"]
X_price_map, X_time_map, y_map = {}, {}, {}
missing = []
for t in tickers:
if t not in structured_data_dict_8:
missing.append(t)
continue
entry = structured_data_dict_8[t]
Xp = entry.get("X_other", None)
Xt = entry.get("X_time", None)
Y = entry.get("y", None)
if Xp is None or Y is None:
print(f"[WARN] {t}: missing X_other or y → skipping.")
continue
Xp = np.asarray(Xp, dtype=float)
Xt = None if Xt is None else np.asarray(Xt, dtype=float)
Y = np.asarray(Y, dtype=float)
if Xp.ndim != 3 or Y.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_other={Xp.shape}, y={Y.shape} → skipping.")
continue
if Xt is not None and Xt.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_time={Xt.shape} → setting to None.")
Xt = None
X_price_map[t] = Xp
X_time_map[t] = Xt
y_map[t] = Y
if missing:
print(f"[INFO] Missing tickers in data dict (skipped): {missing}")
for t in tickers:
Xp = X_price_map.get(t); Xt = X_time_map.get(t); Y = y_map.get(t)
print(t, "X_other", None if Xp is None else Xp.shape,
"X_time", None if Xt is None else Xt.shape,
"y", None if Y is None else Y.shape)
multiple_tasks_results_store = {}
multiple_tasks_models = ["Simple_KAN"]
MULTI_TASK_MODELS = {"Simple_KAN"}
MULTI_TASK_BASE_SAVE_DIR = os.path.join(root_folder, objects_relative_path, "multi_task_results_saved_object")
os.makedirs(MULTI_TASK_BASE_SAVE_DIR, exist_ok=True)
_ = run_all_models_for_all(
tickers=final_tickers,
horizons=final_horizons,
model_list=multiple_tasks_models,
X_price_map=X_price_map,
X_time_map=X_time_map,
y_map=y_map,
base_save_dir=MULTI_TASK_BASE_SAVE_DIR,
fixed_params=FIXED_PARAMS_MERGED,
common_params=COMMON,
model_io=MODEL_IO,
multitask_models=MULTI_TASK_MODELS,
param_grids=None,
results_store=multiple_tasks_results_store,
overwrite=False
)
Data dictionary 8 loaded successfully. AAPL X_other (2018, 60, 1) X_time (2018, 60, 14) y (2018, 60, 6) MSFT X_other (2018, 60, 1) X_time (2018, 60, 14) y (2018, 60, 6) GE X_other (2018, 60, 1) X_time (2018, 60, 14) y (2018, 60, 6) BAC X_other (2018, 60, 1) X_time (2018, 60, 14) y (2018, 60, 6) C X_other (2018, 60, 1) X_time (2018, 60, 14) y (2018, 60, 6) BTCUSDT X_other (2399, 60, 1) X_time (2399, 60, 14) y (2399, 60, 6) EURUSD X_other (3782, 60, 1) X_time (3782, 60, 14) y (3782, 60, 6) GOLD X_other (5534, 60, 1) X_time (5534, 60, 14) y (5534, 60, 6) SP500 X_other (3466, 60, 1) X_time (3466, 60, 14) y (3466, 60, 6) [skip] AAPL | H=1 | Simple_KAN (exists, overwrite=False) [skip] AAPL | H=5 | Simple_KAN (exists, overwrite=False) [skip] AAPL | H=10 | Simple_KAN (exists, overwrite=False) [skip] AAPL | H=20 | Simple_KAN (exists, overwrite=False) [skip] MSFT | H=1 | Simple_KAN (exists, overwrite=False) [skip] MSFT | H=5 | Simple_KAN (exists, overwrite=False) [skip] MSFT | H=10 | Simple_KAN (exists, overwrite=False) [skip] MSFT | H=20 | Simple_KAN (exists, overwrite=False) [skip] GE | H=1 | Simple_KAN (exists, overwrite=False) [skip] GE | H=5 | Simple_KAN (exists, overwrite=False) [skip] GE | H=10 | Simple_KAN (exists, overwrite=False) [skip] GE | H=20 | Simple_KAN (exists, overwrite=False) [skip] BAC | H=1 | Simple_KAN (exists, overwrite=False) [skip] BAC | H=5 | Simple_KAN (exists, overwrite=False) [skip] BAC | H=10 | Simple_KAN (exists, overwrite=False) [skip] BAC | H=20 | Simple_KAN (exists, overwrite=False) [skip] C | H=1 | Simple_KAN (exists, overwrite=False) [skip] C | H=5 | Simple_KAN (exists, overwrite=False) [skip] C | H=10 | Simple_KAN (exists, overwrite=False) [skip] C | H=20 | Simple_KAN (exists, overwrite=False) [skip] BTCUSDT | H=1 | Simple_KAN (exists, overwrite=False) [skip] BTCUSDT | H=5 | Simple_KAN (exists, overwrite=False) [skip] BTCUSDT | H=10 | Simple_KAN (exists, overwrite=False) [skip] BTCUSDT | H=20 | Simple_KAN (exists, overwrite=False) [skip] EURUSD | H=1 | Simple_KAN (exists, overwrite=False) [skip] EURUSD | H=5 | Simple_KAN (exists, overwrite=False) [skip] EURUSD | H=10 | Simple_KAN (exists, overwrite=False) [skip] EURUSD | H=20 | Simple_KAN (exists, overwrite=False) [skip] GOLD | H=1 | Simple_KAN (exists, overwrite=False) [skip] GOLD | H=5 | Simple_KAN (exists, overwrite=False) [skip] GOLD | H=10 | Simple_KAN (exists, overwrite=False) [skip] GOLD | H=20 | Simple_KAN (exists, overwrite=False) [skip] SP500 | H=1 | Simple_KAN (exists, overwrite=False) [skip] SP500 | H=5 | Simple_KAN (exists, overwrite=False) [skip] SP500 | H=10 | Simple_KAN (exists, overwrite=False) [skip] SP500 | H=20 | Simple_KAN (exists, overwrite=False)
In [827]:
frames = build_metric_frames(
multiple_tasks_results_store,
task_name="Task 0",
outer_horizon="full",
pretty_print=True
)
os.makedirs("results", exist_ok=True)
export_metrics(multiple_tasks_results_store, "results/multiple_task_all.csv", outer_horizon="full")
export_metrics(multiple_tasks_results_store, "results/multiple_task_all.txt", outer_horizon="full")
print("Saved metrics to results/multiple_task_all.csv and results/multiple_task_all.txt")
=== AAPL | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.233370 2.420229 2.496394 2.542620
Pearson r 0.403259 0.374102 0.226162 0.176836
QLIKE 0.339369 0.390355 0.456250 0.483922
R2 0.049072 0.032749 0.005055 -0.029823
RMSE 8.427859 8.791616 8.953487 9.128504
=== MSFT | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.025080 2.072445 2.086677 2.150716
Pearson r 0.054634 -0.008634 -0.032227 -0.021689
QLIKE 0.479356 0.485605 0.488221 0.494321
R2 -0.017841 -0.022692 -0.025370 -0.023969
RMSE 6.762301 6.778548 6.787668 6.987651
=== GE | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.109229 4.179038 4.323730 4.390263
Pearson r 0.005856 -0.003281 -0.009208 -0.019724
QLIKE 0.693059 0.717717 0.716372 0.750153
R2 -0.011797 -0.014988 -0.010958 -0.017834
RMSE 27.587478 27.629370 27.576487 27.670843
=== BAC | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.246216 2.245612 2.253850 2.858156
Pearson r 0.365015 0.087234 0.025997 0.051217
QLIKE 0.337393 0.417836 0.424769 0.467983
R2 0.123384 -0.010612 -0.015985 -0.483034
RMSE 7.268024 7.807460 7.828558 9.456755
=== C | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.761594 2.970135 3.076174 2.866582
Pearson r 0.432482 0.282608 0.161675 0.039654
QLIKE 0.312212 0.378857 0.418921 0.430557
R2 0.175541 0.063556 0.000168 -0.038355
RMSE 8.570666 9.171173 9.487568 9.672447
=== BTCUSDT | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.085041 4.623494 4.735971 4.865638
Pearson r 0.336386 0.168642 0.109898 0.013987
QLIKE 0.257825 0.372967 0.381542 0.398666
R2 0.017161 -0.024781 -0.027522 -0.057018
RMSE 9.106789 9.298258 9.311806 9.442844
=== EURUSD | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.089256 0.098608 0.098084 0.101612
Pearson r 0.589095 0.457400 0.432414 0.355138
QLIKE 0.379050 0.490405 0.474720 0.525571
R2 0.342939 0.173361 0.141422 0.070494
RMSE 0.173178 0.192408 0.195161 0.201875
=== GOLD | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.320758 0.386195 0.404782 0.441989
Pearson r 0.482068 0.369484 0.358787 0.226124
QLIKE 0.505835 0.645387 0.548921 0.630642
R2 0.190177 -0.004309 -0.196087 -0.234453
RMSE 0.589719 0.657936 0.722050 0.739013
=== SP500 | Simple_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.512058 0.633248 0.661464 0.749131
Pearson r 0.562249 0.371859 0.278503 0.200656
QLIKE 0.639160 0.666840 0.760797 0.807649
R2 0.246671 0.089974 0.053271 -0.116840
RMSE 2.457739 2.701426 2.755551 2.993070
Saved metrics to results/multiple_task_all.csv and results/multiple_task_all.txt
Results on hierarhical models¶
In [835]:
import os
import pickle
import numpy as np
load_data_object_8_file_path = os.path.join(root_folder, objects_relative_path, "structured_data_dict_8.pkl")
with open(load_data_object_8_file_path, "rb") as f:
structured_data_dict_8 = pickle.load(f)
print("Data dictionary 8 loaded successfully.")
final_tickers = ["AAPL", "MSFT", "GE", "BAC", "C", "BTCUSDT", "EURUSD", "GOLD", "SP500"]
final_horizons = [1, 5, 10, 20]
X_price_map, X_time_map, y_map = {}, {}, {}
missing = []
for t in final_tickers:
if t not in structured_data_dict_8:
missing.append(t); continue
entry = structured_data_dict_8[t]
Xp = entry.get("X_other"); Xt = entry.get("X_time"); Y = entry.get("y")
if Xp is None or Y is None:
print(f"[WARN] {t}: missing X_other or y → skipping."); continue
Xp = np.asarray(Xp, dtype=float)
Xt = None if Xt is None else np.asarray(Xt, dtype=float)
Y = np.asarray(Y, dtype=float)
if Xp.ndim != 3 or Y.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_other={Xp.shape}, y={Y.shape} → skipping."); continue
if Xt is not None and Xt.ndim != 3:
print(f"[WARN] {t}: unexpected dims X_time={Xt.shape} → setting to None."); Xt = None
X_price_map[t] = Xp; X_time_map[t] = Xt; y_map[t] = Y
if missing:
print(f"[INFO] Missing tickers in data dict (skipped): {missing}")
FIXED_PARAMS = {
"Custom_KAN": dict(
lr=5e-4, dropout=0.0, l2_weight=1e-5, batch_size=128, hidden_layers=3, hidden_dim=128, knots=8, spline_power=5,
epochs=50, patience=10, min_epochs=30, min_delta=1e-4
),
"Custom_KAN_LSTM": dict(
lr=5e-4, dropout=0.0, l2_weight=1e-5, batch_size=32, hidden_layers=2, hidden_dim=32, knots=8, spline_power=5,
epochs=20, patience=5, min_epochs=10, min_delta=1e-4
)
}
MODEL_IO = {
"Custom_KAN": dict(merge_price_time=False, flatten=True),
"Custom_KAN_LSTM": dict(merge_price_time=False, flatten=False),
}
COMMON = dict(
use_nested_cv=False,
single_holdout=False,
normalize_X=True,
normalize_Time=True,
normalize_y=True,
verbose=True,
target_mode="log_mse"
)
hierarchical_results_store = {}
hierarchical_tasks_models = ["Custom_KAN", "Custom_KAN_LSTM"]
MULTI_TASK_MODELS = {"Custom_KAN", "Custom_KAN_LSTM"}
HIERARCHICAL_BASE_SAVE_DIR = os.path.join(root_folder, objects_relative_path, "hierarchical_results_saved_object")
os.makedirs(HIERARCHICAL_BASE_SAVE_DIR, exist_ok=True)
_ = run_all_models_for_all(
tickers=final_tickers,
horizons=final_horizons,
model_list=hierarchical_tasks_models,
X_price_map=X_price_map,
X_time_map=X_time_map,
y_map=y_map,
base_save_dir=HIERARCHICAL_BASE_SAVE_DIR,
fixed_params=FIXED_PARAMS,
common_params=COMMON,
model_io=MODEL_IO,
multitask_models=MULTI_TASK_MODELS,
param_grids=None,
results_store=hierarchical_results_store,
overwrite=False
)
Data dictionary 8 loaded successfully.
=== AAPL | H=1 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.099874372714204
Min value: -0.03434294798653995
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.133266415235102
Min value: -13.098170041087558
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.051920323212192546
Min value: -0.0338203458440787
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.9069843280619727
Min value: -6.555005878598166
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.19306601030720755
Min value: -0.03421247164703902
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.8950185841626324
Min value: -13.098170041087558
Epoch 001 | phase=1 | train_loss=5.2656 | val_main=0.580879
Epoch 002 | phase=1 | train_loss=4.7336 | val_main=0.580655
Epoch 003 | phase=1 | train_loss=5.0280 | val_main=0.580830
Epoch 004 | phase=1 | train_loss=5.0805 | val_main=0.580853
Epoch 005 | phase=1 | train_loss=4.7396 | val_main=0.580849
Epoch 006 | phase=1 | train_loss=4.7586 | val_main=0.580791
Epoch 007 | phase=1 | train_loss=5.0139 | val_main=0.580762
Epoch 008 | phase=1 | train_loss=4.9333 | val_main=0.580841
Epoch 009 | phase=1 | train_loss=4.9417 | val_main=0.580780
Epoch 010 | phase=1 | train_loss=4.8842 | val_main=0.580725
Epoch 011 | phase=1 | train_loss=4.8221 | val_main=0.580772
Epoch 012 | phase=1 | train_loss=4.8437 | val_main=0.580800
Epoch 013 | phase=1 | train_loss=4.8300 | val_main=0.580828
Epoch 014 | phase=1 | train_loss=4.9623 | val_main=0.580748
Epoch 015 | phase=1 | train_loss=4.7319 | val_main=0.580731
Epoch 016 | phase=0 | train_loss=5.9581 | val_main=0.555534
Epoch 017 | phase=0 | train_loss=5.6949 | val_main=0.557962
Epoch 018 | phase=0 | train_loss=5.8127 | val_main=0.546465
Epoch 019 | phase=0 | train_loss=5.7872 | val_main=0.542669
Epoch 020 | phase=0 | train_loss=5.7358 | val_main=0.552767
Epoch 021 | phase=0 | train_loss=5.6837 | val_main=0.542813
Epoch 022 | phase=0 | train_loss=5.6503 | val_main=0.416276
Epoch 023 | phase=0 | train_loss=5.6826 | val_main=0.420905
Epoch 024 | phase=0 | train_loss=5.4319 | val_main=0.601478
Epoch 025 | phase=0 | train_loss=5.6861 | val_main=0.352899
Epoch 026 | phase=0 | train_loss=5.2834 | val_main=0.360113
Epoch 027 | phase=0 | train_loss=5.1426 | val_main=0.344435
Epoch 028 | phase=0 | train_loss=5.3386 | val_main=0.341884
Epoch 029 | phase=0 | train_loss=5.2108 | val_main=0.354185
Epoch 030 | phase=0 | train_loss=5.2402 | val_main=0.355077
Epoch 031 | phase=2 | train_loss=0.6062 | val_main=0.353629
Epoch 032 | phase=2 | train_loss=0.6129 | val_main=0.360563
Epoch 033 | phase=2 | train_loss=0.6202 | val_main=0.356180
Epoch 034 | phase=2 | train_loss=0.5954 | val_main=0.358796
Epoch 035 | phase=2 | train_loss=0.6951 | val_main=0.355777
Epoch 036 | phase=2 | train_loss=0.5897 | val_main=0.358530
Epoch 037 | phase=2 | train_loss=0.5819 | val_main=0.360066
Epoch 038 | phase=2 | train_loss=0.5915 | val_main=0.357502
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 6
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.31861463
1 day(s) RMSE : 8.52916063
1 day(s) R2 : 0.02607494
1 day(s) Pearson r : 0.37040281
1 day(s) QLIKE : 0.36833499
full horizon MAE : 2.31861463
full horizon RMSE : 8.52916063
full horizon R2 : 0.02607494
full horizon Pearson r : 0.37040281
full horizon QLIKE : 0.36833499
--- Task 2 ---
1 day(s) MAE : 0.08133260
1 day(s) RMSE : 0.10965309
1 day(s) R2 : -0.64432686
1 day(s) Pearson r : 0.41169904
1 day(s) QLIKE : 3.74021587
full horizon MAE : 0.08133260
full horizon RMSE : 0.10965309
full horizon R2 : -0.64432686
full horizon Pearson r : 0.41169904
full horizon QLIKE : 3.74021587
--- Task 3 ---
1 day(s) MAE : 0.51494433
1 day(s) RMSE : 0.53802927
1 day(s) R2 : -0.36626247
1 day(s) Pearson r : -0.13818377
1 day(s) QLIKE : 15.52352989
full horizon MAE : 0.51494433
full horizon RMSE : 0.53802927
full horizon R2 : -0.36626247
full horizon Pearson r : -0.13818377
full horizon QLIKE : 15.52352989
--- Task 4 ---
1 day(s) MAE : 1.03912987
1 day(s) RMSE : 1.27053290
1 day(s) R2 : -1.27672097
1 day(s) Pearson r : 0.13806596
1 day(s) QLIKE : 0.99488755
full horizon MAE : 1.03912987
full horizon RMSE : 1.27053290
full horizon R2 : -1.27672097
full horizon Pearson r : 0.13806596
full horizon QLIKE : 0.99488755
--- Task 5 ---
1 day(s) MAE : 0.10461350
1 day(s) RMSE : 0.11908139
1 day(s) R2 : -4.97197511
1 day(s) Pearson r : -0.24486432
1 day(s) QLIKE : 0.80372846
full horizon MAE : 0.10461350
full horizon RMSE : 0.11908139
full horizon R2 : -4.97197511
full horizon Pearson r : -0.24486432
full horizon QLIKE : 0.80372846
--- Task 6 ---
1 day(s) MAE : 1.77869615
1 day(s) RMSE : 2.26988993
1 day(s) R2 : -1.36228680
1 day(s) Pearson r : -0.49594072
1 day(s) QLIKE : 0.08016863
full horizon MAE : 1.77869615
full horizon RMSE : 2.26988993
full horizon R2 : -1.36228680
full horizon Pearson r : -0.49594072
full horizon QLIKE : 0.08016863
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/AAPL/Custom_KAN_H1.pkl
Saved y_true min=0.314451, max=112.408
Saved y_pred min=1.13205, max=7.21495
=== AAPL | H=1 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.099874372714204
Min value: -0.03434294798653995
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.133266415235102
Min value: -13.098170041087558
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.051920323212192546
Min value: -0.0338203458440787
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.9069843280619727
Min value: -6.555005878598166
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.19306601030720755
Min value: -0.03421247164703902
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.8950185841626324
Min value: -13.098170041087558
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.ops.tensor_array_ops.TensorArray'>):
<tensorflow.python.ops.tensor_array_ops.TensorArray object at 0x7174e4f640>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
File "/opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/keras/src/backend/tensorflow/rnn.py", line 418, in <genexpr>
output_ta_t = tuple( File "/opt/anaconda3/envs/NN_env/lib/python3.10/site-packages/tensorflow/python/util/tf_should_use.py", line 288, in wrapped
return _add_should_use_warning(fn(*args, **kwargs),
==================================
46/46 ━━━━━━━━━━━━━━━━━━━━ 246s 423ms/step - loss: 1.0050 - val_loss: 0.6297 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 7s 162ms/step - loss: 0.9933 - val_loss: 0.6360 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 8s 183ms/step - loss: 0.9932 - val_loss: 0.6397 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 187ms/step - loss: 0.9931 - val_loss: 0.6526 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 196ms/step - loss: 0.9929 - val_loss: 0.6482 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 184ms/step - loss: 1.0096
Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 190ms/step - loss: 0.9928 - val_loss: 0.6476 - learning_rate: 5.0000e-04
Epoch 6: early stopping
Restoring model weights from the end of the best epoch: 1.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 1
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.43932468
1 day(s) RMSE : 8.72179704
1 day(s) R2 : -0.01841527
1 day(s) Pearson r : 0.45143803
1 day(s) QLIKE : 0.53538042
full horizon MAE : 2.43932468
full horizon RMSE : 8.72179704
full horizon R2 : -0.01841527
full horizon Pearson r : 0.45143803
full horizon QLIKE : 0.53538042
--- Task 2 ---
1 day(s) MAE : 0.08153062
1 day(s) RMSE : 0.11017861
1 day(s) R2 : -0.66012580
1 day(s) Pearson r : -0.10636611
1 day(s) QLIKE : 3.72285469
full horizon MAE : 0.08153062
full horizon RMSE : 0.11017861
full horizon R2 : -0.66012580
full horizon Pearson r : -0.10636611
full horizon QLIKE : 3.72285469
--- Task 3 ---
1 day(s) MAE : 0.49865958
1 day(s) RMSE : 0.52397619
1 day(s) R2 : -0.29582228
1 day(s) Pearson r : -0.03726016
1 day(s) QLIKE : 15.47962049
full horizon MAE : 0.49865958
full horizon RMSE : 0.52397619
full horizon R2 : -0.29582228
full horizon Pearson r : -0.03726016
full horizon QLIKE : 15.47962049
--- Task 4 ---
1 day(s) MAE : 1.03795360
1 day(s) RMSE : 1.26664632
1 day(s) R2 : -1.26281321
1 day(s) Pearson r : 0.11562097
1 day(s) QLIKE : 0.99635614
full horizon MAE : 1.03795360
full horizon RMSE : 1.26664632
full horizon R2 : -1.26281321
full horizon Pearson r : 0.11562097
full horizon QLIKE : 0.99635614
--- Task 5 ---
1 day(s) MAE : 0.04714031
1 day(s) RMSE : 0.06211790
1 day(s) R2 : -0.62503863
1 day(s) Pearson r : 0.31877883
1 day(s) QLIKE : 0.11160550
full horizon MAE : 0.04714031
full horizon RMSE : 0.06211790
full horizon R2 : -0.62503863
full horizon Pearson r : 0.31877883
full horizon QLIKE : 0.11160550
--- Task 6 ---
1 day(s) MAE : 1.44468791
1 day(s) RMSE : 1.58885216
1 day(s) R2 : -0.15741739
1 day(s) Pearson r : 0.28092223
1 day(s) QLIKE : 0.03597071
full horizon MAE : 1.44468791
full horizon RMSE : 1.58885216
full horizon R2 : -0.15741739
full horizon Pearson r : 0.28092223
full horizon QLIKE : 0.03597071
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/AAPL/Custom_KAN_LSTM_H1.pkl
Saved y_true min=0.314451, max=112.408
Saved y_pred min=2.4167, max=2.41709
=== AAPL | H=5 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.099874372714204
Min value: -0.03434294798653995
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.140231129276028
Min value: -13.09819653554311
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.051920323212192546
Min value: -0.0338203458440787
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.9079382526637185
Min value: -6.554957295267729
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.19306601030720755
Min value: -0.03421247164703902
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.8969263469922275
Min value: -13.09819653554311
Epoch 001 | phase=1 | train_loss=5.0681 | val_main=0.587713
Epoch 002 | phase=1 | train_loss=4.7434 | val_main=0.587708
Epoch 003 | phase=1 | train_loss=4.9557 | val_main=0.587690
Epoch 004 | phase=1 | train_loss=4.9394 | val_main=0.587686
Epoch 005 | phase=1 | train_loss=4.7655 | val_main=0.587713
Epoch 006 | phase=1 | train_loss=4.8220 | val_main=0.587699
Epoch 007 | phase=1 | train_loss=4.9456 | val_main=0.587671
Epoch 008 | phase=1 | train_loss=4.8953 | val_main=0.587693
Epoch 009 | phase=1 | train_loss=4.9145 | val_main=0.587682
Epoch 010 | phase=1 | train_loss=4.9372 | val_main=0.587678
Epoch 011 | phase=1 | train_loss=4.8423 | val_main=0.587683
Epoch 012 | phase=1 | train_loss=4.8731 | val_main=0.587686
Epoch 013 | phase=1 | train_loss=4.8636 | val_main=0.587682
Epoch 014 | phase=1 | train_loss=4.8959 | val_main=0.587677
Epoch 015 | phase=1 | train_loss=4.7466 | val_main=0.587672
Epoch 016 | phase=0 | train_loss=5.9270 | val_main=0.577843
Epoch 017 | phase=0 | train_loss=5.7934 | val_main=0.564152
Epoch 018 | phase=0 | train_loss=5.9877 | val_main=0.560024
Epoch 019 | phase=0 | train_loss=5.8449 | val_main=0.554524
Epoch 020 | phase=0 | train_loss=5.9626 | val_main=0.556333
Epoch 021 | phase=0 | train_loss=5.8091 | val_main=0.559991
Epoch 022 | phase=0 | train_loss=5.9202 | val_main=0.559138
Epoch 023 | phase=0 | train_loss=5.9448 | val_main=0.567318
Epoch 024 | phase=0 | train_loss=5.8409 | val_main=0.561319
Epoch 025 | phase=0 | train_loss=6.1025 | val_main=0.554772
Epoch 026 | phase=0 | train_loss=5.8045 | val_main=0.540072
Epoch 027 | phase=0 | train_loss=5.6988 | val_main=0.427341
Epoch 028 | phase=0 | train_loss=5.7054 | val_main=0.379700
Epoch 029 | phase=0 | train_loss=5.4793 | val_main=0.406403
Epoch 030 | phase=0 | train_loss=5.4662 | val_main=0.359878
Epoch 031 | phase=2 | train_loss=0.6987 | val_main=0.455149
Epoch 032 | phase=2 | train_loss=0.6905 | val_main=0.402307
Epoch 033 | phase=2 | train_loss=0.6873 | val_main=0.379673
Epoch 034 | phase=2 | train_loss=0.6694 | val_main=0.386497
Epoch 035 | phase=2 | train_loss=0.6901 | val_main=0.393787
Epoch 036 | phase=2 | train_loss=0.6550 | val_main=0.389367
Epoch 037 | phase=2 | train_loss=0.6402 | val_main=0.378044
Epoch 038 | phase=2 | train_loss=0.6421 | val_main=0.380437
Epoch 039 | phase=2 | train_loss=0.6494 | val_main=0.373024
Epoch 040 | phase=2 | train_loss=0.6337 | val_main=0.385306
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 30
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.35718910
1 day(s) RMSE : 8.58201427
1 day(s) R2 : 0.01396708
1 day(s) Pearson r : 0.35270661
1 day(s) QLIKE : 0.38347309
3 day(s) MAE : 2.45247231
3 day(s) RMSE : 8.85642024
3 day(s) R2 : 0.00748488
3 day(s) Pearson r : 0.33469079
3 day(s) QLIKE : 0.41221190
5 day(s) MAE : 2.48726231
5 day(s) RMSE : 8.95131615
5 day(s) R2 : -0.00271077
5 day(s) Pearson r : 0.28444708
5 day(s) QLIKE : 0.43209580
full horizon MAE : 2.48726231
full horizon RMSE : 8.95131615
full horizon R2 : -0.00271077
full horizon Pearson r : 0.28444708
full horizon QLIKE : 0.43209580
--- Task 2 ---
1 day(s) MAE : 0.08377364
1 day(s) RMSE : 0.11465373
1 day(s) R2 : -0.79772301
1 day(s) Pearson r : 0.35229515
1 day(s) QLIKE : 3.63412772
3 day(s) MAE : 0.08425763
3 day(s) RMSE : 0.11505583
3 day(s) R2 : -0.79499929
3 day(s) Pearson r : 0.32727704
3 day(s) QLIKE : 3.64135878
5 day(s) MAE : 0.08483972
5 day(s) RMSE : 0.11564764
5 day(s) R2 : -0.80022688
5 day(s) Pearson r : 0.32006032
5 day(s) QLIKE : 3.64643012
full horizon MAE : 0.08483972
full horizon RMSE : 0.11564764
full horizon R2 : -0.80022688
full horizon Pearson r : 0.32006032
full horizon QLIKE : 3.64643012
--- Task 3 ---
1 day(s) MAE : 0.50863885
1 day(s) RMSE : 0.53213426
1 day(s) R2 : -0.33648711
1 day(s) Pearson r : -0.07152109
1 day(s) QLIKE : 15.49084688
3 day(s) MAE : 0.50688446
3 day(s) RMSE : 0.53118217
3 day(s) R2 : -0.33646181
3 day(s) Pearson r : -0.06080745
3 day(s) QLIKE : 15.48750370
5 day(s) MAE : 0.50485207
5 day(s) RMSE : 0.52965804
5 day(s) R2 : -0.33325075
5 day(s) Pearson r : -0.04799882
5 day(s) QLIKE : 15.48456858
full horizon MAE : 0.50485207
full horizon RMSE : 0.52965804
full horizon R2 : -0.33325075
full horizon Pearson r : -0.04799882
full horizon QLIKE : 15.48456858
--- Task 4 ---
1 day(s) MAE : 1.03968581
1 day(s) RMSE : 1.27088613
1 day(s) R2 : -1.27798706
1 day(s) Pearson r : -0.07987644
1 day(s) QLIKE : 0.99644317
3 day(s) MAE : 1.04113320
3 day(s) RMSE : 1.27136733
3 day(s) R2 : -1.28873740
3 day(s) Pearson r : -0.13006035
3 day(s) QLIKE : 0.98935306
5 day(s) MAE : 1.04243624
5 day(s) RMSE : 1.27168341
5 day(s) R2 : -1.29889327
5 day(s) Pearson r : -0.11772813
5 day(s) QLIKE : 0.98209272
full horizon MAE : 1.04243624
full horizon RMSE : 1.27168341
full horizon R2 : -1.29889327
full horizon Pearson r : -0.11772813
full horizon QLIKE : 0.98209272
--- Task 5 ---
1 day(s) MAE : 0.04882928
1 day(s) RMSE : 0.06607644
1 day(s) R2 : -0.83875318
1 day(s) Pearson r : -0.33530271
1 day(s) QLIKE : 0.26964598
3 day(s) MAE : 0.04970202
3 day(s) RMSE : 0.06708103
3 day(s) R2 : -0.86007125
3 day(s) Pearson r : -0.33462253
3 day(s) QLIKE : 0.27946477
5 day(s) MAE : 0.04990478
5 day(s) RMSE : 0.06761364
5 day(s) R2 : -0.85308747
5 day(s) Pearson r : -0.33641994
5 day(s) QLIKE : 0.27976206
full horizon MAE : 0.04990478
full horizon RMSE : 0.06761364
full horizon R2 : -0.85308747
full horizon Pearson r : -0.33641994
full horizon QLIKE : 0.27976206
--- Task 6 ---
1 day(s) MAE : 1.60954344
1 day(s) RMSE : 2.03313770
1 day(s) R2 : -0.89520683
1 day(s) Pearson r : -0.48810658
1 day(s) QLIKE : 0.06504519
3 day(s) MAE : 1.60392788
3 day(s) RMSE : 2.01860167
3 day(s) R2 : -0.86044913
3 day(s) Pearson r : -0.48654089
3 day(s) QLIKE : 0.06441922
5 day(s) MAE : 1.60759207
5 day(s) RMSE : 2.02175228
5 day(s) R2 : -0.85664750
5 day(s) Pearson r : -0.48681333
5 day(s) QLIKE : 0.06476662
full horizon MAE : 1.60759207
full horizon RMSE : 2.02175228
full horizon R2 : -0.85664750
full horizon Pearson r : -0.48681333
full horizon QLIKE : 0.06476662
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/AAPL/Custom_KAN_H5.pkl
Saved y_true min=0.314451, max=112.408
Saved y_pred min=1.16991, max=6.75209
=== AAPL | H=5 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.099874372714204
Min value: -0.03434294798653995
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.140231129276028
Min value: -13.09819653554311
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.051920323212192546
Min value: -0.0338203458440787
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.9079382526637185
Min value: -6.554957295267729
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.19306601030720755
Min value: -0.03421247164703902
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.8969263469922275
Min value: -13.09819653554311
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 139s 428ms/step - loss: 0.8677 - val_loss: 0.7315 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 10s 213ms/step - loss: 0.6348 - val_loss: 1.0547 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 240ms/step - loss: 0.5550 - val_loss: 1.0444 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 241ms/step - loss: 0.5191 - val_loss: 1.0782 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 243ms/step - loss: 0.5003 - val_loss: 1.0121 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 238ms/step - loss: 0.4637
Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 246ms/step - loss: 0.4904 - val_loss: 0.9914 - learning_rate: 5.0000e-04
Epoch 6: early stopping
Restoring model weights from the end of the best epoch: 1.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 5
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.49347025
1 day(s) RMSE : 8.69984223
1 day(s) R2 : -0.01329455
1 day(s) Pearson r : 0.44610270
1 day(s) QLIKE : 0.53532791
3 day(s) MAE : 2.67081843
3 day(s) RMSE : 8.93140018
3 day(s) R2 : -0.00939185
3 day(s) Pearson r : 0.00759024
3 day(s) QLIKE : 0.55392763
5 day(s) MAE : 2.73369511
5 day(s) RMSE : 8.97461124
5 day(s) R2 : -0.00793652
5 day(s) Pearson r : 0.00651749
5 day(s) QLIKE : 0.55788240
full horizon MAE : 2.73369511
full horizon RMSE : 8.97461124
full horizon R2 : -0.00793652
full horizon Pearson r : 0.00651749
full horizon QLIKE : 0.55788240
--- Task 2 ---
1 day(s) MAE : 0.08382950
1 day(s) RMSE : 0.11500732
1 day(s) R2 : -0.80882856
1 day(s) Pearson r : -0.29905991
1 day(s) QLIKE : 3.72320856
3 day(s) MAE : 0.08777396
3 day(s) RMSE : 0.12071934
3 day(s) R2 : -0.97606291
3 day(s) Pearson r : -0.00658043
3 day(s) QLIKE : 4.87053765
5 day(s) MAE : 0.08993878
5 day(s) RMSE : 0.12337385
5 day(s) R2 : -1.04880161
5 day(s) Pearson r : -0.00928899
5 day(s) QLIKE : 7.08719452
full horizon MAE : 0.08993878
full horizon RMSE : 0.12337385
full horizon R2 : -1.04880161
full horizon Pearson r : -0.00928899
full horizon QLIKE : 7.08719452
--- Task 3 ---
1 day(s) MAE : 0.49542020
1 day(s) RMSE : 0.51788626
1 day(s) R2 : -0.26587586
1 day(s) Pearson r : 0.01053806
1 day(s) QLIKE : 15.47961952
3 day(s) MAE : 0.48754887
3 day(s) RMSE : 0.50511198
3 day(s) R2 : -0.20849514
3 day(s) Pearson r : 0.00152261
3 day(s) QLIKE : 15.47977937
5 day(s) MAE : 0.47867428
5 day(s) RMSE : 0.49219322
5 day(s) R2 : -0.15130913
5 day(s) Pearson r : 0.00253218
5 day(s) QLIKE : 15.48147450
full horizon MAE : 0.47867428
full horizon RMSE : 0.49219322
full horizon R2 : -0.15130913
full horizon Pearson r : 0.00253218
full horizon QLIKE : 15.48147450
--- Task 4 ---
1 day(s) MAE : 1.03494832
1 day(s) RMSE : 1.25937489
1 day(s) R2 : -1.23690755
1 day(s) Pearson r : 0.12137409
1 day(s) QLIKE : 0.99632345
3 day(s) MAE : 1.03354099
3 day(s) RMSE : 1.25333381
3 day(s) R2 : -1.22426938
3 day(s) Pearson r : 0.00396884
3 day(s) QLIKE : 0.98967993
5 day(s) MAE : 1.03151866
5 day(s) RMSE : 1.24645823
5 day(s) R2 : -1.20859588
5 day(s) Pearson r : 0.00731001
5 day(s) QLIKE : 0.98386279
full horizon MAE : 1.03151866
full horizon RMSE : 1.24645823
full horizon R2 : -1.20859588
full horizon Pearson r : 0.00731001
full horizon QLIKE : 0.98386279
--- Task 5 ---
1 day(s) MAE : 0.06571096
1 day(s) RMSE : 0.08005515
1 day(s) R2 : -1.69903686
1 day(s) Pearson r : -0.26101939
1 day(s) QLIKE : 0.11163239
3 day(s) MAE : 0.05077131
3 day(s) RMSE : 0.06558723
3 day(s) R2 : -0.77815127
3 day(s) Pearson r : 0.00636304
3 day(s) QLIKE : 0.17253106
5 day(s) MAE : 0.05333587
5 day(s) RMSE : 0.06646957
5 day(s) R2 : -0.79090735
5 day(s) Pearson r : 0.01093631
5 day(s) QLIKE : 0.22711865
full horizon MAE : 0.05333587
full horizon RMSE : 0.06646957
full horizon R2 : -0.79090735
full horizon Pearson r : 0.01093631
full horizon QLIKE : 0.22711865
--- Task 6 ---
1 day(s) MAE : 1.54517484
1 day(s) RMSE : 1.69235306
1 day(s) R2 : -0.31312167
1 day(s) Pearson r : 0.19128402
1 day(s) QLIKE : 0.03596833
3 day(s) MAE : 2.80135990
3 day(s) RMSE : 3.36577693
3 day(s) R2 : -4.17234163
3 day(s) Pearson r : -0.00535594
3 day(s) QLIKE : 0.05917191
5 day(s) MAE : 6.08816989
5 day(s) RMSE : 7.56399271
5 day(s) R2 : -24.98817477
5 day(s) Pearson r : -0.00884888
5 day(s) QLIKE : 0.12094343
full horizon MAE : 6.08816989
full horizon RMSE : 7.56399271
full horizon R2 : -24.98817477
full horizon Pearson r : -0.00884888
full horizon QLIKE : 0.12094343
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/AAPL/Custom_KAN_LSTM_H5.pkl
Saved y_true min=0.314451, max=112.408
Saved y_pred min=2.59225, max=3.08685
=== AAPL | H=10 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.099874372714204
Min value: -0.03434294798653995
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.149858145997081
Min value: -13.0982259671673
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.051920323212192546
Min value: -0.0338203458440787
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.909556794039411
Min value: -6.554914737131346
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.19306601030720755
Min value: -0.03421247164703902
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.8998157347365185
Min value: -13.0982259671673
Epoch 001 | phase=1 | train_loss=4.9976 | val_main=0.591480
Epoch 002 | phase=1 | train_loss=4.7587 | val_main=0.591577
Epoch 003 | phase=1 | train_loss=4.9780 | val_main=0.591626
Epoch 004 | phase=1 | train_loss=4.9096 | val_main=0.591647
Epoch 005 | phase=1 | train_loss=4.7706 | val_main=0.591579
Epoch 006 | phase=1 | train_loss=4.8389 | val_main=0.591620
Epoch 007 | phase=1 | train_loss=4.9549 | val_main=0.591703
Epoch 008 | phase=1 | train_loss=4.9579 | val_main=0.591605
Epoch 009 | phase=1 | train_loss=4.8700 | val_main=0.591637
Epoch 010 | phase=1 | train_loss=4.9039 | val_main=0.591662
Epoch 011 | phase=1 | train_loss=4.8354 | val_main=0.591630
Epoch 012 | phase=1 | train_loss=4.8875 | val_main=0.591619
Epoch 013 | phase=1 | train_loss=4.8638 | val_main=0.591618
Epoch 014 | phase=1 | train_loss=4.8710 | val_main=0.591653
Epoch 015 | phase=1 | train_loss=4.7845 | val_main=0.591678
Epoch 016 | phase=0 | train_loss=5.9830 | val_main=0.587268
Epoch 017 | phase=0 | train_loss=5.7894 | val_main=0.573910
Epoch 018 | phase=0 | train_loss=6.0005 | val_main=0.569595
Epoch 019 | phase=0 | train_loss=5.8008 | val_main=0.566631
Epoch 020 | phase=0 | train_loss=5.8804 | val_main=0.562590
Epoch 021 | phase=0 | train_loss=5.7765 | val_main=0.565525
Epoch 022 | phase=0 | train_loss=5.8871 | val_main=0.569739
Epoch 023 | phase=0 | train_loss=5.9654 | val_main=0.572219
Epoch 024 | phase=0 | train_loss=5.8246 | val_main=0.566105
Epoch 025 | phase=0 | train_loss=6.0971 | val_main=0.554751
Epoch 026 | phase=0 | train_loss=5.7697 | val_main=0.474335
Epoch 027 | phase=0 | train_loss=5.5786 | val_main=0.435445
Epoch 028 | phase=0 | train_loss=5.7261 | val_main=0.488873
Epoch 029 | phase=0 | train_loss=5.4016 | val_main=0.386680
Epoch 030 | phase=0 | train_loss=5.4291 | val_main=0.377969
Epoch 031 | phase=2 | train_loss=0.7440 | val_main=0.466510
Epoch 032 | phase=2 | train_loss=0.7338 | val_main=0.387323
Epoch 033 | phase=2 | train_loss=0.7269 | val_main=0.414941
Epoch 034 | phase=2 | train_loss=0.7204 | val_main=0.400250
Epoch 035 | phase=2 | train_loss=0.7203 | val_main=0.413027
Epoch 036 | phase=2 | train_loss=0.6948 | val_main=0.408802
Epoch 037 | phase=2 | train_loss=0.6910 | val_main=0.402995
Epoch 038 | phase=2 | train_loss=0.6966 | val_main=0.428999
Epoch 039 | phase=2 | train_loss=0.6971 | val_main=0.395387
Epoch 040 | phase=2 | train_loss=0.6861 | val_main=0.419196
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 60
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.40900681
1 day(s) RMSE : 8.63152780
1 day(s) R2 : 0.00255651
1 day(s) Pearson r : 0.32745879
1 day(s) QLIKE : 0.38314056
3 day(s) MAE : 2.49867375
3 day(s) RMSE : 8.90470268
3 day(s) R2 : -0.00336638
3 day(s) Pearson r : 0.31269487
3 day(s) QLIKE : 0.41044636
5 day(s) MAE : 2.52819879
5 day(s) RMSE : 8.98871572
5 day(s) R2 : -0.01110715
5 day(s) Pearson r : 0.27386522
5 day(s) QLIKE : 0.42945206
10 day(s) MAE : 2.55504159
10 day(s) RMSE : 9.07959438
10 day(s) R2 : -0.02316958
10 day(s) Pearson r : 0.20831028
10 day(s) QLIKE : 0.45959905
full horizon MAE : 2.55504159
full horizon RMSE : 9.07959438
full horizon R2 : -0.02316958
full horizon Pearson r : 0.20831028
full horizon QLIKE : 0.45959905
--- Task 2 ---
1 day(s) MAE : 0.08521499
1 day(s) RMSE : 0.11652767
1 day(s) R2 : -0.85696862
1 day(s) Pearson r : 0.30556452
1 day(s) QLIKE : 3.67605449
3 day(s) MAE : 0.08583480
3 day(s) RMSE : 0.11706553
3 day(s) R2 : -0.85825421
3 day(s) Pearson r : 0.31768770
3 day(s) QLIKE : 3.66534425
5 day(s) MAE : 0.08629705
5 day(s) RMSE : 0.11759035
5 day(s) R2 : -0.86121703
5 day(s) Pearson r : 0.29577166
5 day(s) QLIKE : 3.68036688
10 day(s) MAE : 0.08782582
10 day(s) RMSE : 0.11915583
10 day(s) R2 : -0.88511290
10 day(s) Pearson r : 0.25950830
10 day(s) QLIKE : 3.68846604
full horizon MAE : 0.08782582
full horizon RMSE : 0.11915583
full horizon R2 : -0.88511290
full horizon Pearson r : 0.25950830
full horizon QLIKE : 3.68846604
--- Task 3 ---
1 day(s) MAE : 0.50374023
1 day(s) RMSE : 0.52150717
1 day(s) R2 : -0.28363901
1 day(s) Pearson r : -0.09998450
1 day(s) QLIKE : 15.50696589
3 day(s) MAE : 0.50102731
3 day(s) RMSE : 0.51913352
3 day(s) R2 : -0.27652026
3 day(s) Pearson r : -0.08777129
3 day(s) QLIKE : 15.50341190
5 day(s) MAE : 0.49863147
5 day(s) RMSE : 0.51725957
5 day(s) R2 : -0.27156264
5 day(s) Pearson r : -0.07428393
5 day(s) QLIKE : 15.49911166
10 day(s) MAE : 0.49353786
10 day(s) RMSE : 0.51380140
10 day(s) R2 : -0.26527584
10 day(s) Pearson r : -0.03121923
10 day(s) QLIKE : 15.48973787
full horizon MAE : 0.49353786
full horizon RMSE : 0.51380140
full horizon R2 : -0.26527584
full horizon Pearson r : -0.03121923
full horizon QLIKE : 15.48973787
--- Task 4 ---
1 day(s) MAE : 1.03995553
1 day(s) RMSE : 1.27213193
1 day(s) R2 : -1.28245531
1 day(s) Pearson r : 0.11294152
1 day(s) QLIKE : 0.99555048
3 day(s) MAE : 1.04131688
3 day(s) RMSE : 1.27237728
3 day(s) R2 : -1.29237514
3 day(s) Pearson r : 0.09532335
3 day(s) QLIKE : 0.98852189
5 day(s) MAE : 1.04269513
5 day(s) RMSE : 1.27285107
5 day(s) R2 : -1.30311688
5 day(s) Pearson r : 0.08503811
5 day(s) QLIKE : 0.98132393
10 day(s) MAE : 1.04635897
10 day(s) RMSE : 1.27408447
10 day(s) R2 : -1.33108317
10 day(s) Pearson r : 0.03997526
10 day(s) QLIKE : 0.96329488
full horizon MAE : 1.04635897
full horizon RMSE : 1.27408447
full horizon R2 : -1.33108317
full horizon Pearson r : 0.03997526
full horizon QLIKE : 0.96329488
--- Task 5 ---
1 day(s) MAE : 0.16272801
1 day(s) RMSE : 0.18271942
1 day(s) R2 : -13.06046345
1 day(s) Pearson r : -0.32473915
1 day(s) QLIKE : 0.74381645
3 day(s) MAE : 0.17412920
3 day(s) RMSE : 0.19670403
3 day(s) R2 : -14.99398438
3 day(s) Pearson r : -0.32506647
3 day(s) QLIKE : 0.78296344
5 day(s) MAE : 0.17100334
5 day(s) RMSE : 0.19316717
5 day(s) R2 : -14.12495101
5 day(s) Pearson r : -0.32854207
5 day(s) QLIKE : 0.76533237
10 day(s) MAE : 0.16499043
10 day(s) RMSE : 0.18668914
10 day(s) R2 : -12.63357832
10 day(s) Pearson r : -0.33241370
10 day(s) QLIKE : 0.75333162
full horizon MAE : 0.16499043
full horizon RMSE : 0.18668914
full horizon R2 : -12.63357832
full horizon Pearson r : -0.33241370
full horizon QLIKE : 0.75333162
--- Task 6 ---
1 day(s) MAE : 1.78076867
1 day(s) RMSE : 2.30784480
1 day(s) R2 : -1.44194696
1 day(s) Pearson r : -0.47615094
1 day(s) QLIKE : 0.08039461
3 day(s) MAE : 1.74572315
3 day(s) RMSE : 2.26754810
3 day(s) R2 : -1.34762952
3 day(s) Pearson r : -0.47408607
3 day(s) QLIKE : 0.07833172
5 day(s) MAE : 1.73604630
5 day(s) RMSE : 2.25666366
5 day(s) R2 : -1.31316838
5 day(s) Pearson r : -0.47385418
5 day(s) QLIKE : 0.07804482
10 day(s) MAE : 1.72141617
10 day(s) RMSE : 2.23843532
10 day(s) R2 : -1.24645367
10 day(s) Pearson r : -0.47446097
10 day(s) QLIKE : 0.07799410
full horizon MAE : 1.72141617
full horizon RMSE : 2.23843532
full horizon R2 : -1.24645367
full horizon Pearson r : -0.47446097
full horizon QLIKE : 0.07799410
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/AAPL/Custom_KAN_H10.pkl
Saved y_true min=0.314451, max=112.408
Saved y_pred min=1.02408, max=5.72146
=== AAPL | H=10 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.099874372714204
Min value: -0.03434294798653995
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.149858145997081
Min value: -13.0982259671673
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.051920323212192546
Min value: -0.0338203458440787
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.909556794039411
Min value: -6.554914737131346
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.19306601030720755
Min value: -0.03421247164703902
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.8998157347365185
Min value: -13.0982259671673
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 114s 509ms/step - loss: 0.8379 - val_loss: 0.6560 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 297ms/step - loss: 0.5647 - val_loss: 0.9022 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 322ms/step - loss: 0.4796 - val_loss: 1.0637 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 337ms/step - loss: 0.4385 - val_loss: 0.7686 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 309ms/step - loss: 0.4208 - val_loss: 0.7959 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 308ms/step - loss: 0.4102 - val_loss: 0.5208 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 324ms/step - loss: 0.4053 - val_loss: 0.6875 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 311ms/step - loss: 0.3939 - val_loss: 0.5701 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 308ms/step - loss: 0.3847 - val_loss: 0.6253 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 310ms/step - loss: 0.3815 - val_loss: 0.6930 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 300ms/step - loss: 0.3510
Epoch 11: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 310ms/step - loss: 0.3742 - val_loss: 0.5463 - learning_rate: 5.0000e-04
Epoch 11: early stopping
Restoring model weights from the end of the best epoch: 6.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 10
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.46397343
1 day(s) RMSE : 8.71101124
1 day(s) R2 : -0.01589799
1 day(s) Pearson r : 0.29350679
1 day(s) QLIKE : 0.53538775
3 day(s) MAE : 2.61535391
3 day(s) RMSE : 8.96426726
3 day(s) R2 : -0.01683454
3 day(s) Pearson r : -0.00028160
3 day(s) QLIKE : 0.56559023
5 day(s) MAE : 2.58468178
5 day(s) RMSE : 9.04217206
5 day(s) R2 : -0.02316912
5 day(s) Pearson r : -0.00196901
5 day(s) QLIKE : 0.57105434
10 day(s) MAE : 2.57626262
10 day(s) RMSE : 9.15704222
10 day(s) R2 : -0.04069906
10 day(s) Pearson r : -0.00350184
10 day(s) QLIKE : 0.58748597
full horizon MAE : 2.57626262
full horizon RMSE : 9.15704222
full horizon R2 : -0.04069906
full horizon Pearson r : -0.00350184
full horizon QLIKE : 0.58748597
--- Task 2 ---
1 day(s) MAE : 0.08223410
1 day(s) RMSE : 0.11168223
1 day(s) R2 : -0.70574682
1 day(s) Pearson r : -0.27185307
1 day(s) QLIKE : 3.72287583
3 day(s) MAE : 0.08727535
3 day(s) RMSE : 0.11969820
3 day(s) R2 : -0.94277399
3 day(s) Pearson r : -0.00584482
3 day(s) QLIKE : 5.63875843
5 day(s) MAE : 0.08964029
5 day(s) RMSE : 0.12277614
5 day(s) R2 : -1.02899785
5 day(s) Pearson r : -0.00858837
5 day(s) QLIKE : 12.77932080
10 day(s) MAE : 0.09237686
10 day(s) RMSE : 0.12599738
10 day(s) R2 : -1.10780207
10 day(s) Pearson r : -0.01263680
10 day(s) QLIKE : 9.86217665
full horizon MAE : 0.09237686
full horizon RMSE : 0.12599738
full horizon R2 : -1.10780207
full horizon Pearson r : -0.01263680
full horizon QLIKE : 9.86217665
--- Task 3 ---
1 day(s) MAE : 0.50411234
1 day(s) RMSE : 0.53397198
1 day(s) R2 : -0.34573414
1 day(s) Pearson r : -0.04932665
1 day(s) QLIKE : 15.47962049
3 day(s) MAE : 0.49878045
3 day(s) RMSE : 0.52543630
3 day(s) R2 : -0.30770478
3 day(s) Pearson r : 0.00147403
3 day(s) QLIKE : 15.47887560
5 day(s) MAE : 0.47256735
5 day(s) RMSE : 0.50549898
5 day(s) R2 : -0.21439861
5 day(s) Pearson r : 0.00226544
5 day(s) QLIKE : 15.57739185
10 day(s) MAE : 0.41810996
10 day(s) RMSE : 0.54148835
10 day(s) R2 : -0.40531244
10 day(s) Pearson r : 0.00470844
10 day(s) QLIKE : 18.44718140
full horizon MAE : 0.41810996
full horizon RMSE : 0.54148835
full horizon R2 : -0.40531244
full horizon Pearson r : 0.00470844
full horizon QLIKE : 18.44718140
--- Task 4 ---
1 day(s) MAE : 1.03791365
1 day(s) RMSE : 1.26655455
1 day(s) R2 : -1.26248532
1 day(s) Pearson r : 0.13472507
1 day(s) QLIKE : 0.99634811
3 day(s) MAE : 1.02461998
3 day(s) RMSE : 1.23299913
3 day(s) R2 : -1.15267971
3 day(s) Pearson r : 0.00204501
3 day(s) QLIKE : 1.00477906
5 day(s) MAE : 0.99911457
5 day(s) RMSE : 1.17838795
5 day(s) R2 : -0.97395560
5 day(s) Pearson r : 0.00330661
5 day(s) QLIKE : 1.04700206
10 day(s) MAE : 0.97355989
10 day(s) RMSE : 1.12419399
10 day(s) R2 : -0.81486300
10 day(s) Pearson r : 0.00679542
10 day(s) QLIKE : 1.03573813
full horizon MAE : 0.97355989
full horizon RMSE : 1.12419399
full horizon R2 : -0.81486300
full horizon Pearson r : 0.00679542
full horizon QLIKE : 1.03573813
--- Task 5 ---
1 day(s) MAE : 0.05919999
1 day(s) RMSE : 0.07398360
1 day(s) R2 : -1.30516046
1 day(s) Pearson r : 0.29619951
1 day(s) QLIKE : 0.11160756
3 day(s) MAE : 0.06395891
3 day(s) RMSE : 0.07878769
3 day(s) R2 : -1.56594265
3 day(s) Pearson r : -0.00608440
3 day(s) QLIKE : 0.11997811
5 day(s) MAE : 0.07028057
5 day(s) RMSE : 0.08521099
5 day(s) R2 : -1.94319210
5 day(s) Pearson r : -0.01064519
5 day(s) QLIKE : 0.15374097
10 day(s) MAE : 0.07997829
10 day(s) RMSE : 0.09472834
10 day(s) R2 : -2.51019797
10 day(s) Pearson r : -0.01753278
10 day(s) QLIKE : 0.19053128
full horizon MAE : 0.07997829
full horizon RMSE : 0.09472834
full horizon R2 : -2.51019797
full horizon Pearson r : -0.01753278
full horizon QLIKE : 0.19053128
--- Task 6 ---
1 day(s) MAE : 1.42452141
1 day(s) RMSE : 1.57135496
1 day(s) R2 : -0.13206568
1 day(s) Pearson r : 0.29007043
1 day(s) QLIKE : 0.03596429
3 day(s) MAE : 1.27956033
3 day(s) RMSE : 1.51695858
3 day(s) R2 : -0.05066433
3 day(s) Pearson r : 0.00581808
3 day(s) QLIKE : 0.03764986
5 day(s) MAE : 1.22242794
5 day(s) RMSE : 1.56132889
5 day(s) R2 : -0.10729253
5 day(s) Pearson r : 0.00904879
5 day(s) QLIKE : 0.04048249
10 day(s) MAE : 1.28327009
10 day(s) RMSE : 1.73329532
10 day(s) R2 : -0.34695627
10 day(s) Pearson r : 0.01687602
10 day(s) QLIKE : 0.04496652
full horizon MAE : 1.28327009
full horizon RMSE : 1.73329532
full horizon R2 : -0.34695627
full horizon Pearson r : 0.01687602
full horizon QLIKE : 0.04496652
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/AAPL/Custom_KAN_LSTM_H10.pkl
Saved y_true min=0.314451, max=112.408
Saved y_pred min=1.5093, max=3.16441
=== AAPL | H=20 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.099874372714204
Min value: -0.03434294798653995
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.174045403839706
Min value: -13.098372439983104
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.051920323212192546
Min value: -0.0338203458440787
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.913020640489958
Min value: -6.554949262074746
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.19306601030720755
Min value: -0.03421247164703902
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.906568131251448
Min value: -13.098372439983104
Epoch 001 | phase=1 | train_loss=4.9979 | val_main=0.591742
Epoch 002 | phase=1 | train_loss=4.7912 | val_main=0.591874
Epoch 003 | phase=1 | train_loss=4.9514 | val_main=0.591954
Epoch 004 | phase=1 | train_loss=4.9156 | val_main=0.591972
Epoch 005 | phase=1 | train_loss=4.7861 | val_main=0.591836
Epoch 006 | phase=1 | train_loss=4.8890 | val_main=0.591915
Epoch 007 | phase=1 | train_loss=4.8696 | val_main=0.591993
Epoch 008 | phase=1 | train_loss=4.9981 | val_main=0.591872
Epoch 009 | phase=1 | train_loss=4.8941 | val_main=0.591918
Epoch 010 | phase=1 | train_loss=4.8847 | val_main=0.591972
Epoch 011 | phase=1 | train_loss=4.8457 | val_main=0.591894
Epoch 012 | phase=1 | train_loss=4.8882 | val_main=0.591876
Epoch 013 | phase=1 | train_loss=4.8547 | val_main=0.591914
Epoch 014 | phase=1 | train_loss=4.8544 | val_main=0.591955
Epoch 015 | phase=1 | train_loss=4.7870 | val_main=0.591989
Epoch 016 | phase=0 | train_loss=6.0241 | val_main=0.589061
Epoch 017 | phase=0 | train_loss=5.8466 | val_main=0.577694
Epoch 018 | phase=0 | train_loss=5.9788 | val_main=0.575886
Epoch 019 | phase=0 | train_loss=5.8163 | val_main=0.573202
Epoch 020 | phase=0 | train_loss=5.8210 | val_main=0.566247
Epoch 021 | phase=0 | train_loss=5.8500 | val_main=0.569827
Epoch 022 | phase=0 | train_loss=5.9337 | val_main=0.575103
Epoch 023 | phase=0 | train_loss=5.9664 | val_main=0.574382
Epoch 024 | phase=0 | train_loss=5.8163 | val_main=0.572494
Epoch 025 | phase=0 | train_loss=6.0288 | val_main=0.569530
Epoch 026 | phase=0 | train_loss=5.8468 | val_main=0.552994
Epoch 027 | phase=0 | train_loss=5.7339 | val_main=0.450604
Epoch 028 | phase=0 | train_loss=5.8713 | val_main=0.469513
Epoch 029 | phase=0 | train_loss=5.5493 | val_main=0.491613
Epoch 030 | phase=0 | train_loss=5.5614 | val_main=0.383899
Epoch 031 | phase=2 | train_loss=0.7786 | val_main=0.449028
Epoch 032 | phase=2 | train_loss=0.7797 | val_main=0.463928
Epoch 033 | phase=2 | train_loss=0.7785 | val_main=0.427523
Epoch 034 | phase=2 | train_loss=0.7704 | val_main=0.404972
Epoch 035 | phase=2 | train_loss=0.7650 | val_main=0.434853
Epoch 036 | phase=2 | train_loss=0.7481 | val_main=0.450797
Epoch 037 | phase=2 | train_loss=0.7463 | val_main=0.415057
Epoch 038 | phase=2 | train_loss=0.7459 | val_main=0.438108
Epoch 039 | phase=2 | train_loss=0.7503 | val_main=0.435642
Epoch 040 | phase=2 | train_loss=0.7380 | val_main=0.432412
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 120
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.34890330
1 day(s) RMSE : 8.54669265
1 day(s) R2 : 0.02206695
1 day(s) Pearson r : 0.33516735
1 day(s) QLIKE : 0.38060120
3 day(s) MAE : 2.45140157
3 day(s) RMSE : 8.82670105
3 day(s) R2 : 0.01413481
3 day(s) Pearson r : 0.31800991
3 day(s) QLIKE : 0.40802753
5 day(s) MAE : 2.48510319
5 day(s) RMSE : 8.91597622
5 day(s) R2 : 0.00519103
5 day(s) Pearson r : 0.27990843
5 day(s) QLIKE : 0.42716715
10 day(s) MAE : 2.51665646
10 day(s) RMSE : 9.01025918
10 day(s) R2 : -0.00760263
10 day(s) Pearson r : 0.22270058
10 day(s) QLIKE : 0.45354420
20 day(s) MAE : 2.54592700
20 day(s) RMSE : 9.06581028
20 day(s) R2 : -0.01572627
20 day(s) Pearson r : 0.18654300
20 day(s) QLIKE : 0.47167433
full horizon MAE : 2.54592700
full horizon RMSE : 9.06581028
full horizon R2 : -0.01572627
full horizon Pearson r : 0.18654300
full horizon QLIKE : 0.47167433
--- Task 2 ---
1 day(s) MAE : 0.08309117
1 day(s) RMSE : 0.11358503
1 day(s) R2 : -0.76436573
1 day(s) Pearson r : 0.33496882
1 day(s) QLIKE : 3.63579061
3 day(s) MAE : 0.08372854
3 day(s) RMSE : 0.11419420
3 day(s) R2 : -0.76821527
3 day(s) Pearson r : 0.33494978
3 day(s) QLIKE : 3.63352689
5 day(s) MAE : 0.08439980
5 day(s) RMSE : 0.11487603
5 day(s) R2 : -0.77628429
5 day(s) Pearson r : 0.33357040
5 day(s) QLIKE : 3.63291191
10 day(s) MAE : 0.08601066
10 day(s) RMSE : 0.11652892
10 day(s) R2 : -0.80291080
10 day(s) Pearson r : 0.31236896
10 day(s) QLIKE : 3.63771561
20 day(s) MAE : 0.08909036
20 day(s) RMSE : 0.11939357
20 day(s) R2 : -0.85660469
20 day(s) Pearson r : 0.27040859
20 day(s) QLIKE : 3.61036604
full horizon MAE : 0.08909036
full horizon RMSE : 0.11939357
full horizon R2 : -0.85660469
full horizon Pearson r : 0.27040859
full horizon QLIKE : 3.61036604
--- Task 3 ---
1 day(s) MAE : 0.51082842
1 day(s) RMSE : 0.53352974
1 day(s) R2 : -0.34350599
1 day(s) Pearson r : -0.09068248
1 day(s) QLIKE : 15.49655782
3 day(s) MAE : 0.50816043
3 day(s) RMSE : 0.53107573
3 day(s) R2 : -0.33592624
3 day(s) Pearson r : -0.08008046
3 day(s) QLIKE : 15.49329782
5 day(s) MAE : 0.50578417
5 day(s) RMSE : 0.52909023
5 day(s) R2 : -0.33039371
5 day(s) Pearson r : -0.06559234
5 day(s) QLIKE : 15.48993906
10 day(s) MAE : 0.50167971
10 day(s) RMSE : 0.52649312
10 day(s) R2 : -0.32855657
10 day(s) Pearson r : -0.02469918
10 day(s) QLIKE : 15.48248281
20 day(s) MAE : 0.49409997
20 day(s) RMSE : 0.52204398
20 day(s) R2 : -0.32847608
20 day(s) Pearson r : 0.04618538
20 day(s) QLIKE : 15.46933326
full horizon MAE : 0.49409997
full horizon RMSE : 0.52204398
full horizon R2 : -0.32847608
full horizon Pearson r : 0.04618538
full horizon QLIKE : 15.46933326
--- Task 4 ---
1 day(s) MAE : 1.03963986
1 day(s) RMSE : 1.27125416
1 day(s) R2 : -1.27930660
1 day(s) Pearson r : 0.10507892
1 day(s) QLIKE : 0.99573043
3 day(s) MAE : 1.04100208
3 day(s) RMSE : 1.27152411
3 day(s) R2 : -1.28930192
3 day(s) Pearson r : 0.09040070
3 day(s) QLIKE : 0.98866650
5 day(s) MAE : 1.04234142
5 day(s) RMSE : 1.27194612
5 day(s) R2 : -1.29984320
5 day(s) Pearson r : 0.08229582
5 day(s) QLIKE : 0.98141668
10 day(s) MAE : 1.04598383
10 day(s) RMSE : 1.27316285
10 day(s) R2 : -1.32771197
10 day(s) Pearson r : 0.04029546
10 day(s) QLIKE : 0.96333336
20 day(s) MAE : 1.05365794
20 day(s) RMSE : 1.27623325
20 day(s) R2 : -1.38804969
20 day(s) Pearson r : -0.01364550
20 day(s) QLIKE : 0.92844019
full horizon MAE : 1.05365794
full horizon RMSE : 1.27623325
full horizon R2 : -1.38804969
full horizon Pearson r : -0.01364550
full horizon QLIKE : 0.92844019
--- Task 5 ---
1 day(s) MAE : 0.04980700
1 day(s) RMSE : 0.06672287
1 day(s) R2 : -0.87490658
1 day(s) Pearson r : -0.33058753
1 day(s) QLIKE : 0.27001455
3 day(s) MAE : 0.04998157
3 day(s) RMSE : 0.06717294
3 day(s) R2 : -0.86517205
3 day(s) Pearson r : -0.33365717
3 day(s) QLIKE : 0.27319780
5 day(s) MAE : 0.04966397
5 day(s) RMSE : 0.06720294
5 day(s) R2 : -0.83064417
5 day(s) Pearson r : -0.33253131
5 day(s) QLIKE : 0.26225426
10 day(s) MAE : 0.05015189
10 day(s) RMSE : 0.06796766
10 day(s) R2 : -0.80707560
10 day(s) Pearson r : -0.33422610
10 day(s) QLIKE : 0.25819816
20 day(s) MAE : 0.05169919
20 day(s) RMSE : 0.06904645
20 day(s) R2 : -0.78376980
20 day(s) Pearson r : -0.32317876
20 day(s) QLIKE : 0.23751577
full horizon MAE : 0.05169919
full horizon RMSE : 0.06904645
full horizon R2 : -0.78376980
full horizon Pearson r : -0.32317876
full horizon QLIKE : 0.23751577
--- Task 6 ---
1 day(s) MAE : 1.68330074
1 day(s) RMSE : 2.14070353
1 day(s) R2 : -1.10104850
1 day(s) Pearson r : -0.47932257
1 day(s) QLIKE : 0.07053657
3 day(s) MAE : 1.65432934
3 day(s) RMSE : 2.08836267
3 day(s) R2 : -0.99126191
3 day(s) Pearson r : -0.47967895
3 day(s) QLIKE : 0.06800707
5 day(s) MAE : 1.65668251
5 day(s) RMSE : 2.08855352
5 day(s) R2 : -0.98136640
5 day(s) Pearson r : -0.48016096
5 day(s) QLIKE : 0.06820685
10 day(s) MAE : 1.66806026
10 day(s) RMSE : 2.09919464
10 day(s) R2 : -0.97566718
10 day(s) Pearson r : -0.47899850
10 day(s) QLIKE : 0.06915634
20 day(s) MAE : 1.66807621
20 day(s) RMSE : 2.08973765
20 day(s) R2 : -0.91595456
20 day(s) Pearson r : -0.47295018
20 day(s) QLIKE : 0.06933301
full horizon MAE : 1.66807621
full horizon RMSE : 2.08973765
full horizon R2 : -0.91595456
full horizon Pearson r : -0.47295018
full horizon QLIKE : 0.06933301
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/AAPL/Custom_KAN_H20.pkl
Saved y_true min=0.314451, max=112.408
Saved y_pred min=1.21563, max=6.07227
=== AAPL | H=20 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.099874372714204
Min value: -0.03434294798653995
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.174045403839706
Min value: -13.098372439983104
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.051920323212192546
Min value: -0.0338203458440787
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.913020640489958
Min value: -6.554949262074746
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.19306601030720755
Min value: -0.03421247164703902
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.906568131251448
Min value: -13.098372439983104
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 122s 548ms/step - loss: 0.8392 - val_loss: 0.7273 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 17s 380ms/step - loss: 0.5311 - val_loss: 0.5729 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 450ms/step - loss: 0.4380 - val_loss: 0.9895 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 22s 474ms/step - loss: 0.3960 - val_loss: 0.5673 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 450ms/step - loss: 0.3701 - val_loss: 0.9167 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 450ms/step - loss: 0.3543 - val_loss: 0.5071 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 465ms/step - loss: 0.3415 - val_loss: 0.7707 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 456ms/step - loss: 0.3370 - val_loss: 0.5400 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 460ms/step - loss: 0.3300 - val_loss: 0.7480 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 448ms/step - loss: 0.3193 - val_loss: 1.3389 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 434ms/step - loss: 0.2917
Epoch 11: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 447ms/step - loss: 0.3087 - val_loss: 0.7788 - learning_rate: 5.0000e-04
Epoch 11: early stopping
Restoring model weights from the end of the best epoch: 6.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 20
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.48920489
1 day(s) RMSE : 8.70133537
1 day(s) R2 : -0.01364240
1 day(s) Pearson r : 0.47237885
1 day(s) QLIKE : 0.53532435
3 day(s) MAE : 2.62344394
3 day(s) RMSE : 8.94640594
3 day(s) R2 : -0.01278649
3 day(s) Pearson r : 0.00859488
3 day(s) QLIKE : 0.55513676
5 day(s) MAE : 2.59093773
5 day(s) RMSE : 9.06902202
5 day(s) R2 : -0.02925456
5 day(s) Pearson r : -0.00117803
5 day(s) QLIKE : 0.59108920
10 day(s) MAE : 2.66165568
10 day(s) RMSE : 9.22587455
10 day(s) R2 : -0.05640346
10 day(s) Pearson r : -0.00343953
10 day(s) QLIKE : 0.62629859
20 day(s) MAE : 2.62517297
20 day(s) RMSE : 9.21871665
20 day(s) R2 : -0.05027823
20 day(s) Pearson r : -0.00235928
20 day(s) QLIKE : 0.60221160
full horizon MAE : 2.62517297
full horizon RMSE : 9.21871665
full horizon R2 : -0.05027823
full horizon Pearson r : -0.00235928
full horizon QLIKE : 0.60221160
--- Task 2 ---
1 day(s) MAE : 0.08361652
1 day(s) RMSE : 0.11458045
1 day(s) R2 : -0.79542583
1 day(s) Pearson r : -0.26244867
1 day(s) QLIKE : 3.72293760
3 day(s) MAE : 0.08474677
3 day(s) RMSE : 0.11617794
3 day(s) R2 : -0.83018249
3 day(s) Pearson r : -0.00590791
3 day(s) QLIKE : 3.75901187
5 day(s) MAE : 0.08788551
5 day(s) RMSE : 0.12047831
5 day(s) R2 : -0.95376067
5 day(s) Pearson r : -0.00994152
5 day(s) QLIKE : 7.20769466
10 day(s) MAE : 0.09149871
10 day(s) RMSE : 0.12488260
10 day(s) R2 : -1.07066899
10 day(s) Pearson r : -0.01604894
10 day(s) QLIKE : 12.79176248
20 day(s) MAE : 0.09537101
20 day(s) RMSE : 0.12894501
20 day(s) R2 : -1.16554206
20 day(s) Pearson r : -0.02363395
20 day(s) QLIKE : 8.86001360
full horizon MAE : 0.09537101
full horizon RMSE : 0.12894501
full horizon R2 : -1.16554206
full horizon Pearson r : -0.02363395
full horizon QLIKE : 8.86001360
--- Task 3 ---
1 day(s) MAE : 0.50195262
1 day(s) RMSE : 0.53001280
1 day(s) R2 : -0.32585202
1 day(s) Pearson r : -0.05920515
1 day(s) QLIKE : 15.47962278
3 day(s) MAE : 0.48842608
3 day(s) RMSE : 0.50704975
3 day(s) R2 : -0.21778527
3 day(s) Pearson r : 0.00086605
3 day(s) QLIKE : 15.48105647
5 day(s) MAE : 0.50588580
5 day(s) RMSE : 0.54159480
5 day(s) R2 : -0.39402212
5 day(s) Pearson r : -0.00164752
5 day(s) QLIKE : 15.48651871
10 day(s) MAE : 0.55283640
10 day(s) RMSE : 0.63815808
10 day(s) R2 : -0.95187139
10 day(s) Pearson r : -0.00460666
10 day(s) QLIKE : 15.49933686
20 day(s) MAE : 0.59707377
20 day(s) RMSE : 0.71005362
20 day(s) R2 : -1.45766029
20 day(s) Pearson r : -0.00767625
20 day(s) QLIKE : 15.49312292
full horizon MAE : 0.59707377
full horizon RMSE : 0.71005362
full horizon R2 : -1.45766029
full horizon Pearson r : -0.00767625
full horizon QLIKE : 15.49312292
--- Task 4 ---
1 day(s) MAE : 1.02268840
1 day(s) RMSE : 1.22991890
1 day(s) R2 : -1.13349154
1 day(s) Pearson r : -0.08464380
1 day(s) QLIKE : 0.99637230
3 day(s) MAE : 1.02969101
3 day(s) RMSE : 1.24410641
3 day(s) R2 : -1.19163854
3 day(s) Pearson r : -0.00130991
3 day(s) QLIKE : 0.99136897
5 day(s) MAE : 1.02607872
5 day(s) RMSE : 1.23380864
5 day(s) R2 : -1.16399577
5 day(s) Pearson r : 0.00263449
5 day(s) QLIKE : 0.98680586
10 day(s) MAE : 1.00204416
10 day(s) RMSE : 1.18508553
10 day(s) R2 : -1.01679017
10 day(s) Pearson r : 0.00585048
10 day(s) QLIKE : 1.02258363
20 day(s) MAE : 0.94257148
20 day(s) RMSE : 1.08094961
20 day(s) R2 : -0.71314509
20 day(s) Pearson r : 0.01273928
20 day(s) QLIKE : 1.05559586
full horizon MAE : 0.94257148
full horizon RMSE : 1.08094961
full horizon R2 : -0.71314509
full horizon Pearson r : 0.01273928
full horizon QLIKE : 1.05559586
--- Task 5 ---
1 day(s) MAE : 0.06608492
1 day(s) RMSE : 0.08040594
1 day(s) R2 : -1.72274164
1 day(s) Pearson r : 0.30484880
1 day(s) QLIKE : 0.11160304
3 day(s) MAE : 0.06771440
3 day(s) RMSE : 0.08220846
3 day(s) R2 : -1.79359321
3 day(s) Pearson r : 0.00202635
3 day(s) QLIKE : 0.11708741
5 day(s) MAE : 0.06584282
5 day(s) RMSE : 0.08072658
5 day(s) R2 : -1.64155957
5 day(s) Pearson r : 0.00579872
5 day(s) QLIKE : 0.11976656
10 day(s) MAE : 0.08044462
10 day(s) RMSE : 0.09568450
10 day(s) R2 : -2.58141733
10 day(s) Pearson r : -0.01436070
10 day(s) QLIKE : 0.86469826
20 day(s) MAE : 0.09672599
20 day(s) RMSE : 0.11115728
20 day(s) R2 : -3.62308525
20 day(s) Pearson r : -0.02547409
20 day(s) QLIKE : 6.33471813
full horizon MAE : 0.09672599
full horizon RMSE : 0.11115728
full horizon R2 : -3.62308525
full horizon Pearson r : -0.02547409
full horizon QLIKE : 6.33471813
--- Task 6 ---
1 day(s) MAE : 1.43006278
1 day(s) RMSE : 1.57610697
1 day(s) R2 : -0.13892311
1 day(s) Pearson r : -0.16235651
1 day(s) QLIKE : 0.03597401
3 day(s) MAE : 1.42549737
3 day(s) RMSE : 1.57444226
3 day(s) R2 : -0.13180086
3 day(s) Pearson r : -0.02399268
3 day(s) QLIKE : 0.03628156
5 day(s) MAE : 1.34326766
5 day(s) RMSE : 1.54227969
5 day(s) R2 : -0.08043801
5 day(s) Pearson r : 0.00676982
5 day(s) QLIKE : 0.03767898
10 day(s) MAE : 1.26659919
10 day(s) RMSE : 1.53701849
10 day(s) R2 : -0.05917214
10 day(s) Pearson r : 0.01693161
10 day(s) QLIKE : 0.03966460
20 day(s) MAE : 1.21441608
20 day(s) RMSE : 1.59831077
20 day(s) R2 : -0.12078968
20 day(s) Pearson r : 0.04117961
20 day(s) QLIKE : 0.04305269
full horizon MAE : 1.21441608
full horizon RMSE : 1.59831077
full horizon R2 : -0.12078968
full horizon Pearson r : 0.04117961
full horizon QLIKE : 0.04305269
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/AAPL/Custom_KAN_LSTM_H20.pkl
Saved y_true min=0.314451, max=112.408
Saved y_pred min=1.02872, max=3.04886
=== MSFT | H=1 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.550928529745955
Min value: -0.49195094366183095
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.892531280455467
Min value: -4.877434769908842
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9099560750963094
Min value: -0.42477627271922275
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.8947596773346853
Min value: -6.550744378714031
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.030086059790822
Min value: -0.4794650148863312
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.930619914036179
Min value: -181.24706122190173
Epoch 001 | phase=1 | train_loss=4.9498 | val_main=0.458259
Epoch 002 | phase=1 | train_loss=4.8817 | val_main=0.458262
Epoch 003 | phase=1 | train_loss=4.7267 | val_main=0.458264
Epoch 004 | phase=1 | train_loss=4.4710 | val_main=0.458279
Epoch 005 | phase=1 | train_loss=4.4481 | val_main=0.458297
Epoch 006 | phase=1 | train_loss=4.2545 | val_main=0.458305
Epoch 007 | phase=1 | train_loss=4.1628 | val_main=0.458327
Epoch 008 | phase=1 | train_loss=3.8979 | val_main=0.458321
Epoch 009 | phase=1 | train_loss=3.4693 | val_main=0.458376
Epoch 010 | phase=1 | train_loss=3.2632 | val_main=0.458380
Epoch 011 | phase=1 | train_loss=3.0020 | val_main=0.458430
Epoch 012 | phase=1 | train_loss=2.8576 | val_main=0.458452
Epoch 013 | phase=1 | train_loss=2.6502 | val_main=0.458449
Epoch 014 | phase=1 | train_loss=2.5666 | val_main=0.458481
Epoch 015 | phase=1 | train_loss=2.4990 | val_main=0.458527
Epoch 016 | phase=0 | train_loss=3.4069 | val_main=0.463098
Epoch 017 | phase=0 | train_loss=3.1957 | val_main=0.450229
Epoch 018 | phase=0 | train_loss=2.9886 | val_main=0.454575
Epoch 019 | phase=0 | train_loss=2.8270 | val_main=0.451153
Epoch 020 | phase=0 | train_loss=2.6330 | val_main=0.471209
Epoch 021 | phase=0 | train_loss=2.5705 | val_main=0.494933
Epoch 022 | phase=0 | train_loss=2.3647 | val_main=0.506408
Epoch 023 | phase=0 | train_loss=2.2821 | val_main=0.520924
Epoch 024 | phase=0 | train_loss=2.0893 | val_main=0.493835
Epoch 025 | phase=0 | train_loss=1.9584 | val_main=0.495601
Epoch 026 | phase=0 | train_loss=1.8881 | val_main=0.527587
Epoch 027 | phase=0 | train_loss=1.6580 | val_main=0.516662
Epoch 028 | phase=0 | train_loss=2.2990 | val_main=0.543893
Epoch 029 | phase=0 | train_loss=1.7506 | val_main=0.543283
Epoch 030 | phase=0 | train_loss=1.5064 | val_main=0.530209
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 6
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.06569565
1 day(s) RMSE : 6.74668536
1 day(s) R2 : -0.01314609
1 day(s) Pearson r : 0.04933923
1 day(s) QLIKE : 0.48041865
full horizon MAE : 2.06569565
full horizon RMSE : 6.74668536
full horizon R2 : -0.01314609
full horizon Pearson r : 0.04933923
full horizon QLIKE : 0.48041865
--- Task 2 ---
1 day(s) MAE : 0.11191474
1 day(s) RMSE : 0.12254706
1 day(s) R2 : -14.83918425
1 day(s) Pearson r : -0.34973109
1 day(s) QLIKE : 14.56362632
full horizon MAE : 0.11191474
full horizon RMSE : 0.12254706
full horizon R2 : -14.83918425
full horizon Pearson r : -0.34973109
full horizon QLIKE : 14.56362632
--- Task 3 ---
1 day(s) MAE : 0.23168203
1 day(s) RMSE : 0.30513781
1 day(s) R2 : -0.05150632
1 day(s) Pearson r : -0.22572153
1 day(s) QLIKE : 1.53854846
full horizon MAE : 0.23168203
full horizon RMSE : 0.30513781
full horizon R2 : -0.05150632
full horizon Pearson r : -0.22572153
full horizon QLIKE : 1.53854846
--- Task 4 ---
1 day(s) MAE : 0.37990357
1 day(s) RMSE : 0.59628913
1 day(s) R2 : -0.34808989
1 day(s) Pearson r : -0.27561779
1 day(s) QLIKE : 3.22918412
full horizon MAE : 0.37990357
full horizon RMSE : 0.59628913
full horizon R2 : -0.34808989
full horizon Pearson r : -0.27561779
full horizon QLIKE : 3.22918412
--- Task 5 ---
1 day(s) MAE : 0.08285953
1 day(s) RMSE : 0.11006984
1 day(s) R2 : -8.65899036
1 day(s) Pearson r : 0.00029409
1 day(s) QLIKE : 0.46723172
full horizon MAE : 0.08285953
full horizon RMSE : 0.11006984
full horizon R2 : -8.65899036
full horizon Pearson r : 0.00029409
full horizon QLIKE : 0.46723172
--- Task 6 ---
1 day(s) MAE : 4.17324130
1 day(s) RMSE : 5.52334024
1 day(s) R2 : -0.43603498
1 day(s) Pearson r : -0.13130473
1 day(s) QLIKE : 0.15742201
full horizon MAE : 4.17324130
full horizon RMSE : 5.52334024
full horizon R2 : -0.43603498
full horizon Pearson r : -0.13130473
full horizon QLIKE : 0.15742201
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/MSFT/Custom_KAN_H1.pkl
Saved y_true min=0.27301, max=89.8516
Saved y_pred min=1.8109, max=4.37427
=== MSFT | H=1 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.550928529745955
Min value: -0.49195094366183095
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.892531280455467
Min value: -4.877434769908842
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9099560750963094
Min value: -0.42477627271922275
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.8947596773346853
Min value: -6.550744378714031
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.030086059790822
Min value: -0.4794650148863312
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.930619914036179
Min value: -181.24706122190173
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 97s 338ms/step - loss: 1.0103 - val_loss: 0.5394 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 7s 163ms/step - loss: 0.9547 - val_loss: 0.5279 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 8s 184ms/step - loss: 0.9124 - val_loss: 0.5376 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 8s 184ms/step - loss: 0.9001 - val_loss: 0.5250 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 8s 183ms/step - loss: 0.8891 - val_loss: 0.5296 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 8s 184ms/step - loss: 0.8908 - val_loss: 0.5450 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 8s 182ms/step - loss: 0.8852 - val_loss: 0.5576 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 8s 183ms/step - loss: 0.8760 - val_loss: 0.5373 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 181ms/step - loss: 0.8971
Epoch 9: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 186ms/step - loss: 0.8853 - val_loss: 0.5637 - learning_rate: 5.0000e-04
Epoch 9: early stopping
Restoring model weights from the end of the best epoch: 4.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 1
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 1.96958293
1 day(s) RMSE : 6.59991965
1 day(s) R2 : 0.03045391
1 day(s) Pearson r : 0.26251946
1 day(s) QLIKE : 0.37953376
full horizon MAE : 1.96958293
full horizon RMSE : 6.59991965
full horizon R2 : 0.03045391
full horizon Pearson r : 0.26251946
full horizon QLIKE : 0.37953376
--- Task 2 ---
1 day(s) MAE : 0.02096519
1 day(s) RMSE : 0.03189231
1 day(s) R2 : -0.07275115
1 day(s) Pearson r : 0.01570202
1 day(s) QLIKE : 13.27161376
full horizon MAE : 0.02096519
full horizon RMSE : 0.03189231
full horizon R2 : -0.07275115
full horizon Pearson r : 0.01570202
full horizon QLIKE : 13.27161376
--- Task 3 ---
1 day(s) MAE : 0.21920741
1 day(s) RMSE : 0.30082655
1 day(s) R2 : -0.02200304
1 day(s) Pearson r : -0.00739623
1 day(s) QLIKE : 1.53489210
full horizon MAE : 0.21920741
full horizon RMSE : 0.30082655
full horizon R2 : -0.02200304
full horizon Pearson r : -0.00739623
full horizon QLIKE : 1.53489210
--- Task 4 ---
1 day(s) MAE : 0.36466276
1 day(s) RMSE : 0.61365991
1 day(s) R2 : -0.42777760
1 day(s) Pearson r : -0.07696344
1 day(s) QLIKE : 2.54016385
full horizon MAE : 0.36466276
full horizon RMSE : 0.61365991
full horizon R2 : -0.42777760
full horizon Pearson r : -0.07696344
full horizon QLIKE : 2.54016385
--- Task 5 ---
1 day(s) MAE : 0.05068668
1 day(s) RMSE : 0.06333863
1 day(s) R2 : -2.19839770
1 day(s) Pearson r : 0.14704874
1 day(s) QLIKE : 0.64085866
full horizon MAE : 0.05068668
full horizon RMSE : 0.06333863
full horizon R2 : -2.19839770
full horizon Pearson r : 0.14704874
full horizon QLIKE : 0.64085866
--- Task 6 ---
1 day(s) MAE : 4.01395767
1 day(s) RMSE : 5.36760562
1 day(s) R2 : -0.35619651
1 day(s) Pearson r : -0.06868085
1 day(s) QLIKE : 0.16851237
full horizon MAE : 4.01395767
full horizon RMSE : 5.36760562
full horizon R2 : -0.35619651
full horizon Pearson r : -0.06868085
full horizon QLIKE : 0.16851237
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/MSFT/Custom_KAN_LSTM_H1.pkl
Saved y_true min=0.27301, max=89.8516
Saved y_pred min=1.31197, max=6.00958
=== MSFT | H=5 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.550928529745955
Min value: -0.49195094366183095
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.894907177625173
Min value: -4.875740826920772
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9099560750963094
Min value: -0.42477627271922275
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.894348562811063
Min value: -6.5517309160899515
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.030086059790822
Min value: -0.4794650148863312
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.930739994585198
Min value: -181.2597467460353
Epoch 001 | phase=1 | train_loss=4.9284 | val_main=0.454810
Epoch 002 | phase=1 | train_loss=4.8908 | val_main=0.454810
Epoch 003 | phase=1 | train_loss=4.7961 | val_main=0.454812
Epoch 004 | phase=1 | train_loss=4.4992 | val_main=0.454762
Epoch 005 | phase=1 | train_loss=4.4809 | val_main=0.454652
Epoch 006 | phase=1 | train_loss=4.2779 | val_main=0.454638
Epoch 007 | phase=1 | train_loss=4.1591 | val_main=0.454616
Epoch 008 | phase=1 | train_loss=3.9276 | val_main=0.454794
Epoch 009 | phase=1 | train_loss=3.5285 | val_main=0.454845
Epoch 010 | phase=1 | train_loss=3.3865 | val_main=0.454796
Epoch 011 | phase=1 | train_loss=3.1399 | val_main=0.454660
Epoch 012 | phase=1 | train_loss=3.0143 | val_main=0.454643
Epoch 013 | phase=1 | train_loss=2.7461 | val_main=0.454714
Epoch 014 | phase=1 | train_loss=2.6315 | val_main=0.454723
Epoch 015 | phase=1 | train_loss=2.5678 | val_main=0.454730
Epoch 016 | phase=0 | train_loss=3.4693 | val_main=0.454742
Epoch 017 | phase=0 | train_loss=3.3169 | val_main=0.449849
Epoch 018 | phase=0 | train_loss=3.1457 | val_main=0.448923
Epoch 019 | phase=0 | train_loss=2.9480 | val_main=0.483934
Epoch 020 | phase=0 | train_loss=2.7609 | val_main=0.500867
Epoch 021 | phase=0 | train_loss=2.7187 | val_main=0.518818
Epoch 022 | phase=0 | train_loss=2.5798 | val_main=0.527641
Epoch 023 | phase=0 | train_loss=2.5613 | val_main=0.538016
Epoch 024 | phase=0 | train_loss=2.4540 | val_main=0.524091
Epoch 025 | phase=0 | train_loss=2.3086 | val_main=0.531437
Epoch 026 | phase=0 | train_loss=2.1858 | val_main=0.563545
Epoch 027 | phase=0 | train_loss=1.9853 | val_main=0.523279
Epoch 028 | phase=0 | train_loss=1.8284 | val_main=0.540988
Epoch 029 | phase=0 | train_loss=1.7129 | val_main=0.526538
Epoch 030 | phase=0 | train_loss=1.6062 | val_main=0.543027
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 30
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.09821793
1 day(s) RMSE : 6.75594322
1 day(s) R2 : -0.01592850
1 day(s) Pearson r : 0.06224651
1 day(s) QLIKE : 0.50734442
3 day(s) MAE : 2.13581495
3 day(s) RMSE : 6.77751467
3 day(s) R2 : -0.02249485
3 day(s) Pearson r : 0.03421798
3 day(s) QLIKE : 0.51951570
5 day(s) MAE : 2.14613885
5 day(s) RMSE : 6.80004931
5 day(s) R2 : -0.02919009
5 day(s) Pearson r : 0.00247596
5 day(s) QLIKE : 0.52834308
full horizon MAE : 2.14613885
full horizon RMSE : 6.80004931
full horizon R2 : -0.02919009
full horizon Pearson r : 0.00247596
full horizon QLIKE : 0.52834308
--- Task 2 ---
1 day(s) MAE : 0.10457399
1 day(s) RMSE : 0.11175172
1 day(s) R2 : -12.17150679
1 day(s) Pearson r : -0.45163596
1 day(s) QLIKE : 15.58442270
3 day(s) MAE : 0.10722468
3 day(s) RMSE : 0.11544383
3 day(s) R2 : -13.05621800
3 day(s) Pearson r : -0.41900243
3 day(s) QLIKE : 15.53356609
5 day(s) MAE : 0.10629010
5 day(s) RMSE : 0.11490932
5 day(s) R2 : -12.92635717
5 day(s) Pearson r : -0.40526274
5 day(s) QLIKE : 15.41846557
full horizon MAE : 0.10629010
full horizon RMSE : 0.11490932
full horizon R2 : -12.92635717
full horizon Pearson r : -0.40526274
full horizon QLIKE : 15.41846557
--- Task 3 ---
1 day(s) MAE : 0.23542905
1 day(s) RMSE : 0.30712081
1 day(s) R2 : -0.06521764
1 day(s) Pearson r : -0.22831725
1 day(s) QLIKE : 1.53943646
3 day(s) MAE : 0.23509040
3 day(s) RMSE : 0.30711437
3 day(s) R2 : -0.06504860
3 day(s) Pearson r : -0.22662700
3 day(s) QLIKE : 1.53942477
5 day(s) MAE : 0.23491785
5 day(s) RMSE : 0.30727692
5 day(s) R2 : -0.06601649
5 day(s) Pearson r : -0.23047965
5 day(s) QLIKE : 1.53948441
full horizon MAE : 0.23491785
full horizon RMSE : 0.30727692
full horizon R2 : -0.06601649
full horizon Pearson r : -0.23047965
full horizon QLIKE : 1.53948441
--- Task 4 ---
1 day(s) MAE : 0.37697990
1 day(s) RMSE : 0.59684003
1 day(s) R2 : -0.35058200
1 day(s) Pearson r : -0.26417788
1 day(s) QLIKE : 3.51502793
3 day(s) MAE : 0.37710069
3 day(s) RMSE : 0.59823705
3 day(s) R2 : -0.35693698
3 day(s) Pearson r : -0.27658413
3 day(s) QLIKE : 3.46333072
5 day(s) MAE : 0.37701253
5 day(s) RMSE : 0.59974786
5 day(s) R2 : -0.36374391
5 day(s) Pearson r : -0.28392939
5 day(s) QLIKE : 3.41546954
full horizon MAE : 0.37701253
full horizon RMSE : 0.59974786
full horizon R2 : -0.36374391
full horizon Pearson r : -0.28392939
full horizon QLIKE : 3.41546954
--- Task 5 ---
1 day(s) MAE : 0.05490078
1 day(s) RMSE : 0.06889454
1 day(s) R2 : -2.78411853
1 day(s) Pearson r : 0.03097022
1 day(s) QLIKE : 0.08823921
3 day(s) MAE : 0.05631060
3 day(s) RMSE : 0.07047521
3 day(s) R2 : -2.98246655
3 day(s) Pearson r : 0.04490080
3 day(s) QLIKE : 0.08940142
5 day(s) MAE : 0.05646836
5 day(s) RMSE : 0.07041312
5 day(s) R2 : -2.99321786
5 day(s) Pearson r : 0.04776128
5 day(s) QLIKE : 0.08943115
full horizon MAE : 0.05646836
full horizon RMSE : 0.07041312
full horizon R2 : -2.99321786
full horizon Pearson r : 0.04776128
full horizon QLIKE : 0.08943115
--- Task 6 ---
1 day(s) MAE : 4.13323735
1 day(s) RMSE : 5.47672063
1 day(s) R2 : -0.41189565
1 day(s) Pearson r : -0.10322093
1 day(s) QLIKE : 0.15613679
3 day(s) MAE : 4.13728725
3 day(s) RMSE : 5.47082995
3 day(s) R2 : -0.40292940
3 day(s) Pearson r : -0.10676298
3 day(s) QLIKE : 0.15705181
5 day(s) MAE : 4.14777053
5 day(s) RMSE : 5.47838545
5 day(s) R2 : -0.40090624
5 day(s) Pearson r : -0.10951048
5 day(s) QLIKE : 0.15836593
full horizon MAE : 4.14777053
full horizon RMSE : 5.47838545
full horizon R2 : -0.40090624
full horizon Pearson r : -0.10951048
full horizon QLIKE : 0.15836593
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/MSFT/Custom_KAN_H5.pkl
Saved y_true min=0.27301, max=89.8516
Saved y_pred min=1.47062, max=5.42287
=== MSFT | H=5 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.550928529745955
Min value: -0.49195094366183095
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.894907177625173
Min value: -4.875740826920772
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9099560750963094
Min value: -0.42477627271922275
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.894348562811063
Min value: -6.5517309160899515
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.030086059790822
Min value: -0.4794650148863312
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.930739994585198
Min value: -181.2597467460353
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 103s 412ms/step - loss: 0.7809 - val_loss: 0.5309 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 240ms/step - loss: 0.4505 - val_loss: 0.6013 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 239ms/step - loss: 0.3529 - val_loss: 0.5288 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 244ms/step - loss: 0.3280 - val_loss: 0.6104 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 250ms/step - loss: 0.3121 - val_loss: 0.5275 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 240ms/step - loss: 0.3035 - val_loss: 0.5741 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 12s 252ms/step - loss: 0.2977 - val_loss: 0.5831 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 239ms/step - loss: 0.2952 - val_loss: 0.6283 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 247ms/step - loss: 0.2875 - val_loss: 0.6308 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 232ms/step - loss: 0.2892
Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 240ms/step - loss: 0.2855 - val_loss: 0.6239 - learning_rate: 5.0000e-04
Epoch 10: early stopping
Restoring model weights from the end of the best epoch: 5.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 5
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 1.98036567
1 day(s) RMSE : 6.61727798
1 day(s) R2 : 0.02534724
1 day(s) Pearson r : 0.23955943
1 day(s) QLIKE : 0.37725368
3 day(s) MAE : 2.09657640
3 day(s) RMSE : 6.65119007
3 day(s) R2 : 0.01526604
3 day(s) Pearson r : 0.20438250
3 day(s) QLIKE : 0.40895173
5 day(s) MAE : 2.14978118
5 day(s) RMSE : 6.68645570
5 day(s) R2 : 0.00490758
5 day(s) Pearson r : 0.16457869
5 day(s) QLIKE : 0.43332651
full horizon MAE : 2.14978118
full horizon RMSE : 6.68645570
full horizon R2 : 0.00490758
full horizon Pearson r : 0.16457869
full horizon QLIKE : 0.43332651
--- Task 2 ---
1 day(s) MAE : 0.02063631
1 day(s) RMSE : 0.03178594
1 day(s) R2 : -0.06560738
1 day(s) Pearson r : 0.10381133
1 day(s) QLIKE : 13.32515492
3 day(s) MAE : 0.01967751
3 day(s) RMSE : 0.03358416
3 day(s) R2 : -0.18958661
3 day(s) Pearson r : 0.07189903
3 day(s) QLIKE : 26.08101808
5 day(s) MAE : 0.01933705
5 day(s) RMSE : 0.03453911
5 day(s) R2 : -0.25819918
5 day(s) Pearson r : 0.04870982
5 day(s) QLIKE : 26.49248102
full horizon MAE : 0.01933705
full horizon RMSE : 0.03453911
full horizon R2 : -0.25819918
full horizon Pearson r : 0.04870982
full horizon QLIKE : 26.49248102
--- Task 3 ---
1 day(s) MAE : 0.21854148
1 day(s) RMSE : 0.30355890
1 day(s) R2 : -0.04065267
1 day(s) Pearson r : -0.09336665
1 day(s) QLIKE : 1.53511192
3 day(s) MAE : 0.21563843
3 day(s) RMSE : 0.31230766
3 day(s) R2 : -0.10137298
3 day(s) Pearson r : -0.00255283
3 day(s) QLIKE : 1.53545152
5 day(s) MAE : 0.21425954
5 day(s) RMSE : 0.31559122
5 day(s) R2 : -0.12448548
5 day(s) Pearson r : 0.01015167
5 day(s) QLIKE : 1.53532818
full horizon MAE : 0.21425954
full horizon RMSE : 0.31559122
full horizon R2 : -0.12448548
full horizon Pearson r : 0.01015167
full horizon QLIKE : 1.53532818
--- Task 4 ---
1 day(s) MAE : 0.36282261
1 day(s) RMSE : 0.60337449
1 day(s) R2 : -0.38031738
1 day(s) Pearson r : -0.08608123
1 day(s) QLIKE : 2.56695935
3 day(s) MAE : 0.36236775
3 day(s) RMSE : 0.59545033
3 day(s) R2 : -0.34432463
3 day(s) Pearson r : -0.09259660
3 day(s) QLIKE : 2.58017797
5 day(s) MAE : 0.35923935
5 day(s) RMSE : 0.58719914
5 day(s) R2 : -0.30727278
5 day(s) Pearson r : -0.07097061
5 day(s) QLIKE : 2.57142537
full horizon MAE : 0.35923935
full horizon RMSE : 0.58719914
full horizon R2 : -0.30727278
full horizon Pearson r : -0.07097061
full horizon QLIKE : 2.57142537
--- Task 5 ---
1 day(s) MAE : 0.09905877
1 day(s) RMSE : 0.10514211
1 day(s) R2 : -7.81350174
1 day(s) Pearson r : 0.19525103
1 day(s) QLIKE : 0.59820974
3 day(s) MAE : 0.09842530
3 day(s) RMSE : 0.10516443
3 day(s) R2 : -7.86782471
3 day(s) Pearson r : 0.18780890
3 day(s) QLIKE : 9.11132831
5 day(s) MAE : 0.09382319
5 day(s) RMSE : 0.10358125
5 day(s) R2 : -7.64128164
5 day(s) Pearson r : 0.13478433
5 day(s) QLIKE : 8.65549636
full horizon MAE : 0.09382319
full horizon RMSE : 0.10358125
full horizon R2 : -7.64128164
full horizon Pearson r : 0.13478433
full horizon QLIKE : 8.65549636
--- Task 6 ---
1 day(s) MAE : 4.22256008
1 day(s) RMSE : 5.62791062
1 day(s) R2 : -0.49092504
1 day(s) Pearson r : -0.08280985
1 day(s) QLIKE : 0.18217767
3 day(s) MAE : 4.20813777
3 day(s) RMSE : 5.61196536
3 day(s) R2 : -0.47624810
3 day(s) Pearson r : -0.09535613
3 day(s) QLIKE : 0.18626596
5 day(s) MAE : 4.27341987
5 day(s) RMSE : 5.66794150
5 day(s) R2 : -0.49952814
5 day(s) Pearson r : -0.10160203
5 day(s) QLIKE : 0.19270526
full horizon MAE : 4.27341987
full horizon RMSE : 5.66794150
full horizon R2 : -0.49952814
full horizon Pearson r : -0.10160203
full horizon QLIKE : 0.19270526
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/MSFT/Custom_KAN_LSTM_H5.pkl
Saved y_true min=0.27301, max=89.8516
Saved y_pred min=1.03588, max=5.66104
=== MSFT | H=10 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.550928529745955
Min value: -0.49195094366183095
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.898543949269212
Min value: -4.873616493745474
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9099560750963094
Min value: -0.42477627271922275
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.895260784986747
Min value: -6.552726793435114
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.030086059790822
Min value: -0.4794650148863312
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9328875355250386
Min value: -181.27145471977147
Epoch 001 | phase=1 | train_loss=4.9319 | val_main=0.451001
Epoch 002 | phase=1 | train_loss=4.8942 | val_main=0.450999
Epoch 003 | phase=1 | train_loss=4.7950 | val_main=0.450947
Epoch 004 | phase=1 | train_loss=4.4929 | val_main=0.450689
Epoch 005 | phase=1 | train_loss=4.4594 | val_main=0.450529
Epoch 006 | phase=1 | train_loss=4.2565 | val_main=0.450570
Epoch 007 | phase=1 | train_loss=4.1498 | val_main=0.450507
Epoch 008 | phase=1 | train_loss=3.9095 | val_main=0.450845
Epoch 009 | phase=1 | train_loss=3.5291 | val_main=0.450791
Epoch 010 | phase=1 | train_loss=3.3822 | val_main=0.450848
Epoch 011 | phase=1 | train_loss=3.1015 | val_main=0.450701
Epoch 012 | phase=1 | train_loss=2.9987 | val_main=0.450675
Epoch 013 | phase=1 | train_loss=2.7387 | val_main=0.450872
Epoch 014 | phase=1 | train_loss=2.6643 | val_main=0.450999
Epoch 015 | phase=1 | train_loss=2.5903 | val_main=0.450970
Epoch 016 | phase=0 | train_loss=3.4731 | val_main=0.450999
Epoch 017 | phase=0 | train_loss=3.3669 | val_main=0.445380
Epoch 018 | phase=0 | train_loss=3.2484 | val_main=0.439788
Epoch 019 | phase=0 | train_loss=3.0746 | val_main=0.467828
Epoch 020 | phase=0 | train_loss=2.9311 | val_main=0.492395
Epoch 021 | phase=0 | train_loss=2.9176 | val_main=0.516377
Epoch 022 | phase=0 | train_loss=2.8413 | val_main=0.532712
Epoch 023 | phase=0 | train_loss=2.6822 | val_main=0.543461
Epoch 024 | phase=0 | train_loss=2.5503 | val_main=0.536404
Epoch 025 | phase=0 | train_loss=2.4472 | val_main=0.563700
Epoch 026 | phase=0 | train_loss=2.3855 | val_main=0.567693
Epoch 027 | phase=0 | train_loss=2.8886 | val_main=0.545082
Epoch 028 | phase=0 | train_loss=2.3808 | val_main=0.556759
Epoch 029 | phase=0 | train_loss=2.2280 | val_main=0.537515
Epoch 030 | phase=0 | train_loss=2.0416 | val_main=0.544788
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 60
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.05994218
1 day(s) RMSE : 6.75480872
1 day(s) R2 : -0.01558732
1 day(s) Pearson r : 0.06498680
1 day(s) QLIKE : 0.49252290
3 day(s) MAE : 2.08826907
3 day(s) RMSE : 6.77253359
3 day(s) R2 : -0.02099245
3 day(s) Pearson r : 0.04144024
3 day(s) QLIKE : 0.50364680
5 day(s) MAE : 2.11437073
5 day(s) RMSE : 6.80084718
5 day(s) R2 : -0.02943162
5 day(s) Pearson r : 0.00362447
5 day(s) QLIKE : 0.52139784
10 day(s) MAE : 2.13950557
10 day(s) RMSE : 6.81474993
10 day(s) R2 : -0.03356899
10 day(s) Pearson r : -0.01936160
10 day(s) QLIKE : 0.51913530
full horizon MAE : 2.13950557
full horizon RMSE : 6.81474993
full horizon R2 : -0.03356899
full horizon Pearson r : -0.01936160
full horizon QLIKE : 0.51913530
--- Task 2 ---
1 day(s) MAE : 0.11644080
1 day(s) RMSE : 0.12634868
1 day(s) R2 : -15.83714529
1 day(s) Pearson r : -0.38795880
1 day(s) QLIKE : 15.59223888
3 day(s) MAE : 0.11790015
3 day(s) RMSE : 0.12741750
3 day(s) R2 : -16.12320895
3 day(s) Pearson r : -0.37447169
3 day(s) QLIKE : 15.42212447
5 day(s) MAE : 0.11290460
5 day(s) RMSE : 0.12263983
5 day(s) R2 : -14.86317456
5 day(s) Pearson r : -0.36375938
5 day(s) QLIKE : 15.38085554
10 day(s) MAE : 0.10436352
10 day(s) RMSE : 0.11449461
10 day(s) R2 : -12.82601827
10 day(s) Pearson r : -0.33365478
10 day(s) QLIKE : 15.17646493
full horizon MAE : 0.10436352
full horizon RMSE : 0.11449461
full horizon R2 : -12.82601827
full horizon Pearson r : -0.33365478
full horizon QLIKE : 15.17646493
--- Task 3 ---
1 day(s) MAE : 0.23539319
1 day(s) RMSE : 0.30691593
1 day(s) R2 : -0.06379689
1 day(s) Pearson r : -0.20733689
1 day(s) QLIKE : 1.53933859
3 day(s) MAE : 0.23503912
3 day(s) RMSE : 0.30688382
3 day(s) R2 : -0.06345009
3 day(s) Pearson r : -0.20537529
3 day(s) QLIKE : 1.53931352
5 day(s) MAE : 0.23491299
5 day(s) RMSE : 0.30710162
5 day(s) R2 : -0.06480049
5 day(s) Pearson r : -0.20891686
5 day(s) QLIKE : 1.53940358
10 day(s) MAE : 0.23405800
10 day(s) RMSE : 0.30673279
10 day(s) R2 : -0.06182585
10 day(s) Pearson r : -0.19593420
10 day(s) QLIKE : 1.53918451
full horizon MAE : 0.23405800
full horizon RMSE : 0.30673279
full horizon R2 : -0.06182585
full horizon Pearson r : -0.19593420
full horizon QLIKE : 1.53918451
--- Task 4 ---
1 day(s) MAE : 0.37932764
1 day(s) RMSE : 0.59811229
1 day(s) R2 : -0.35634607
1 day(s) Pearson r : -0.26761431
1 day(s) QLIKE : 3.47474227
3 day(s) MAE : 0.37921058
3 day(s) RMSE : 0.59968921
3 day(s) R2 : -0.36353263
3 day(s) Pearson r : -0.27620739
3 day(s) QLIKE : 3.44532146
5 day(s) MAE : 0.37926206
5 day(s) RMSE : 0.60086265
5 day(s) R2 : -0.36881837
5 day(s) Pearson r : -0.27829093
5 day(s) QLIKE : 3.41553725
10 day(s) MAE : 0.37806249
10 day(s) RMSE : 0.60266757
10 day(s) R2 : -0.37683651
10 day(s) Pearson r : -0.26144637
10 day(s) QLIKE : 3.29775869
full horizon MAE : 0.37806249
full horizon RMSE : 0.60266757
full horizon R2 : -0.37683651
full horizon Pearson r : -0.26144637
full horizon QLIKE : 3.29775869
--- Task 5 ---
1 day(s) MAE : 0.05302796
1 day(s) RMSE : 0.06766426
1 day(s) R2 : -2.65017596
1 day(s) Pearson r : 0.15579296
1 day(s) QLIKE : 0.10977132
3 day(s) MAE : 0.05299013
3 day(s) RMSE : 0.06815902
3 day(s) R2 : -2.72499734
3 day(s) Pearson r : 0.12964519
3 day(s) QLIKE : 0.10896491
5 day(s) MAE : 0.05462641
5 day(s) RMSE : 0.07108693
5 day(s) R2 : -3.07000891
5 day(s) Pearson r : 0.10016994
5 day(s) QLIKE : 0.10977313
10 day(s) MAE : 0.05403190
10 day(s) RMSE : 0.07059481
10 day(s) R2 : -3.04097647
10 day(s) Pearson r : 0.10155400
10 day(s) QLIKE : 0.11469595
full horizon MAE : 0.05403190
full horizon RMSE : 0.07059481
full horizon R2 : -3.04097647
full horizon Pearson r : 0.10155400
full horizon QLIKE : 0.11469595
--- Task 6 ---
1 day(s) MAE : 4.07316342
1 day(s) RMSE : 5.43676637
1 day(s) R2 : -0.39137042
1 day(s) Pearson r : -0.07723529
1 day(s) QLIKE : 0.15565903
3 day(s) MAE : 4.07519902
3 day(s) RMSE : 5.42541427
3 day(s) R2 : -0.37973347
3 day(s) Pearson r : -0.08146839
3 day(s) QLIKE : 0.15663308
5 day(s) MAE : 4.08002405
5 day(s) RMSE : 5.42795507
5 day(s) R2 : -0.37523333
5 day(s) Pearson r : -0.08454208
5 day(s) QLIKE : 0.15814245
10 day(s) MAE : 4.11398643
10 day(s) RMSE : 5.45584405
10 day(s) R2 : -0.37481595
10 day(s) Pearson r : -0.09447324
10 day(s) QLIKE : 0.16207371
full horizon MAE : 4.11398643
full horizon RMSE : 5.45584405
full horizon R2 : -0.37481595
full horizon Pearson r : -0.09447324
full horizon QLIKE : 0.16207371
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/MSFT/Custom_KAN_H10.pkl
Saved y_true min=0.27301, max=89.8516
Saved y_pred min=1.41726, max=5.00394
=== MSFT | H=10 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.550928529745955
Min value: -0.49195094366183095
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.898543949269212
Min value: -4.873616493745474
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9099560750963094
Min value: -0.42477627271922275
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.895260784986747
Min value: -6.552726793435114
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.030086059790822
Min value: -0.4794650148863312
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9328875355250386
Min value: -181.27145471977147
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 94s 397ms/step - loss: 0.7314 - val_loss: 0.5029 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 247ms/step - loss: 0.3720 - val_loss: 0.6175 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 321ms/step - loss: 0.2873 - val_loss: 0.7338 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 16s 348ms/step - loss: 0.2638 - val_loss: 0.5871 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 329ms/step - loss: 0.2476 - val_loss: 0.6477 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 313ms/step - loss: 0.2472
Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 323ms/step - loss: 0.2385 - val_loss: 0.7068 - learning_rate: 5.0000e-04
Epoch 6: early stopping
Restoring model weights from the end of the best epoch: 1.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 10
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.11804880
1 day(s) RMSE : 6.74060814
1 day(s) R2 : -0.01132169
1 day(s) Pearson r : 0.26091232
1 day(s) QLIKE : 0.47176355
3 day(s) MAE : 2.17090216
3 day(s) RMSE : 6.72827842
3 day(s) R2 : -0.00769268
3 day(s) Pearson r : 0.02745899
3 day(s) QLIKE : 0.47090628
5 day(s) MAE : 2.22431311
5 day(s) RMSE : 6.72202983
5 day(s) R2 : -0.00570902
5 day(s) Pearson r : 0.01401513
5 day(s) QLIKE : 0.47205390
10 day(s) MAE : 2.34567283
10 day(s) RMSE : 6.71323546
10 day(s) R2 : -0.00300566
10 day(s) Pearson r : 0.00605339
10 day(s) QLIKE : 0.47443249
full horizon MAE : 2.34567283
full horizon RMSE : 6.71323546
full horizon R2 : -0.00300566
full horizon Pearson r : 0.00605339
full horizon QLIKE : 0.47443249
--- Task 2 ---
1 day(s) MAE : 0.01944005
1 day(s) RMSE : 0.03447548
1 day(s) R2 : -0.25356729
1 day(s) Pearson r : 0.04354546
1 day(s) QLIKE : 13.11242010
3 day(s) MAE : 0.01917811
3 day(s) RMSE : 0.03511427
3 day(s) R2 : -0.30045177
3 day(s) Pearson r : 0.00255811
3 day(s) QLIKE : 15.44142370
5 day(s) MAE : 0.01902542
5 day(s) RMSE : 0.03550190
5 day(s) R2 : -0.32932210
5 day(s) Pearson r : 0.00148499
5 day(s) QLIKE : 19.21522452
10 day(s) MAE : 0.01891091
10 day(s) RMSE : 0.03578987
10 day(s) R2 : -0.35097523
10 day(s) Pearson r : 0.00091092
10 day(s) QLIKE : 17.77433360
full horizon MAE : 0.01891091
full horizon RMSE : 0.03578987
full horizon R2 : -0.35097523
full horizon Pearson r : 0.00091092
full horizon QLIKE : 17.77433360
--- Task 3 ---
1 day(s) MAE : 0.21804675
1 day(s) RMSE : 0.30217906
1 day(s) R2 : -0.03121350
1 day(s) Pearson r : -0.05189082
1 day(s) QLIKE : 1.53488363
3 day(s) MAE : 0.21644290
3 day(s) RMSE : 0.30600275
3 day(s) R2 : -0.05735252
3 day(s) Pearson r : -0.00335207
3 day(s) QLIKE : 1.53504425
5 day(s) MAE : 0.21539572
5 day(s) RMSE : 0.31326338
5 day(s) R2 : -0.10795799
5 day(s) Pearson r : -0.00260003
5 day(s) QLIKE : 1.53556103
10 day(s) MAE : 0.21548148
10 day(s) RMSE : 0.32692067
10 day(s) R2 : -0.20619534
10 day(s) Pearson r : -0.00103685
10 day(s) QLIKE : 1.53609745
full horizon MAE : 0.21548148
full horizon RMSE : 0.32692067
full horizon R2 : -0.20619534
full horizon Pearson r : -0.00103685
full horizon QLIKE : 1.53609745
--- Task 4 ---
1 day(s) MAE : 0.36377273
1 day(s) RMSE : 0.61745612
1 day(s) R2 : -0.44549725
1 day(s) Pearson r : -0.06520453
1 day(s) QLIKE : 2.41572773
3 day(s) MAE : 0.36827664
3 day(s) RMSE : 0.62501732
3 day(s) R2 : -0.48114362
3 day(s) Pearson r : -0.00206695
3 day(s) QLIKE : 2.47082000
5 day(s) MAE : 0.37035569
5 day(s) RMSE : 0.62793196
5 day(s) R2 : -0.49492905
5 day(s) Pearson r : -0.00353760
5 day(s) QLIKE : 2.47967409
10 day(s) MAE : 0.37406117
10 day(s) RMSE : 0.63216294
10 day(s) R2 : -0.51490289
10 day(s) Pearson r : -0.00204776
10 day(s) QLIKE : 2.62518115
full horizon MAE : 0.37406117
full horizon RMSE : 0.63216294
full horizon R2 : -0.51490289
full horizon Pearson r : -0.00204776
full horizon QLIKE : 2.62518115
--- Task 5 ---
1 day(s) MAE : 0.12346026
1 day(s) RMSE : 0.12840370
1 day(s) R2 : -12.14468489
1 day(s) Pearson r : 0.13032235
1 day(s) QLIKE : 0.07835040
3 day(s) MAE : 0.12370110
3 day(s) RMSE : 0.12860083
3 day(s) R2 : -12.26071172
3 day(s) Pearson r : 0.05550166
3 day(s) QLIKE : 0.08851274
5 day(s) MAE : 0.12465564
5 day(s) RMSE : 0.12950882
5 day(s) R2 : -12.50873216
5 day(s) Pearson r : 0.02966255
5 day(s) QLIKE : 0.12637769
10 day(s) MAE : 0.12721281
10 day(s) RMSE : 0.13198207
10 day(s) R2 : -13.12442191
10 day(s) Pearson r : 0.00257644
10 day(s) QLIKE : 1.19121509
full horizon MAE : 0.12721281
full horizon RMSE : 0.13198207
full horizon R2 : -13.12442191
full horizon Pearson r : 0.00257644
full horizon QLIKE : 1.19121509
--- Task 6 ---
1 day(s) MAE : 3.49673293
1 day(s) RMSE : 4.68913420
1 day(s) R2 : -0.03501525
1 day(s) Pearson r : -0.06193903
1 day(s) QLIKE : 0.14313466
3 day(s) MAE : 3.70897268
3 day(s) RMSE : 4.86365790
3 day(s) R2 : -0.10880564
3 day(s) Pearson r : -0.00618381
3 day(s) QLIKE : 0.15296510
5 day(s) MAE : 5.46204709
5 day(s) RMSE : 6.82319798
5 day(s) R2 : -1.17310067
5 day(s) Pearson r : -0.00602182
5 day(s) QLIKE : 0.18445616
10 day(s) MAE : 10.30444504
10 day(s) RMSE : 12.22188440
10 day(s) R2 : -5.89917258
10 day(s) Pearson r : -0.00912721
10 day(s) QLIKE : 0.23221389
full horizon MAE : 10.30444504
full horizon RMSE : 12.22188440
full horizon R2 : -5.89917258
full horizon Pearson r : -0.00912721
full horizon QLIKE : 0.23221389
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/MSFT/Custom_KAN_LSTM_H10.pkl
Saved y_true min=0.27301, max=89.8516
Saved y_pred min=2.46108, max=3.27167
=== MSFT | H=20 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.550928529745955
Min value: -0.49195094366183095
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.90673127244724
Min value: -4.869753815405025
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9099560750963094
Min value: -0.42477627271922275
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.8916888397222356
Min value: -6.561724659722739
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.030086059790822
Min value: -0.4794650148863312
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9300069821373937
Min value: -181.45606985742796
Epoch 001 | phase=1 | train_loss=4.9332 | val_main=0.425176
Epoch 002 | phase=1 | train_loss=4.9002 | val_main=0.425179
Epoch 003 | phase=1 | train_loss=4.8421 | val_main=0.425156
Epoch 004 | phase=1 | train_loss=4.5307 | val_main=0.424818
Epoch 005 | phase=1 | train_loss=4.4701 | val_main=0.424535
Epoch 006 | phase=1 | train_loss=4.2419 | val_main=0.424733
Epoch 007 | phase=1 | train_loss=4.1402 | val_main=0.424733
Epoch 008 | phase=1 | train_loss=3.8685 | val_main=0.425054
Epoch 009 | phase=1 | train_loss=3.5741 | val_main=0.424977
Epoch 010 | phase=1 | train_loss=3.4012 | val_main=0.425126
Epoch 011 | phase=1 | train_loss=3.1451 | val_main=0.425065
Epoch 012 | phase=1 | train_loss=3.0649 | val_main=0.424925
Epoch 013 | phase=1 | train_loss=2.8159 | val_main=0.425108
Epoch 014 | phase=1 | train_loss=2.7693 | val_main=0.425183
Epoch 015 | phase=1 | train_loss=2.7085 | val_main=0.425189
Epoch 016 | phase=0 | train_loss=3.5951 | val_main=0.425742
Epoch 017 | phase=0 | train_loss=3.5045 | val_main=0.423737
Epoch 018 | phase=0 | train_loss=3.4105 | val_main=0.419710
Epoch 019 | phase=0 | train_loss=3.2499 | val_main=0.451063
Epoch 020 | phase=0 | train_loss=3.1324 | val_main=0.475064
Epoch 021 | phase=0 | train_loss=3.1562 | val_main=0.489739
Epoch 022 | phase=0 | train_loss=3.0883 | val_main=0.502088
Epoch 023 | phase=0 | train_loss=2.9975 | val_main=0.507449
Epoch 024 | phase=0 | train_loss=2.9872 | val_main=0.530195
Epoch 025 | phase=0 | train_loss=2.9733 | val_main=0.558238
Epoch 026 | phase=0 | train_loss=2.9358 | val_main=0.527366
Epoch 027 | phase=0 | train_loss=2.7707 | val_main=0.550965
Epoch 028 | phase=0 | train_loss=2.7176 | val_main=0.609336
Epoch 029 | phase=0 | train_loss=2.6449 | val_main=0.560673
Epoch 030 | phase=0 | train_loss=2.5541 | val_main=0.537875
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 120
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.07032996
1 day(s) RMSE : 6.75243695
1 day(s) R2 : -0.01487426
1 day(s) Pearson r : 0.07028341
1 day(s) QLIKE : 0.48463290
3 day(s) MAE : 2.06625132
3 day(s) RMSE : 6.76502843
3 day(s) R2 : -0.01873083
3 day(s) Pearson r : 0.04605737
3 day(s) QLIKE : 0.48598563
5 day(s) MAE : 2.08538531
5 day(s) RMSE : 6.78586626
5 day(s) R2 : -0.02490135
5 day(s) Pearson r : 0.01262888
5 day(s) QLIKE : 0.49817016
10 day(s) MAE : 2.11895728
10 day(s) RMSE : 6.80664101
10 day(s) R2 : -0.03111075
10 day(s) Pearson r : -0.01740762
10 day(s) QLIKE : 0.50613154
20 day(s) MAE : 2.17312796
20 day(s) RMSE : 7.00877191
20 day(s) R2 : -0.03016894
20 day(s) Pearson r : -0.01399952
20 day(s) QLIKE : 0.50942814
full horizon MAE : 2.17312796
full horizon RMSE : 7.00877191
full horizon R2 : -0.03016894
full horizon Pearson r : -0.01399952
full horizon QLIKE : 0.50942814
--- Task 2 ---
1 day(s) MAE : 0.12175565
1 day(s) RMSE : 0.13456099
1 day(s) R2 : -18.09701005
1 day(s) Pearson r : -0.36954693
1 day(s) QLIKE : 14.88330357
3 day(s) MAE : 0.12018755
3 day(s) RMSE : 0.13357548
3 day(s) R2 : -17.81830486
3 day(s) Pearson r : -0.34839402
3 day(s) QLIKE : 14.82031616
5 day(s) MAE : 0.12096849
5 day(s) RMSE : 0.13467366
5 day(s) R2 : -18.12900528
5 day(s) Pearson r : -0.33399897
5 day(s) QLIKE : 14.77835817
10 day(s) MAE : 0.11081658
10 day(s) RMSE : 0.12419445
10 day(s) R2 : -15.26789558
10 day(s) Pearson r : -0.28505602
10 day(s) QLIKE : 14.63115543
20 day(s) MAE : 0.09344805
20 day(s) RMSE : 0.10796179
20 day(s) R2 : -11.30062717
20 day(s) Pearson r : -0.17465136
20 day(s) QLIKE : 14.24978028
full horizon MAE : 0.09344805
full horizon RMSE : 0.10796179
full horizon R2 : -11.30062717
full horizon Pearson r : -0.17465136
full horizon QLIKE : 14.24978028
--- Task 3 ---
1 day(s) MAE : 0.23805396
1 day(s) RMSE : 0.30967289
1 day(s) R2 : -0.08299445
1 day(s) Pearson r : -0.22335583
1 day(s) QLIKE : 1.54051584
3 day(s) MAE : 0.23743387
3 day(s) RMSE : 0.30974245
3 day(s) R2 : -0.08335454
3 day(s) Pearson r : -0.22597406
3 day(s) QLIKE : 1.54052621
5 day(s) MAE : 0.23700739
5 day(s) RMSE : 0.30977584
5 day(s) R2 : -0.08342569
5 day(s) Pearson r : -0.22562679
5 day(s) QLIKE : 1.54051977
10 day(s) MAE : 0.23554031
10 day(s) RMSE : 0.30889867
10 day(s) R2 : -0.07687420
10 day(s) Pearson r : -0.20427875
10 day(s) QLIKE : 1.54005809
20 day(s) MAE : 0.23284303
20 day(s) RMSE : 0.30806627
20 day(s) R2 : -0.07104812
20 day(s) Pearson r : -0.18062353
20 day(s) QLIKE : 1.53957974
full horizon MAE : 0.23284303
full horizon RMSE : 0.30806627
full horizon R2 : -0.07104812
full horizon Pearson r : -0.18062353
full horizon QLIKE : 1.53957974
--- Task 4 ---
1 day(s) MAE : 0.38070263
1 day(s) RMSE : 0.60856164
1 day(s) R2 : -0.40415229
1 day(s) Pearson r : -0.32737690
1 day(s) QLIKE : 3.15913931
3 day(s) MAE : 0.38061183
3 day(s) RMSE : 0.60936838
3 day(s) R2 : -0.40790355
3 day(s) Pearson r : -0.32674117
3 day(s) QLIKE : 3.14406199
5 day(s) MAE : 0.38053155
5 day(s) RMSE : 0.60950199
5 day(s) R2 : -0.40846372
5 day(s) Pearson r : -0.31862185
5 day(s) QLIKE : 3.14414242
10 day(s) MAE : 0.37894013
10 day(s) RMSE : 0.60900300
10 day(s) R2 : -0.40593613
10 day(s) Pearson r : -0.28746183
10 day(s) QLIKE : 3.10015440
20 day(s) MAE : 0.37689161
20 day(s) RMSE : 0.61044123
20 day(s) R2 : -0.41315953
20 day(s) Pearson r : -0.27173998
20 day(s) QLIKE : 3.03407174
full horizon MAE : 0.37689161
full horizon RMSE : 0.61044123
full horizon R2 : -0.41315953
full horizon Pearson r : -0.27173998
full horizon QLIKE : 3.03407174
--- Task 5 ---
1 day(s) MAE : 0.04795582
1 day(s) RMSE : 0.06062639
1 day(s) R2 : -1.93034298
1 day(s) Pearson r : 0.12717250
1 day(s) QLIKE : 0.13877042
3 day(s) MAE : 0.05675102
3 day(s) RMSE : 0.07467591
3 day(s) R2 : -3.47136772
3 day(s) Pearson r : 0.10253219
3 day(s) QLIKE : 0.14744881
5 day(s) MAE : 0.06066590
5 day(s) RMSE : 0.07960991
5 day(s) R2 : -4.10446376
5 day(s) Pearson r : 0.08720359
5 day(s) QLIKE : 0.16696606
10 day(s) MAE : 0.05743160
10 day(s) RMSE : 0.07387950
10 day(s) R2 : -3.42576795
10 day(s) Pearson r : 0.09247784
10 day(s) QLIKE : 0.17658716
20 day(s) MAE : 0.05822765
20 day(s) RMSE : 0.07362033
20 day(s) R2 : -3.36106467
20 day(s) Pearson r : 0.09174663
20 day(s) QLIKE : 0.23381683
full horizon MAE : 0.05822765
full horizon RMSE : 0.07362033
full horizon R2 : -3.36106467
full horizon Pearson r : 0.09174663
full horizon QLIKE : 0.23381683
--- Task 6 ---
1 day(s) MAE : 4.07569941
1 day(s) RMSE : 5.54103887
1 day(s) R2 : -0.44525280
1 day(s) Pearson r : -0.05902290
1 day(s) QLIKE : 0.15899659
3 day(s) MAE : 4.10113847
3 day(s) RMSE : 5.56355318
3 day(s) R2 : -0.45088795
3 day(s) Pearson r : -0.06112668
3 day(s) QLIKE : 0.16045445
5 day(s) MAE : 4.11147252
5 day(s) RMSE : 5.56425683
5 day(s) R2 : -0.44516767
5 day(s) Pearson r : -0.06424020
5 day(s) QLIKE : 0.16161143
10 day(s) MAE : 4.15294003
10 day(s) RMSE : 5.60113909
10 day(s) R2 : -0.44901666
10 day(s) Pearson r : -0.07773766
10 day(s) QLIKE : 0.16596920
20 day(s) MAE : 4.25121070
20 day(s) RMSE : 5.67243087
20 day(s) R2 : -0.45493012
20 day(s) Pearson r : -0.10263408
20 day(s) QLIKE : 0.17492979
full horizon MAE : 4.25121070
full horizon RMSE : 5.67243087
full horizon R2 : -0.45493012
full horizon Pearson r : -0.10263408
full horizon QLIKE : 0.17492979
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/MSFT/Custom_KAN_H20.pkl
Saved y_true min=0.27301, max=89.8516
Saved y_pred min=1.52378, max=4.95886
=== MSFT | H=20 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.550928529745955
Min value: -0.49195094366183095
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.90673127244724
Min value: -4.869753815405025
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9099560750963094
Min value: -0.42477627271922275
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.8916888397222356
Min value: -6.561724659722739
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.030086059790822
Min value: -0.4794650148863312
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9300069821373937
Min value: -181.45606985742796
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 104s 584ms/step - loss: 0.7231 - val_loss: 0.4606 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 16s 350ms/step - loss: 0.3363 - val_loss: 0.6615 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 462ms/step - loss: 0.2532 - val_loss: 0.7196 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 461ms/step - loss: 0.2255 - val_loss: 0.5380 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 23s 491ms/step - loss: 0.2267 - val_loss: 0.4307 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 22s 479ms/step - loss: 0.2102 - val_loss: 0.4241 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 23s 495ms/step - loss: 0.2041 - val_loss: 0.5022 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 465ms/step - loss: 0.1938 - val_loss: 0.4864 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 462ms/step - loss: 0.1946 - val_loss: 0.9998 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 22s 482ms/step - loss: 0.1835 - val_loss: 0.4994 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 451ms/step - loss: 0.1763
Epoch 11: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 464ms/step - loss: 0.1742 - val_loss: 0.8783 - learning_rate: 5.0000e-04
Epoch 11: early stopping
Restoring model weights from the end of the best epoch: 6.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 20
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 1.98886518
1 day(s) RMSE : 6.76937606
1 day(s) R2 : -0.01997245
1 day(s) Pearson r : 0.25823553
1 day(s) QLIKE : 0.45828279
3 day(s) MAE : 1.97812001
3 day(s) RMSE : 6.81766424
3 day(s) R2 : -0.03464512
3 day(s) Pearson r : 0.02518825
3 day(s) QLIKE : 0.47323862
5 day(s) MAE : 1.98209058
5 day(s) RMSE : 6.83140741
5 day(s) R2 : -0.03870410
5 day(s) Pearson r : 0.00886094
5 day(s) QLIKE : 0.47509543
10 day(s) MAE : 2.02694648
10 day(s) RMSE : 6.80434563
10 day(s) R2 : -0.03041543
10 day(s) Pearson r : 0.00528899
10 day(s) QLIKE : 0.47787671
20 day(s) MAE : 2.15855082
20 day(s) RMSE : 6.98159968
20 day(s) R2 : -0.02219672
20 day(s) Pearson r : 0.00694684
20 day(s) QLIKE : 0.49360606
full horizon MAE : 2.15855082
full horizon RMSE : 6.98159968
full horizon R2 : -0.02219672
full horizon Pearson r : 0.00694684
full horizon QLIKE : 0.49360606
--- Task 2 ---
1 day(s) MAE : 0.01958084
1 day(s) RMSE : 0.03417473
1 day(s) R2 : -0.23179132
1 day(s) Pearson r : 0.05071413
1 day(s) QLIKE : 13.11202109
3 day(s) MAE : 0.01907710
3 day(s) RMSE : 0.03539899
3 day(s) R2 : -0.32162685
3 day(s) Pearson r : 0.00210716
3 day(s) QLIKE : 17.10902322
5 day(s) MAE : 0.01896482
5 day(s) RMSE : 0.03567116
5 day(s) R2 : -0.34202746
5 day(s) Pearson r : 0.00146058
5 day(s) QLIKE : 20.28693197
10 day(s) MAE : 0.01888060
10 day(s) RMSE : 0.03587392
10 day(s) R2 : -0.35732791
10 day(s) Pearson r : 0.00096332
10 day(s) QLIKE : 17.72364256
20 day(s) MAE : 0.01888520
20 day(s) RMSE : 0.03599142
20 day(s) R2 : -0.36705198
20 day(s) Pearson r : 0.00028542
20 day(s) QLIKE : 15.43411716
full horizon MAE : 0.01888520
full horizon RMSE : 0.03599142
full horizon R2 : -0.36705198
full horizon Pearson r : 0.00028542
full horizon QLIKE : 15.43411716
--- Task 3 ---
1 day(s) MAE : 0.22044988
1 day(s) RMSE : 0.29953173
1 day(s) R2 : -0.01322410
1 day(s) Pearson r : 0.05768947
1 day(s) QLIKE : 1.53487512
3 day(s) MAE : 0.21819802
3 day(s) RMSE : 0.30249520
3 day(s) R2 : -0.03325164
3 day(s) Pearson r : -0.00409841
3 day(s) QLIKE : 1.53504571
5 day(s) MAE : 0.21814106
5 day(s) RMSE : 0.30246197
5 day(s) R2 : -0.03286981
5 day(s) Pearson r : -0.00862321
5 day(s) QLIKE : 1.53502820
10 day(s) MAE : 0.22041222
10 day(s) RMSE : 0.30033844
10 day(s) R2 : -0.01801627
10 day(s) Pearson r : 0.00914782
10 day(s) QLIKE : 1.53513238
20 day(s) MAE : 0.22444092
20 day(s) RMSE : 0.29902972
20 day(s) R2 : -0.00913527
20 day(s) Pearson r : 0.00448845
20 day(s) QLIKE : 1.53536035
full horizon MAE : 0.22444092
full horizon RMSE : 0.29902972
full horizon R2 : -0.00913527
full horizon Pearson r : 0.00448845
full horizon QLIKE : 1.53536035
--- Task 4 ---
1 day(s) MAE : 0.35981570
1 day(s) RMSE : 0.60808029
1 day(s) R2 : -0.40193193
1 day(s) Pearson r : 0.04771480
1 day(s) QLIKE : 2.41482785
3 day(s) MAE : 0.36123164
3 day(s) RMSE : 0.61118439
3 day(s) R2 : -0.41630759
3 day(s) Pearson r : -0.00059906
3 day(s) QLIKE : 2.43739071
5 day(s) MAE : 0.35785916
5 day(s) RMSE : 0.60037148
5 day(s) R2 : -0.36658141
5 day(s) Pearson r : 0.00640399
5 day(s) QLIKE : 2.48997613
10 day(s) MAE : 0.35524244
10 day(s) RMSE : 0.56988657
10 day(s) R2 : -0.23112905
10 day(s) Pearson r : 0.00658217
10 day(s) QLIKE : 2.64725435
20 day(s) MAE : 0.39899784
20 day(s) RMSE : 0.55178461
20 day(s) R2 : -0.15462945
20 day(s) Pearson r : 0.00388012
20 day(s) QLIKE : 2.82999380
full horizon MAE : 0.39899784
full horizon RMSE : 0.55178461
full horizon R2 : -0.15462945
full horizon Pearson r : 0.00388012
full horizon QLIKE : 2.82999380
--- Task 5 ---
1 day(s) MAE : 0.12235100
1 day(s) RMSE : 0.12734595
1 day(s) R2 : -11.92901339
1 day(s) Pearson r : -0.08861294
1 day(s) QLIKE : 0.08039153
3 day(s) MAE : 0.12107725
3 day(s) RMSE : 0.12620874
3 day(s) R2 : -11.77197887
3 day(s) Pearson r : -0.01684983
3 day(s) QLIKE : 0.25737520
5 day(s) MAE : 0.12472748
5 day(s) RMSE : 0.12973706
5 day(s) R2 : -12.55638708
5 day(s) Pearson r : -0.01055463
5 day(s) QLIKE : 6.93764917
10 day(s) MAE : 0.12753204
10 day(s) RMSE : 0.13237117
10 day(s) R2 : -13.20782529
10 day(s) Pearson r : -0.00772788
10 day(s) QLIKE : 3.80121026
20 day(s) MAE : 0.12876299
20 day(s) RMSE : 0.13355309
20 day(s) R2 : -13.35174988
20 day(s) Pearson r : -0.00380739
20 day(s) QLIKE : 1.97184411
full horizon MAE : 0.12876299
full horizon RMSE : 0.13355309
full horizon R2 : -13.35174988
full horizon Pearson r : -0.00380739
full horizon QLIKE : 1.97184411
--- Task 6 ---
1 day(s) MAE : 3.50439813
1 day(s) RMSE : 4.70069509
1 day(s) R2 : -0.04012513
1 day(s) Pearson r : -0.04062261
1 day(s) QLIKE : 0.14310237
3 day(s) MAE : 3.54309113
3 day(s) RMSE : 4.72994684
3 day(s) R2 : -0.04867739
3 day(s) Pearson r : -0.00027379
3 day(s) QLIKE : 0.14446786
5 day(s) MAE : 3.55322358
5 day(s) RMSE : 4.71696601
5 day(s) R2 : -0.03855475
5 day(s) Pearson r : 0.00084018
5 day(s) QLIKE : 0.14789685
10 day(s) MAE : 3.94863327
10 day(s) RMSE : 5.09153196
10 day(s) R2 : -0.19734034
10 day(s) Pearson r : 0.00132221
10 day(s) QLIKE : 0.16129947
20 day(s) MAE : 5.12150836
20 day(s) RMSE : 6.34123074
20 day(s) R2 : -0.81823839
20 day(s) Pearson r : 0.00338737
20 day(s) QLIKE : 0.17850778
full horizon MAE : 5.12150836
full horizon RMSE : 6.34123074
full horizon R2 : -0.81823839
full horizon Pearson r : 0.00338737
full horizon QLIKE : 0.17850778
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/MSFT/Custom_KAN_LSTM_H20.pkl
Saved y_true min=0.27301, max=89.8516
Saved y_pred min=1.39824, max=2.99016
=== GE | H=1 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.10128934238557
Min value: -0.033788504883313815
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.030029259880507
Min value: -8.1422898936537
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4221295497976101
Min value: -0.03368162544886536
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.0718644033108666
Min value: -68.7433710807441
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4422974929765818
Min value: -0.03368162544886536
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.646703896552387
Min value: -3.0804908670397175
Epoch 001 | phase=1 | train_loss=5.0710 | val_main=0.842711
Epoch 002 | phase=1 | train_loss=4.7646 | val_main=0.842600
Epoch 003 | phase=1 | train_loss=4.5321 | val_main=0.842775
Epoch 004 | phase=1 | train_loss=4.5756 | val_main=0.842752
Epoch 005 | phase=1 | train_loss=4.5848 | val_main=0.842871
Epoch 006 | phase=1 | train_loss=4.5434 | val_main=0.842617
Epoch 007 | phase=1 | train_loss=4.5911 | val_main=0.842696
Epoch 008 | phase=1 | train_loss=4.4970 | val_main=0.842798
Epoch 009 | phase=1 | train_loss=4.4980 | val_main=0.842865
Epoch 010 | phase=1 | train_loss=4.5037 | val_main=0.842897
Epoch 011 | phase=1 | train_loss=4.5152 | val_main=0.842853
Epoch 012 | phase=1 | train_loss=4.4820 | val_main=0.842826
Epoch 013 | phase=1 | train_loss=4.4902 | val_main=0.842879
Epoch 014 | phase=1 | train_loss=4.5328 | val_main=0.842760
Epoch 015 | phase=1 | train_loss=4.4615 | val_main=0.842848
Epoch 016 | phase=0 | train_loss=5.5637 | val_main=0.794035
Epoch 017 | phase=0 | train_loss=5.4957 | val_main=0.866750
Epoch 018 | phase=0 | train_loss=5.5295 | val_main=0.896227
Epoch 019 | phase=0 | train_loss=5.5653 | val_main=0.858292
Epoch 020 | phase=0 | train_loss=5.6043 | val_main=0.873546
Epoch 021 | phase=0 | train_loss=5.4805 | val_main=0.880784
Epoch 022 | phase=0 | train_loss=5.5055 | val_main=0.886490
Epoch 023 | phase=0 | train_loss=5.5762 | val_main=0.900653
Epoch 024 | phase=0 | train_loss=5.5275 | val_main=0.829073
Epoch 025 | phase=0 | train_loss=5.5597 | val_main=0.885910
Epoch 026 | phase=0 | train_loss=5.5787 | val_main=0.842028
Epoch 027 | phase=0 | train_loss=5.3252 | val_main=1.179669
Epoch 028 | phase=0 | train_loss=5.2558 | val_main=0.438044
Epoch 029 | phase=0 | train_loss=5.1978 | val_main=0.441264
Epoch 030 | phase=0 | train_loss=5.1526 | val_main=0.604834
Epoch 031 | phase=2 | train_loss=0.6096 | val_main=0.618603
Epoch 032 | phase=2 | train_loss=0.6397 | val_main=0.679918
Epoch 033 | phase=2 | train_loss=0.6083 | val_main=0.854173
Epoch 034 | phase=2 | train_loss=0.6119 | val_main=0.541600
Epoch 035 | phase=2 | train_loss=0.5788 | val_main=0.663367
Epoch 036 | phase=2 | train_loss=0.5569 | val_main=0.564477
Epoch 037 | phase=2 | train_loss=0.6752 | val_main=0.658690
Epoch 038 | phase=2 | train_loss=0.6002 | val_main=0.643394
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 6
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.12300584
1 day(s) RMSE : 27.56693370
1 day(s) R2 : -0.01029016
1 day(s) Pearson r : 0.01215590
1 day(s) QLIKE : 0.69133027
full horizon MAE : 4.12300584
full horizon RMSE : 27.56693370
full horizon R2 : -0.01029016
full horizon Pearson r : 0.01215590
full horizon QLIKE : 0.69133027
--- Task 2 ---
1 day(s) MAE : 0.05021190
1 day(s) RMSE : 0.06219128
1 day(s) R2 : -1.69440917
1 day(s) Pearson r : -0.03589597
1 day(s) QLIKE : 1.99235480
full horizon MAE : 0.05021190
full horizon RMSE : 0.06219128
full horizon R2 : -1.69440917
full horizon Pearson r : -0.03589597
full horizon QLIKE : 1.99235480
--- Task 3 ---
1 day(s) MAE : 0.75169579
1 day(s) RMSE : 0.77351799
1 day(s) R2 : -16.88775226
1 day(s) Pearson r : 0.12575288
1 day(s) QLIKE : 0.10660118
full horizon MAE : 0.75169579
full horizon RMSE : 0.77351799
full horizon R2 : -16.88775226
full horizon Pearson r : 0.12575288
full horizon QLIKE : 0.10660118
--- Task 4 ---
1 day(s) MAE : 0.56401685
1 day(s) RMSE : 0.99354596
1 day(s) R2 : -0.17351806
1 day(s) Pearson r : 0.10153469
1 day(s) QLIKE : 0.61852354
full horizon MAE : 0.56401685
full horizon RMSE : 0.99354596
full horizon R2 : -0.17351806
full horizon Pearson r : 0.10153469
full horizon QLIKE : 0.61852354
--- Task 5 ---
1 day(s) MAE : 0.23230417
1 day(s) RMSE : 0.23950211
1 day(s) R2 : -15.89067576
1 day(s) Pearson r : nan
1 day(s) QLIKE : 0.02992601
full horizon MAE : 0.23230417
full horizon RMSE : 0.23950211
full horizon R2 : -15.89067576
full horizon Pearson r : nan
full horizon QLIKE : 0.02992601
--- Task 6 ---
1 day(s) MAE : 1.52036156
1 day(s) RMSE : 1.91765234
1 day(s) R2 : -0.00322258
1 day(s) Pearson r : -0.31067792
1 day(s) QLIKE : 0.08090927
full horizon MAE : 1.52036156
full horizon RMSE : 1.91765234
full horizon R2 : -0.00322258
full horizon Pearson r : -0.31067792
full horizon QLIKE : 0.08090927
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GE/Custom_KAN_H1.pkl
Saved y_true min=0.591065, max=527.531
Saved y_pred min=2.56524, max=9.73254
=== GE | H=1 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.10128934238557
Min value: -0.033788504883313815
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.030029259880507
Min value: -8.1422898936537
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4221295497976101
Min value: -0.03368162544886536
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.0718644033108666
Min value: -68.7433710807441
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4422974929765818
Min value: -0.03368162544886536
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.646703896552387
Min value: -3.0804908670397175
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 103s 346ms/step - loss: 1.0123 - val_loss: 1.0138 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 7s 161ms/step - loss: 0.9989 - val_loss: 0.9955 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 8s 182ms/step - loss: 0.9997 - val_loss: 1.0197 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 8s 184ms/step - loss: 0.9996 - val_loss: 1.0497 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 8s 183ms/step - loss: 0.9991 - val_loss: 1.0178 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 186ms/step - loss: 0.9999 - val_loss: 1.0462 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 179ms/step - loss: 1.0202
Epoch 7: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 185ms/step - loss: 0.9996 - val_loss: 1.0409 - learning_rate: 5.0000e-04
Epoch 7: early stopping
Restoring model weights from the end of the best epoch: 2.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 1
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.87094318
1 day(s) RMSE : 27.45119317
1 day(s) R2 : -0.00182450
1 day(s) Pearson r : 0.04686902
1 day(s) QLIKE : 0.68436841
full horizon MAE : 4.87094318
full horizon RMSE : 27.45119317
full horizon R2 : -0.00182450
full horizon Pearson r : 0.04686902
full horizon QLIKE : 0.68436841
--- Task 2 ---
1 day(s) MAE : 0.04726677
1 day(s) RMSE : 0.05921011
1 day(s) R2 : -1.44228490
1 day(s) Pearson r : 0.04266968
1 day(s) QLIKE : 1.99187496
full horizon MAE : 0.04726677
full horizon RMSE : 0.05921011
full horizon R2 : -1.44228490
full horizon Pearson r : 0.04266968
full horizon QLIKE : 1.99187496
--- Task 3 ---
1 day(s) MAE : 0.75403278
1 day(s) RMSE : 0.77582798
1 day(s) R2 : -16.99474986
1 day(s) Pearson r : 0.04756052
1 day(s) QLIKE : 0.10599449
full horizon MAE : 0.75403278
full horizon RMSE : 0.77582798
full horizon R2 : -16.99474986
full horizon Pearson r : 0.04756052
full horizon QLIKE : 0.10599449
--- Task 4 ---
1 day(s) MAE : 0.56416489
1 day(s) RMSE : 0.97783457
1 day(s) R2 : -0.13669678
1 day(s) Pearson r : 0.00111095
1 day(s) QLIKE : 0.61887840
full horizon MAE : 0.56416489
full horizon RMSE : 0.97783457
full horizon R2 : -0.13669678
full horizon Pearson r : 0.00111095
full horizon QLIKE : 0.61887840
--- Task 5 ---
1 day(s) MAE : 0.23230417
1 day(s) RMSE : 0.23950211
1 day(s) R2 : -15.89067577
1 day(s) Pearson r : nan
1 day(s) QLIKE : 0.02992601
full horizon MAE : 0.23230417
full horizon RMSE : 0.23950211
full horizon R2 : -15.89067577
full horizon Pearson r : nan
full horizon QLIKE : 0.02992601
--- Task 6 ---
1 day(s) MAE : 1.46652432
1 day(s) RMSE : 1.92290122
1 day(s) R2 : -0.00872203
1 day(s) Pearson r : 0.10899651
1 day(s) QLIKE : 0.08064682
full horizon MAE : 1.46652432
full horizon RMSE : 1.92290122
full horizon R2 : -0.00872203
full horizon Pearson r : 0.10899651
full horizon QLIKE : 0.08064682
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GE/Custom_KAN_LSTM_H1.pkl
Saved y_true min=0.591065, max=527.531
Saved y_pred min=4.74942, max=4.75008
=== GE | H=5 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.10128934238557
Min value: -0.033788504883313815
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.05628901565783
Min value: -8.143860691767992
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4221295497976101
Min value: -0.03368162544886536
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.0759413314511823
Min value: -68.74229956296557
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4422974929765818
Min value: -0.03368162544886536
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.658989486293699
Min value: -3.0732655627744725
Epoch 001 | phase=1 | train_loss=5.0952 | val_main=0.867658
Epoch 002 | phase=1 | train_loss=4.9363 | val_main=0.867721
Epoch 003 | phase=1 | train_loss=4.5525 | val_main=0.867845
Epoch 004 | phase=1 | train_loss=4.5616 | val_main=0.867784
Epoch 005 | phase=1 | train_loss=4.5859 | val_main=0.867760
Epoch 006 | phase=1 | train_loss=4.5461 | val_main=0.867722
Epoch 007 | phase=1 | train_loss=4.5756 | val_main=0.867722
Epoch 008 | phase=1 | train_loss=4.5846 | val_main=0.867731
Epoch 009 | phase=1 | train_loss=4.5034 | val_main=0.867746
Epoch 010 | phase=1 | train_loss=4.5166 | val_main=0.867771
Epoch 011 | phase=1 | train_loss=4.5179 | val_main=0.867754
Epoch 012 | phase=1 | train_loss=4.4989 | val_main=0.867738
Epoch 013 | phase=1 | train_loss=4.5151 | val_main=0.867742
Epoch 014 | phase=1 | train_loss=4.5484 | val_main=0.867720
Epoch 015 | phase=1 | train_loss=4.4582 | val_main=0.867731
Epoch 016 | phase=0 | train_loss=5.5785 | val_main=0.852679
Epoch 017 | phase=0 | train_loss=5.5946 | val_main=0.882634
Epoch 018 | phase=0 | train_loss=5.5319 | val_main=0.916721
Epoch 019 | phase=0 | train_loss=5.5679 | val_main=0.883187
Epoch 020 | phase=0 | train_loss=5.6231 | val_main=0.895322
Epoch 021 | phase=0 | train_loss=5.5177 | val_main=0.907959
Epoch 022 | phase=0 | train_loss=5.5440 | val_main=0.904905
Epoch 023 | phase=0 | train_loss=5.5521 | val_main=0.919954
Epoch 024 | phase=0 | train_loss=5.5140 | val_main=0.879151
Epoch 025 | phase=0 | train_loss=5.4517 | val_main=0.775710
Epoch 026 | phase=0 | train_loss=5.3584 | val_main=0.642987
Epoch 027 | phase=0 | train_loss=5.1670 | val_main=0.560745
Epoch 028 | phase=0 | train_loss=5.1984 | val_main=0.528654
Epoch 029 | phase=0 | train_loss=5.1512 | val_main=0.594116
Epoch 030 | phase=0 | train_loss=5.1045 | val_main=0.663784
Epoch 031 | phase=2 | train_loss=0.6572 | val_main=0.578249
Epoch 032 | phase=2 | train_loss=0.6440 | val_main=0.804896
Epoch 033 | phase=2 | train_loss=0.6400 | val_main=0.640228
Epoch 034 | phase=2 | train_loss=0.6040 | val_main=0.551303
Epoch 035 | phase=2 | train_loss=0.6084 | val_main=0.647560
Epoch 036 | phase=2 | train_loss=0.5857 | val_main=0.535005
Epoch 037 | phase=2 | train_loss=0.6273 | val_main=0.633939
Epoch 038 | phase=2 | train_loss=0.5835 | val_main=0.538227
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 30
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.25232748
1 day(s) RMSE : 27.62316197
1 day(s) R2 : -0.01441574
1 day(s) Pearson r : 0.01692081
1 day(s) QLIKE : 0.72270956
3 day(s) MAE : 4.28871779
3 day(s) RMSE : 27.63199939
3 day(s) R2 : -0.01512333
3 day(s) Pearson r : 0.00767785
3 day(s) QLIKE : 0.73446514
5 day(s) MAE : 4.32651428
5 day(s) RMSE : 27.63859680
5 day(s) R2 : -0.01566630
5 day(s) Pearson r : 0.00157877
5 day(s) QLIKE : 0.74317571
full horizon MAE : 4.32651428
full horizon RMSE : 27.63859680
full horizon R2 : -0.01566630
full horizon Pearson r : 0.00157877
full horizon QLIKE : 0.74317571
--- Task 2 ---
1 day(s) MAE : 0.05034019
1 day(s) RMSE : 0.06231861
1 day(s) R2 : -1.70545341
1 day(s) Pearson r : -0.00731574
1 day(s) QLIKE : 1.99197639
3 day(s) MAE : 0.05058089
3 day(s) RMSE : 0.06254951
3 day(s) R2 : -1.71860472
3 day(s) Pearson r : 0.00499071
3 day(s) QLIKE : 1.93427160
5 day(s) MAE : 0.05091395
5 day(s) RMSE : 0.06287211
5 day(s) R2 : -1.74031101
5 day(s) Pearson r : -0.00345166
5 day(s) QLIKE : 1.87690970
full horizon MAE : 0.05091395
full horizon RMSE : 0.06287211
full horizon R2 : -1.74031101
full horizon Pearson r : -0.00345166
full horizon QLIKE : 1.87690970
--- Task 3 ---
1 day(s) MAE : 0.75160391
1 day(s) RMSE : 0.77342339
1 day(s) R2 : -16.88337715
1 day(s) Pearson r : 0.12263347
1 day(s) QLIKE : 0.10783720
3 day(s) MAE : 0.75087743
3 day(s) RMSE : 0.77265497
3 day(s) R2 : -16.90212633
3 day(s) Pearson r : 0.11116016
3 day(s) QLIKE : 0.10829613
5 day(s) MAE : 0.75008868
5 day(s) RMSE : 0.77182233
5 day(s) R2 : -16.91932906
5 day(s) Pearson r : 0.11001806
5 day(s) QLIKE : 0.10807462
full horizon MAE : 0.75008868
full horizon RMSE : 0.77182233
full horizon R2 : -16.91932906
full horizon Pearson r : 0.11001806
full horizon QLIKE : 0.10807462
--- Task 4 ---
1 day(s) MAE : 0.56439930
1 day(s) RMSE : 0.99612561
1 day(s) R2 : -0.17961983
1 day(s) Pearson r : 0.09254045
1 day(s) QLIKE : 0.61856579
3 day(s) MAE : 0.56571367
3 day(s) RMSE : 0.99636728
3 day(s) R2 : -0.18117582
3 day(s) Pearson r : 0.05829948
3 day(s) QLIKE : 0.61116607
5 day(s) MAE : 0.56699166
5 day(s) RMSE : 0.99675770
5 day(s) R2 : -0.18319605
5 day(s) Pearson r : 0.04958130
5 day(s) QLIKE : 0.60445765
full horizon MAE : 0.56699166
full horizon RMSE : 0.99675770
full horizon R2 : -0.18319605
full horizon Pearson r : 0.04958130
full horizon QLIKE : 0.60445765
--- Task 5 ---
1 day(s) MAE : 0.23230417
1 day(s) RMSE : 0.23950211
1 day(s) R2 : -15.89067576
1 day(s) Pearson r : nan
1 day(s) QLIKE : 0.02992601
3 day(s) MAE : 0.23234204
3 day(s) RMSE : 0.23953830
3 day(s) R2 : -15.89704844
3 day(s) Pearson r : -0.00775954
3 day(s) QLIKE : 0.02992349
5 day(s) MAE : 0.23243579
5 day(s) RMSE : 0.23962873
5 day(s) R2 : -15.91100721
5 day(s) Pearson r : -0.00982113
5 day(s) QLIKE : 0.02991979
full horizon MAE : 0.23243579
full horizon RMSE : 0.23962873
full horizon R2 : -15.91100721
full horizon Pearson r : -0.00982113
full horizon QLIKE : 0.02991979
--- Task 6 ---
1 day(s) MAE : 1.52324837
1 day(s) RMSE : 1.91833153
1 day(s) R2 : -0.00393335
1 day(s) Pearson r : -0.33402342
1 day(s) QLIKE : 0.08098887
3 day(s) MAE : 1.52220385
3 day(s) RMSE : 1.91895429
3 day(s) R2 : -0.00416266
3 day(s) Pearson r : -0.31603402
3 day(s) QLIKE : 0.08111398
5 day(s) MAE : 1.52298537
5 day(s) RMSE : 1.91911238
5 day(s) R2 : -0.00396300
5 day(s) Pearson r : -0.31823166
5 day(s) QLIKE : 0.08120334
full horizon MAE : 1.52298537
full horizon RMSE : 1.91911238
full horizon R2 : -0.00396300
full horizon Pearson r : -0.31823166
full horizon QLIKE : 0.08120334
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GE/Custom_KAN_H5.pkl
Saved y_true min=0.591065, max=527.531
Saved y_pred min=1.86163, max=14.0646
=== GE | H=5 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.10128934238557
Min value: -0.033788504883313815
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.05628901565783
Min value: -8.143860691767992
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4221295497976101
Min value: -0.03368162544886536
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.0759413314511823
Min value: -68.74229956296557
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4422974929765818
Min value: -0.03368162544886536
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.658989486293699
Min value: -3.0732655627744725
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 292s 523ms/step - loss: 0.7962 - val_loss: 0.9396 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 201ms/step - loss: 0.4696 - val_loss: 0.7999 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 237ms/step - loss: 0.4058 - val_loss: 0.6875 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 236ms/step - loss: 0.3903 - val_loss: 0.8477 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 236ms/step - loss: 0.3913 - val_loss: 0.6796 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 236ms/step - loss: 0.3778 - val_loss: 0.8743 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 237ms/step - loss: 0.3704 - val_loss: 0.9267 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 238ms/step - loss: 0.3645 - val_loss: 0.8520 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 235ms/step - loss: 0.3641 - val_loss: 1.0154 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 229ms/step - loss: 0.3749
Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 236ms/step - loss: 0.3607 - val_loss: 0.8607 - learning_rate: 5.0000e-04
Epoch 10: early stopping
Restoring model weights from the end of the best epoch: 5.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 5
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.98183244
1 day(s) RMSE : 27.44374771
1 day(s) R2 : -0.00128114
1 day(s) Pearson r : 0.02482704
1 day(s) QLIKE : 0.68436680
3 day(s) MAE : 4.53034841
3 day(s) RMSE : 27.51211268
3 day(s) R2 : -0.00633383
3 day(s) Pearson r : -0.00024956
3 day(s) QLIKE : 0.70409702
5 day(s) MAE : 4.52386564
5 day(s) RMSE : 27.50298383
5 day(s) R2 : -0.00572371
5 day(s) Pearson r : -0.00012191
5 day(s) QLIKE : 0.69526227
full horizon MAE : 4.52386564
full horizon RMSE : 27.50298383
full horizon R2 : -0.00572371
full horizon Pearson r : -0.00012191
full horizon QLIKE : 0.69526227
--- Task 2 ---
1 day(s) MAE : 0.04693067
1 day(s) RMSE : 0.05887640
1 day(s) R2 : -1.41483273
1 day(s) Pearson r : -0.04923871
1 day(s) QLIKE : 1.99188176
3 day(s) MAE : 0.05150931
3 day(s) RMSE : 0.06354344
3 day(s) R2 : -1.80569007
3 day(s) Pearson r : -0.00598215
3 day(s) QLIKE : 5.48770888
5 day(s) MAE : 0.05291375
5 day(s) RMSE : 0.06490337
5 day(s) R2 : -1.92023825
5 day(s) Pearson r : -0.00873285
5 day(s) QLIKE : 8.99527059
full horizon MAE : 0.05291375
full horizon RMSE : 0.06490337
full horizon R2 : -1.92023825
full horizon Pearson r : -0.00873285
full horizon QLIKE : 8.99527059
--- Task 3 ---
1 day(s) MAE : 0.74973768
1 day(s) RMSE : 0.77161288
1 day(s) R2 : -16.79974887
1 day(s) Pearson r : -0.01092518
1 day(s) QLIKE : 0.10599550
3 day(s) MAE : 0.75641699
3 day(s) RMSE : 0.77812578
3 day(s) R2 : -17.15653703
3 day(s) Pearson r : 0.00324080
3 day(s) QLIKE : 3.92358131
5 day(s) MAE : 0.75711023
5 day(s) RMSE : 0.77874180
5 day(s) R2 : -17.24206667
5 day(s) Pearson r : 0.00454460
5 day(s) QLIKE : 2.54148353
full horizon MAE : 0.75711023
full horizon RMSE : 0.77874180
full horizon R2 : -17.24206667
full horizon Pearson r : 0.00454460
full horizon QLIKE : 2.54148353
--- Task 4 ---
1 day(s) MAE : 0.56565902
1 day(s) RMSE : 1.00282829
1 day(s) R2 : -0.19554798
1 day(s) Pearson r : -0.01086371
1 day(s) QLIKE : 0.61887970
3 day(s) MAE : 0.61972148
3 day(s) RMSE : 1.06991295
3 day(s) R2 : -0.36198563
3 day(s) Pearson r : -0.00289837
3 day(s) QLIKE : 0.66594440
5 day(s) MAE : 0.65900873
5 day(s) RMSE : 1.10463669
5 day(s) R2 : -0.45317006
5 day(s) Pearson r : -0.00481072
5 day(s) QLIKE : 0.70358296
full horizon MAE : 0.65900873
full horizon RMSE : 1.10463669
full horizon R2 : -0.45317006
full horizon Pearson r : -0.00481072
full horizon QLIKE : 0.70358296
--- Task 5 ---
1 day(s) MAE : 0.23230417
1 day(s) RMSE : 0.23950211
1 day(s) R2 : -15.89067575
1 day(s) Pearson r : nan
1 day(s) QLIKE : 0.02992601
3 day(s) MAE : 0.23234204
3 day(s) RMSE : 0.23953830
3 day(s) R2 : -15.89704832
3 day(s) Pearson r : -0.00001143
3 day(s) QLIKE : 0.02992349
5 day(s) MAE : 0.22426692
5 day(s) RMSE : 0.23228089
5 day(s) R2 : -14.88980780
5 day(s) Pearson r : 0.00183518
5 day(s) QLIKE : 2.88763051
full horizon MAE : 0.22426692
full horizon RMSE : 0.23228089
full horizon R2 : -14.88980780
full horizon Pearson r : 0.00183518
full horizon QLIKE : 2.88763051
--- Task 6 ---
1 day(s) MAE : 1.46581179
1 day(s) RMSE : 1.92306419
1 day(s) R2 : -0.00889301
1 day(s) Pearson r : 0.12480872
1 day(s) QLIKE : 0.08064555
3 day(s) MAE : 1.42406443
3 day(s) RMSE : 1.94094621
3 day(s) R2 : -0.02731070
3 day(s) Pearson r : 0.00306042
3 day(s) QLIKE : 0.08097039
5 day(s) MAE : 1.40532750
5 day(s) RMSE : 1.95584484
5 day(s) R2 : -0.04276319
5 day(s) Pearson r : 0.00544966
5 day(s) QLIKE : 0.08125079
full horizon MAE : 1.40532750
full horizon RMSE : 1.95584484
full horizon R2 : -0.04276319
full horizon Pearson r : 0.00544966
full horizon QLIKE : 0.08125079
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GE/Custom_KAN_LSTM_H5.pkl
Saved y_true min=0.591065, max=527.531
Saved y_pred min=2.98422, max=4.94167
=== GE | H=10 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.10128934238557
Min value: -0.033788504883313815
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.08059546680979
Min value: -8.14578298619116
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4221295497976101
Min value: -0.03368162544886536
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.079343715915981
Min value: -68.74402590450526
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4422974929765818
Min value: -0.03368162544886536
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.670127332275285
Min value: -3.0551258580114347
Epoch 001 | phase=1 | train_loss=5.0877 | val_main=0.895029
Epoch 002 | phase=1 | train_loss=4.9364 | val_main=0.894988
Epoch 003 | phase=1 | train_loss=4.6067 | val_main=0.894868
Epoch 004 | phase=1 | train_loss=4.5586 | val_main=0.894927
Epoch 005 | phase=1 | train_loss=4.6137 | val_main=0.894852
Epoch 006 | phase=1 | train_loss=4.5615 | val_main=0.894955
Epoch 007 | phase=1 | train_loss=4.5752 | val_main=0.894936
Epoch 008 | phase=1 | train_loss=4.6092 | val_main=0.894919
Epoch 009 | phase=1 | train_loss=4.5144 | val_main=0.894881
Epoch 010 | phase=1 | train_loss=4.5313 | val_main=0.894833
Epoch 011 | phase=1 | train_loss=4.5185 | val_main=0.894866
Epoch 012 | phase=1 | train_loss=4.5140 | val_main=0.894888
Epoch 013 | phase=1 | train_loss=4.5308 | val_main=0.894862
Epoch 014 | phase=1 | train_loss=4.5619 | val_main=0.894951
Epoch 015 | phase=1 | train_loss=4.4766 | val_main=0.894956
Epoch 016 | phase=0 | train_loss=5.5831 | val_main=0.890596
Epoch 017 | phase=0 | train_loss=5.5683 | val_main=0.917878
Epoch 018 | phase=0 | train_loss=5.5357 | val_main=0.947994
Epoch 019 | phase=0 | train_loss=5.5790 | val_main=0.917118
Epoch 020 | phase=0 | train_loss=5.5944 | val_main=0.925617
Epoch 021 | phase=0 | train_loss=5.5631 | val_main=0.937675
Epoch 022 | phase=0 | train_loss=5.5438 | val_main=0.940899
Epoch 023 | phase=0 | train_loss=5.5395 | val_main=0.941041
Epoch 024 | phase=0 | train_loss=5.5291 | val_main=0.907607
Epoch 025 | phase=0 | train_loss=5.4958 | val_main=0.753905
Epoch 026 | phase=0 | train_loss=5.3633 | val_main=0.785887
Epoch 027 | phase=0 | train_loss=5.2053 | val_main=0.708950
Epoch 028 | phase=0 | train_loss=5.1785 | val_main=0.828178
Epoch 029 | phase=0 | train_loss=5.0856 | val_main=0.562351
Epoch 030 | phase=0 | train_loss=4.9877 | val_main=0.566214
Epoch 031 | phase=2 | train_loss=0.6909 | val_main=0.790834
Epoch 032 | phase=2 | train_loss=0.6782 | val_main=0.708562
Epoch 033 | phase=2 | train_loss=0.6644 | val_main=0.598293
Epoch 034 | phase=2 | train_loss=0.6319 | val_main=0.547346
Epoch 035 | phase=2 | train_loss=0.6243 | val_main=0.697069
Epoch 036 | phase=2 | train_loss=0.6140 | val_main=0.528479
Epoch 037 | phase=2 | train_loss=0.6491 | val_main=0.665404
Epoch 038 | phase=2 | train_loss=0.6118 | val_main=0.577324
Epoch 039 | phase=2 | train_loss=0.6112 | val_main=0.570082
Epoch 040 | phase=2 | train_loss=0.6017 | val_main=0.668157
Epoch 041 | phase=2 | train_loss=0.6181 | val_main=0.739373
Epoch 042 | phase=2 | train_loss=0.6270 | val_main=0.713053
Epoch 043 | phase=2 | train_loss=0.6081 | val_main=0.527070
Epoch 044 | phase=2 | train_loss=0.6081 | val_main=0.759082
Epoch 045 | phase=2 | train_loss=0.5988 | val_main=0.632046
Epoch 046 | phase=2 | train_loss=0.5930 | val_main=0.584172
Epoch 047 | phase=2 | train_loss=0.6121 | val_main=0.640254
Epoch 048 | phase=2 | train_loss=0.6065 | val_main=0.655024
Epoch 049 | phase=2 | train_loss=0.5968 | val_main=0.627519
Epoch 050 | phase=2 | train_loss=0.5819 | val_main=0.690952
Parameters used in the single-fit model:
input_dim: 60
output_dim: 60
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.25488321
1 day(s) RMSE : 27.57625544
1 day(s) R2 : -0.01097353
1 day(s) Pearson r : 0.03957808
1 day(s) QLIKE : 0.70596503
3 day(s) MAE : 4.32024049
3 day(s) RMSE : 27.60065759
3 day(s) R2 : -0.01282182
3 day(s) Pearson r : 0.02563967
3 day(s) QLIKE : 0.72697813
5 day(s) MAE : 4.36985180
5 day(s) RMSE : 27.61791981
5 day(s) R2 : -0.01414719
5 day(s) Pearson r : 0.01598163
5 day(s) QLIKE : 0.74136460
10 day(s) MAE : 4.45905872
10 day(s) RMSE : 27.63456302
10 day(s) R2 : -0.01522085
10 day(s) Pearson r : 0.00582403
10 day(s) QLIKE : 0.76284899
full horizon MAE : 4.45905872
full horizon RMSE : 27.63456302
full horizon R2 : -0.01522085
full horizon Pearson r : 0.00582403
full horizon QLIKE : 0.76284899
--- Task 2 ---
1 day(s) MAE : 0.05073575
1 day(s) RMSE : 0.06284417
1 day(s) R2 : -1.75127794
1 day(s) Pearson r : -0.13408188
1 day(s) QLIKE : 2.01134565
3 day(s) MAE : 0.05101306
3 day(s) RMSE : 0.06307197
3 day(s) R2 : -1.76421028
3 day(s) Pearson r : -0.13358261
3 day(s) QLIKE : 1.95233694
5 day(s) MAE : 0.05134241
5 day(s) RMSE : 0.06337672
5 day(s) R2 : -1.78447474
5 day(s) Pearson r : -0.13323284
5 day(s) QLIKE : 1.89536137
10 day(s) MAE : 0.05228562
10 day(s) RMSE : 0.06429802
10 day(s) R2 : -1.84491257
10 day(s) Pearson r : -0.10299095
10 day(s) QLIKE : 1.76850636
full horizon MAE : 0.05228562
full horizon RMSE : 0.06429802
full horizon R2 : -1.84491257
full horizon Pearson r : -0.10299095
full horizon QLIKE : 1.76850636
--- Task 3 ---
1 day(s) MAE : 0.75196076
1 day(s) RMSE : 0.77351383
1 day(s) R2 : -16.88755988
1 day(s) Pearson r : 0.14773393
1 day(s) QLIKE : 0.31000201
3 day(s) MAE : 0.75111297
3 day(s) RMSE : 0.77269566
3 day(s) R2 : -16.90401190
3 day(s) Pearson r : 0.12388070
3 day(s) QLIKE : 0.30589960
5 day(s) MAE : 0.75026192
5 day(s) RMSE : 0.77180721
5 day(s) R2 : -16.91862692
5 day(s) Pearson r : 0.12179292
5 day(s) QLIKE : 0.30398838
10 day(s) MAE : 0.74843740
10 day(s) RMSE : 0.76988052
10 day(s) R2 : -16.98854503
10 day(s) Pearson r : 0.11026435
10 day(s) QLIKE : 0.30442294
full horizon MAE : 0.74843740
full horizon RMSE : 0.76988052
full horizon R2 : -16.98854503
full horizon Pearson r : 0.11026435
full horizon QLIKE : 0.30442294
--- Task 4 ---
1 day(s) MAE : 0.56484725
1 day(s) RMSE : 0.99955626
1 day(s) R2 : -0.18775904
1 day(s) Pearson r : 0.05570944
1 day(s) QLIKE : 0.61827089
3 day(s) MAE : 0.56614034
3 day(s) RMSE : 0.99960804
3 day(s) R2 : -0.18887206
3 day(s) Pearson r : 0.05400108
3 day(s) QLIKE : 0.61078232
5 day(s) MAE : 0.56750615
5 day(s) RMSE : 1.00029456
5 day(s) R2 : -0.19160779
5 day(s) Pearson r : 0.05837430
5 day(s) QLIKE : 0.60406199
10 day(s) MAE : 0.57087809
10 day(s) RMSE : 1.00227527
10 day(s) R2 : -0.19948120
10 day(s) Pearson r : 0.06760120
10 day(s) QLIKE : 0.58268537
full horizon MAE : 0.57087809
full horizon RMSE : 1.00227527
full horizon R2 : -0.19948120
full horizon Pearson r : 0.06760120
full horizon QLIKE : 0.58268537
--- Task 5 ---
1 day(s) MAE : 0.23230417
1 day(s) RMSE : 0.23950211
1 day(s) R2 : -15.89067576
1 day(s) Pearson r : -0.02201168
1 day(s) QLIKE : 0.02992601
3 day(s) MAE : 0.23234204
3 day(s) RMSE : 0.23953830
3 day(s) R2 : -15.89704844
3 day(s) Pearson r : -0.02923505
3 day(s) QLIKE : 0.02992349
5 day(s) MAE : 0.23243579
5 day(s) RMSE : 0.23962873
5 day(s) R2 : -15.91100721
5 day(s) Pearson r : -0.02391884
5 day(s) QLIKE : 0.02991979
10 day(s) MAE : 0.23282017
10 day(s) RMSE : 0.24002700
10 day(s) R2 : -15.90655724
10 day(s) Pearson r : -0.01406606
10 day(s) QLIKE : 0.03001085
full horizon MAE : 0.23282017
full horizon RMSE : 0.24002700
full horizon R2 : -15.90655724
full horizon Pearson r : -0.01406606
full horizon QLIKE : 0.03001085
--- Task 6 ---
1 day(s) MAE : 1.70010573
1 day(s) RMSE : 2.11457338
1 day(s) R2 : -0.21984058
1 day(s) Pearson r : -0.41616104
1 day(s) QLIKE : 0.10155590
3 day(s) MAE : 1.69780241
3 day(s) RMSE : 2.11245476
3 day(s) R2 : -0.21688530
3 day(s) Pearson r : -0.41118193
3 day(s) QLIKE : 0.10145364
5 day(s) MAE : 1.69802696
5 day(s) RMSE : 2.11329518
5 day(s) R2 : -0.21741100
5 day(s) Pearson r : -0.40822839
5 day(s) QLIKE : 0.10168460
10 day(s) MAE : 1.70049947
10 day(s) RMSE : 2.12017276
10 day(s) R2 : -0.22425648
10 day(s) Pearson r : -0.40632454
10 day(s) QLIKE : 0.10285897
full horizon MAE : 1.70049947
full horizon RMSE : 2.12017276
full horizon R2 : -0.22425648
full horizon Pearson r : -0.40632454
full horizon QLIKE : 0.10285897
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GE/Custom_KAN_H10.pkl
Saved y_true min=0.591065, max=527.531
Saved y_pred min=1.19596, max=16.8004
=== GE | H=10 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.10128934238557
Min value: -0.033788504883313815
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.08059546680979
Min value: -8.14578298619116
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4221295497976101
Min value: -0.03368162544886536
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.079343715915981
Min value: -68.74402590450526
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4422974929765818
Min value: -0.03368162544886536
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.670127332275285
Min value: -3.0551258580114347
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 152s 450ms/step - loss: 0.7521 - val_loss: 1.0736 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 238ms/step - loss: 0.3828 - val_loss: 0.7090 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 307ms/step - loss: 0.3246 - val_loss: 0.6760 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 304ms/step - loss: 0.3116 - val_loss: 0.8499 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 303ms/step - loss: 0.2973 - val_loss: 0.8219 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 306ms/step - loss: 0.2942 - val_loss: 0.8486 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 302ms/step - loss: 0.2863 - val_loss: 1.1835 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 298ms/step - loss: 0.2806
Epoch 8: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 308ms/step - loss: 0.2747 - val_loss: 0.6975 - learning_rate: 5.0000e-04
Epoch 8: early stopping
Restoring model weights from the end of the best epoch: 3.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 10
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 5.05041420
1 day(s) RMSE : 27.43993195
1 day(s) R2 : -0.00100272
1 day(s) Pearson r : -0.04849518
1 day(s) QLIKE : 0.68437663
3 day(s) MAE : 4.58339733
3 day(s) RMSE : 27.49517937
3 day(s) R2 : -0.00509544
3 day(s) Pearson r : -0.00041208
3 day(s) QLIKE : 0.69505950
5 day(s) MAE : 4.47057793
5 day(s) RMSE : 27.51194401
5 day(s) R2 : -0.00637913
5 day(s) Pearson r : -0.00049049
5 day(s) QLIKE : 0.69167173
10 day(s) MAE : 4.46564283
10 day(s) RMSE : 27.51220414
10 day(s) R2 : -0.00625047
10 day(s) Pearson r : -0.00027229
10 day(s) QLIKE : 0.68389297
full horizon MAE : 4.46564283
full horizon RMSE : 27.51220414
full horizon R2 : -0.00625047
full horizon Pearson r : -0.00027229
full horizon QLIKE : 0.68389297
--- Task 2 ---
1 day(s) MAE : 0.04987835
1 day(s) RMSE : 0.06183439
1 day(s) R2 : -1.66357355
1 day(s) Pearson r : 0.05143913
1 day(s) QLIKE : 1.99186828
3 day(s) MAE : 0.05259499
3 day(s) RMSE : 0.06457532
3 day(s) R2 : -1.89755267
3 day(s) Pearson r : -0.00599315
3 day(s) QLIKE : 3.17186284
5 day(s) MAE : 0.05356516
5 day(s) RMSE : 0.06551159
5 day(s) R2 : -1.97522677
5 day(s) Pearson r : -0.00891282
5 day(s) QLIKE : 9.06000497
10 day(s) MAE : 0.05483590
10 day(s) RMSE : 0.06671752
10 day(s) R2 : -2.06304611
10 day(s) Pearson r : -0.01392376
10 day(s) QLIKE : 7.10708631
full horizon MAE : 0.05483590
full horizon RMSE : 0.06671752
full horizon R2 : -2.06304611
full horizon Pearson r : -0.01392376
full horizon QLIKE : 7.10708631
--- Task 3 ---
1 day(s) MAE : 0.75627812
1 day(s) RMSE : 0.77803206
1 day(s) R2 : -17.09713894
1 day(s) Pearson r : -0.01507400
1 day(s) QLIKE : 0.10599631
3 day(s) MAE : 0.75414424
3 day(s) RMSE : 0.77588144
3 day(s) R2 : -17.05195044
3 day(s) Pearson r : -0.00294819
3 day(s) QLIKE : 0.11993285
5 day(s) MAE : 0.75544674
5 day(s) RMSE : 0.77710176
5 day(s) R2 : -17.16531164
5 day(s) Pearson r : 0.00481108
5 day(s) QLIKE : 1.76013472
10 day(s) MAE : 0.75546219
10 day(s) RMSE : 0.77694315
10 day(s) R2 : -17.32010061
10 day(s) Pearson r : 0.01005592
10 day(s) QLIKE : 3.61656013
full horizon MAE : 0.75546219
full horizon RMSE : 0.77694315
full horizon R2 : -17.32010061
full horizon Pearson r : 0.01005592
full horizon QLIKE : 3.61656013
--- Task 4 ---
1 day(s) MAE : 0.56371423
1 day(s) RMSE : 0.98352842
1 day(s) R2 : -0.14997310
1 day(s) Pearson r : -0.01484540
1 day(s) QLIKE : 0.61887985
3 day(s) MAE : 0.57005062
3 day(s) RMSE : 0.99492273
3 day(s) R2 : -0.17775333
3 day(s) Pearson r : -0.00213879
3 day(s) QLIKE : 0.62440202
5 day(s) MAE : 0.60581183
5 day(s) RMSE : 1.04285285
5 day(s) R2 : -0.29516048
5 day(s) Pearson r : -0.00494130
5 day(s) QLIKE : 0.71734534
10 day(s) MAE : 0.68670954
10 day(s) RMSE : 1.11922678
10 day(s) R2 : -0.49573825
10 day(s) Pearson r : -0.00964162
10 day(s) QLIKE : 0.87395399
full horizon MAE : 0.68670954
full horizon RMSE : 1.11922678
full horizon R2 : -0.49573825
full horizon Pearson r : -0.00964162
full horizon QLIKE : 0.87395399
--- Task 5 ---
1 day(s) MAE : 0.23230417
1 day(s) RMSE : 0.23950211
1 day(s) R2 : -15.89067575
1 day(s) Pearson r : nan
1 day(s) QLIKE : 0.02992601
3 day(s) MAE : 0.23234204
3 day(s) RMSE : 0.23953830
3 day(s) R2 : -15.89704844
3 day(s) Pearson r : -0.00083126
3 day(s) QLIKE : 0.02992349
5 day(s) MAE : 0.23243579
5 day(s) RMSE : 0.23962873
5 day(s) R2 : -15.91100721
5 day(s) Pearson r : -0.00180576
5 day(s) QLIKE : 0.02991979
10 day(s) MAE : 0.23282017
10 day(s) RMSE : 0.24002700
10 day(s) R2 : -15.90655727
10 day(s) Pearson r : -0.00642982
10 day(s) QLIKE : 0.03001085
full horizon MAE : 0.23282017
full horizon RMSE : 0.24002700
full horizon R2 : -15.90655727
full horizon Pearson r : -0.00642982
full horizon QLIKE : 0.03001085
--- Task 6 ---
1 day(s) MAE : 1.47661484
1 day(s) RMSE : 1.92056120
1 day(s) R2 : -0.00626845
1 day(s) Pearson r : 0.10890396
1 day(s) QLIKE : 0.08064654
3 day(s) MAE : 1.41370734
3 day(s) RMSE : 1.99573257
3 day(s) R2 : -0.08612423
3 day(s) Pearson r : 0.00279820
3 day(s) QLIKE : 0.08297267
5 day(s) MAE : 1.41844046
5 day(s) RMSE : 2.07746592
5 day(s) R2 : -0.17648043
5 day(s) Pearson r : 0.00489015
5 day(s) QLIKE : 0.08531626
10 day(s) MAE : 1.46650282
10 day(s) RMSE : 2.22526901
10 day(s) R2 : -0.34863663
10 day(s) Pearson r : 0.01021813
10 day(s) QLIKE : 0.08859203
full horizon MAE : 1.46650282
full horizon RMSE : 2.22526901
full horizon R2 : -0.34863663
full horizon Pearson r : 0.01021813
full horizon QLIKE : 0.08859203
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GE/Custom_KAN_LSTM_H10.pkl
Saved y_true min=0.591065, max=527.531
Saved y_pred min=3.38414, max=5.05276
=== GE | H=20 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.10128934238557
Min value: -0.033788504883313815
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.120224031597433
Min value: -8.143184816833994
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4221295497976101
Min value: -0.03368162544886536
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.084344850046558
Min value: -68.75078734214202
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4422974929765818
Min value: -0.03368162544886536
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.687942200751508
Min value: -3.0083215695863115
Epoch 001 | phase=1 | train_loss=5.0755 | val_main=0.939962
Epoch 002 | phase=1 | train_loss=4.9649 | val_main=0.939907
Epoch 003 | phase=1 | train_loss=4.6755 | val_main=0.939745
Epoch 004 | phase=1 | train_loss=4.5695 | val_main=0.939803
Epoch 005 | phase=1 | train_loss=4.6302 | val_main=0.939699
Epoch 006 | phase=1 | train_loss=4.5988 | val_main=0.939801
Epoch 007 | phase=1 | train_loss=4.5910 | val_main=0.939785
Epoch 008 | phase=1 | train_loss=4.6247 | val_main=0.939737
Epoch 009 | phase=1 | train_loss=4.5317 | val_main=0.939775
Epoch 010 | phase=1 | train_loss=4.5970 | val_main=0.939731
Epoch 011 | phase=1 | train_loss=4.5370 | val_main=0.939755
Epoch 012 | phase=1 | train_loss=4.5334 | val_main=0.939745
Epoch 013 | phase=1 | train_loss=4.5314 | val_main=0.939720
Epoch 014 | phase=1 | train_loss=4.5718 | val_main=0.939804
Epoch 015 | phase=1 | train_loss=4.4952 | val_main=0.939833
Epoch 016 | phase=0 | train_loss=5.5749 | val_main=0.936646
Epoch 017 | phase=0 | train_loss=5.5701 | val_main=0.956292
Epoch 018 | phase=0 | train_loss=5.5399 | val_main=0.989979
Epoch 019 | phase=0 | train_loss=5.5788 | val_main=0.972654
Epoch 020 | phase=0 | train_loss=5.5930 | val_main=0.967430
Epoch 021 | phase=0 | train_loss=5.6011 | val_main=0.979676
Epoch 022 | phase=0 | train_loss=5.5306 | val_main=0.985558
Epoch 023 | phase=0 | train_loss=5.5803 | val_main=0.985358
Epoch 024 | phase=0 | train_loss=5.5319 | val_main=0.995213
Epoch 025 | phase=0 | train_loss=5.4708 | val_main=0.560757
Epoch 026 | phase=0 | train_loss=5.3457 | val_main=1.027384
Epoch 027 | phase=0 | train_loss=5.2485 | val_main=0.630241
Epoch 028 | phase=0 | train_loss=5.1040 | val_main=0.681628
Epoch 029 | phase=0 | train_loss=4.9860 | val_main=0.580018
Epoch 030 | phase=0 | train_loss=4.8271 | val_main=0.528220
Epoch 031 | phase=2 | train_loss=0.7971 | val_main=0.627772
Epoch 032 | phase=2 | train_loss=0.7477 | val_main=0.609650
Epoch 033 | phase=2 | train_loss=0.7235 | val_main=0.555301
Epoch 034 | phase=2 | train_loss=0.6994 | val_main=0.624855
Epoch 035 | phase=2 | train_loss=0.6800 | val_main=0.615603
Epoch 036 | phase=2 | train_loss=0.6684 | val_main=0.569577
Epoch 037 | phase=2 | train_loss=0.6766 | val_main=0.595442
Epoch 038 | phase=2 | train_loss=0.6605 | val_main=0.616834
Epoch 039 | phase=2 | train_loss=0.6520 | val_main=0.561540
Epoch 040 | phase=2 | train_loss=0.6491 | val_main=0.607833
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 120
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.46386438
1 day(s) RMSE : 27.72736170
1 day(s) R2 : -0.02208330
1 day(s) Pearson r : 0.01269703
1 day(s) QLIKE : 0.76380712
3 day(s) MAE : 4.42918499
3 day(s) RMSE : 27.72686957
3 day(s) R2 : -0.02210584
3 day(s) Pearson r : 0.00371191
3 day(s) QLIKE : 0.76712094
5 day(s) MAE : 4.42595674
5 day(s) RMSE : 27.73136617
5 day(s) R2 : -0.02249594
5 day(s) Pearson r : -0.00346349
5 day(s) QLIKE : 0.77220001
10 day(s) MAE : 4.45682796
10 day(s) RMSE : 27.73724845
10 day(s) R2 : -0.02277965
10 day(s) Pearson r : -0.01061715
10 day(s) QLIKE : 0.77858966
20 day(s) MAE : 4.49279193
20 day(s) RMSE : 27.74169292
20 day(s) R2 : -0.02305331
20 day(s) Pearson r : -0.02126558
20 day(s) QLIKE : 0.78356202
full horizon MAE : 4.49279193
full horizon RMSE : 27.74169292
full horizon R2 : -0.02305331
full horizon Pearson r : -0.02126558
full horizon QLIKE : 0.78356202
--- Task 2 ---
1 day(s) MAE : 0.09036602
1 day(s) RMSE : 0.11157310
1 day(s) R2 : -7.67208472
1 day(s) Pearson r : -0.17976365
1 day(s) QLIKE : 3.48215153
3 day(s) MAE : 0.07943692
3 day(s) RMSE : 0.09844805
3 day(s) R2 : -5.73461127
3 day(s) Pearson r : -0.12591352
3 day(s) QLIKE : 3.44228108
5 day(s) MAE : 0.07539323
5 day(s) RMSE : 0.09338653
5 day(s) R2 : -5.04577912
5 day(s) Pearson r : -0.11822258
5 day(s) QLIKE : 3.37180130
10 day(s) MAE : 0.06939412
10 day(s) RMSE : 0.08642735
10 day(s) R2 : -4.14015366
10 day(s) Pearson r : -0.10445153
10 day(s) QLIKE : 3.25848366
20 day(s) MAE : 0.06294539
20 day(s) RMSE : 0.07966988
20 day(s) R2 : -3.31558704
20 day(s) Pearson r : -0.09020908
20 day(s) QLIKE : 3.00967463
full horizon MAE : 0.06294539
full horizon RMSE : 0.07966988
full horizon R2 : -3.31558704
full horizon Pearson r : -0.09020908
full horizon QLIKE : 3.00967463
--- Task 3 ---
1 day(s) MAE : 0.75894122
1 day(s) RMSE : 0.78055904
1 day(s) R2 : -17.21488577
1 day(s) Pearson r : 0.14825552
1 day(s) QLIKE : 0.31420597
3 day(s) MAE : 0.75807803
3 day(s) RMSE : 0.77966139
3 day(s) R2 : -17.22827082
3 day(s) Pearson r : 0.13872197
3 day(s) QLIKE : 0.30860630
5 day(s) MAE : 0.75726564
5 day(s) RMSE : 0.77880504
5 day(s) R2 : -17.24502948
5 day(s) Pearson r : 0.13957058
5 day(s) QLIKE : 0.30468425
10 day(s) MAE : 0.75538832
10 day(s) RMSE : 0.77679596
10 day(s) R2 : -17.31315986
10 day(s) Pearson r : 0.13312134
10 day(s) QLIKE : 0.31418188
20 day(s) MAE : 0.75154994
20 day(s) RMSE : 0.77267058
20 day(s) R2 : -17.46593661
20 day(s) Pearson r : 0.12481308
20 day(s) QLIKE : 0.31876779
full horizon MAE : 0.75154994
full horizon RMSE : 0.77267058
full horizon R2 : -17.46593661
full horizon Pearson r : 0.12481308
full horizon QLIKE : 0.31876779
--- Task 4 ---
1 day(s) MAE : 0.56446928
1 day(s) RMSE : 0.99771069
1 day(s) R2 : -0.18337694
1 day(s) Pearson r : 0.06088134
1 day(s) QLIKE : 0.61807555
3 day(s) MAE : 0.56584289
3 day(s) RMSE : 0.99805735
3 day(s) R2 : -0.18518632
3 day(s) Pearson r : 0.05758842
3 day(s) QLIKE : 0.61073857
5 day(s) MAE : 0.56721899
5 day(s) RMSE : 0.99886209
5 day(s) R2 : -0.18819735
5 day(s) Pearson r : 0.06287411
5 day(s) QLIKE : 0.60402805
10 day(s) MAE : 0.57051066
10 day(s) RMSE : 1.00076803
10 day(s) R2 : -0.19587629
10 day(s) Pearson r : 0.07028606
10 day(s) QLIKE : 0.58260911
20 day(s) MAE : 0.57658227
20 day(s) RMSE : 1.00319141
20 day(s) R2 : -0.20887524
20 day(s) Pearson r : 0.07462472
20 day(s) QLIKE : 0.54254482
full horizon MAE : 0.57658227
full horizon RMSE : 1.00319141
full horizon R2 : -0.20887524
full horizon Pearson r : 0.07462472
full horizon QLIKE : 0.54254482
--- Task 5 ---
1 day(s) MAE : 0.23230417
1 day(s) RMSE : 0.23950211
1 day(s) R2 : -15.89067576
1 day(s) Pearson r : nan
1 day(s) QLIKE : 0.02992601
3 day(s) MAE : 0.23234204
3 day(s) RMSE : 0.23953830
3 day(s) R2 : -15.89704844
3 day(s) Pearson r : -0.01662874
3 day(s) QLIKE : 0.02992349
5 day(s) MAE : 0.23243579
5 day(s) RMSE : 0.23962873
5 day(s) R2 : -15.91100721
5 day(s) Pearson r : -0.01824617
5 day(s) QLIKE : 0.02991979
10 day(s) MAE : 0.23282017
10 day(s) RMSE : 0.24002700
10 day(s) R2 : -15.90655724
10 day(s) Pearson r : -0.01260510
10 day(s) QLIKE : 0.03001085
20 day(s) MAE : 0.23367059
20 day(s) RMSE : 0.24090050
20 day(s) R2 : -15.91381390
20 day(s) Pearson r : 0.02276025
20 day(s) QLIKE : 0.03017665
full horizon MAE : 0.23367059
full horizon RMSE : 0.24090050
full horizon R2 : -15.91381390
full horizon Pearson r : 0.02276025
full horizon QLIKE : 0.03017665
--- Task 6 ---
1 day(s) MAE : 1.48433475
1 day(s) RMSE : 2.31693234
1 day(s) R2 : -0.46448273
1 day(s) Pearson r : -0.21205263
1 day(s) QLIKE : 0.09742487
3 day(s) MAE : 1.47858894
3 day(s) RMSE : 2.31481917
3 day(s) R2 : -0.46119760
3 day(s) Pearson r : -0.20553650
3 day(s) QLIKE : 0.09721400
5 day(s) MAE : 1.47795250
5 day(s) RMSE : 2.32336384
5 day(s) R2 : -0.47146977
5 day(s) Pearson r : -0.20734651
5 day(s) QLIKE : 0.09789629
10 day(s) MAE : 1.47010313
10 day(s) RMSE : 2.32876546
10 day(s) R2 : -0.47700312
10 day(s) Pearson r : -0.20126904
10 day(s) QLIKE : 0.09848692
20 day(s) MAE : 1.45007564
20 day(s) RMSE : 2.32277178
20 day(s) R2 : -0.46916804
20 day(s) Pearson r : -0.19246170
20 day(s) QLIKE : 0.09901890
full horizon MAE : 1.45007564
full horizon RMSE : 2.32277178
full horizon R2 : -0.46916804
full horizon Pearson r : -0.19246170
full horizon QLIKE : 0.09901890
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GE/Custom_KAN_H20.pkl
Saved y_true min=0.591065, max=527.531
Saved y_pred min=1.12347, max=7.96721
=== GE | H=20 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 38.10128934238557
Min value: -0.033788504883313815
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.120224031597433
Min value: -8.143184816833994
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4221295497976101
Min value: -0.03368162544886536
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.084344850046558
Min value: -68.75078734214202
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 0.4422974929765818
Min value: -0.03368162544886536
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.687942200751508
Min value: -3.0083215695863115
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 120s 512ms/step - loss: 0.7411 - val_loss: 0.9876 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 318ms/step - loss: 0.3435 - val_loss: 1.0415 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 448ms/step - loss: 0.2819 - val_loss: 0.9291 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 448ms/step - loss: 0.2595 - val_loss: 0.7508 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 448ms/step - loss: 0.2480 - val_loss: 1.1599 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 447ms/step - loss: 0.2364 - val_loss: 0.6969 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 450ms/step - loss: 0.2232 - val_loss: 0.6964 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 20s 445ms/step - loss: 0.2151 - val_loss: 1.3415 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 450ms/step - loss: 0.2117 - val_loss: 0.5845 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 449ms/step - loss: 0.2281 - val_loss: 1.2592 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 20s 444ms/step - loss: 0.2061 - val_loss: 0.5329 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=12 tf_ratio=0.421 -> TF=ON
Epoch 12/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 448ms/step - loss: 0.1961 - val_loss: 0.5605 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=13 tf_ratio=0.368 -> TF=ON
Epoch 13/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 449ms/step - loss: 0.1883 - val_loss: 0.6122 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=14 tf_ratio=0.316 -> TF=ON
Epoch 14/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 465ms/step - loss: 0.1879 - val_loss: 0.6427 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=15 tf_ratio=0.263 -> TF=ON
Epoch 15/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 20s 445ms/step - loss: 0.1856 - val_loss: 0.6187 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=16 tf_ratio=0.211 -> TF=ON
Epoch 16/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 437ms/step - loss: 0.1891
Epoch 16: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 451ms/step - loss: 0.1876 - val_loss: 1.1144 - learning_rate: 5.0000e-04
Epoch 16: early stopping
Restoring model weights from the end of the best epoch: 11.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 20
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 5.19848697
1 day(s) RMSE : 27.43351981
1 day(s) R2 : -0.00053495
1 day(s) Pearson r : -0.05882689
1 day(s) QLIKE : 0.68447001
3 day(s) MAE : 4.53457541
3 day(s) RMSE : 27.53339268
3 day(s) R2 : -0.00789119
3 day(s) Pearson r : -0.00034660
3 day(s) QLIKE : 0.71691761
5 day(s) MAE : 4.49956069
5 day(s) RMSE : 27.52036699
5 day(s) R2 : -0.00699544
5 day(s) Pearson r : -0.00021765
5 day(s) QLIKE : 0.70348857
10 day(s) MAE : 4.39247452
10 day(s) RMSE : 27.54831759
10 day(s) R2 : -0.00889388
10 day(s) Pearson r : -0.00090567
10 day(s) QLIKE : 0.69463188
20 day(s) MAE : 4.31833896
20 day(s) RMSE : 27.57918393
20 day(s) R2 : -0.01110246
20 day(s) Pearson r : -0.00219196
20 day(s) QLIKE : 0.68773371
full horizon MAE : 4.31833896
full horizon RMSE : 27.57918393
full horizon R2 : -0.01110246
full horizon Pearson r : -0.00219196
full horizon QLIKE : 0.68773371
--- Task 2 ---
1 day(s) MAE : 0.05080419
1 day(s) RMSE : 0.06277969
1 day(s) R2 : -1.74563534
1 day(s) Pearson r : -0.04271951
1 day(s) QLIKE : 1.99194715
3 day(s) MAE : 0.05320894
3 day(s) RMSE : 0.06517598
3 day(s) R2 : -1.95170810
3 day(s) Pearson r : -0.00565711
3 day(s) QLIKE : 8.04835129
5 day(s) MAE : 0.05393353
5 day(s) RMSE : 0.06586753
5 day(s) R2 : -2.00764465
5 day(s) Pearson r : -0.00794331
5 day(s) QLIKE : 7.26690474
10 day(s) MAE : 0.05502009
10 day(s) RMSE : 0.06689252
10 day(s) R2 : -2.07913568
10 day(s) Pearson r : -0.01203480
10 day(s) QLIKE : 5.19338633
20 day(s) MAE : 0.05670625
20 day(s) RMSE : 0.06843971
20 day(s) R2 : -2.18469501
20 day(s) Pearson r : -0.01744371
20 day(s) QLIKE : 3.30555307
full horizon MAE : 0.05670625
full horizon RMSE : 0.06843971
full horizon R2 : -2.18469501
full horizon Pearson r : -0.01744371
full horizon QLIKE : 3.30555307
--- Task 3 ---
1 day(s) MAE : 0.75621302
1 day(s) RMSE : 0.77796814
1 day(s) R2 : -17.09416557
1 day(s) Pearson r : 0.01542727
1 day(s) QLIKE : 0.10599362
3 day(s) MAE : 0.75858821
3 day(s) RMSE : 0.78024485
3 day(s) R2 : -17.25556292
3 day(s) Pearson r : 0.00324093
3 day(s) QLIKE : 2.81606161
5 day(s) MAE : 0.75841297
5 day(s) RMSE : 0.78001293
5 day(s) R2 : -17.30166768
5 day(s) Pearson r : 0.00453717
5 day(s) QLIKE : 1.75185913
10 day(s) MAE : 0.75694531
10 day(s) RMSE : 0.77840039
10 day(s) R2 : -17.38888767
10 day(s) Pearson r : 0.00661807
10 day(s) QLIKE : 0.93461318
20 day(s) MAE : 0.75331976
20 day(s) RMSE : 0.77447749
20 day(s) R2 : -17.55240408
20 day(s) Pearson r : 0.00953152
20 day(s) QLIKE : 0.52083293
full horizon MAE : 0.75331976
full horizon RMSE : 0.77447749
full horizon R2 : -17.55240408
full horizon Pearson r : 0.00953152
full horizon QLIKE : 0.52083293
--- Task 4 ---
1 day(s) MAE : 0.56543870
1 day(s) RMSE : 0.97057041
1 day(s) R2 : -0.11987087
1 day(s) Pearson r : -0.02661751
1 day(s) QLIKE : 0.61888085
3 day(s) MAE : 0.58044323
3 day(s) RMSE : 0.95392514
3 day(s) R2 : -0.08269024
3 day(s) Pearson r : 0.00166384
3 day(s) QLIKE : 0.61519239
5 day(s) MAE : 0.58990335
5 day(s) RMSE : 0.94718124
5 day(s) R2 : -0.06842413
5 day(s) Pearson r : 0.00413751
5 day(s) QLIKE : 0.60964596
10 day(s) MAE : 0.58582276
10 day(s) RMSE : 0.96120324
10 day(s) R2 : -0.10318885
10 day(s) Pearson r : -0.00643573
10 day(s) QLIKE : 0.59644222
20 day(s) MAE : 0.59895996
20 day(s) RMSE : 1.01103438
20 day(s) R2 : -0.22785115
20 day(s) Pearson r : -0.01560695
20 day(s) QLIKE : 0.59253924
full horizon MAE : 0.59895996
full horizon RMSE : 1.01103438
full horizon R2 : -0.22785115
full horizon Pearson r : -0.01560695
full horizon QLIKE : 0.59253924
--- Task 5 ---
1 day(s) MAE : 0.23230417
1 day(s) RMSE : 0.23950211
1 day(s) R2 : -15.89067576
1 day(s) Pearson r : nan
1 day(s) QLIKE : 0.02992601
3 day(s) MAE : 0.23234204
3 day(s) RMSE : 0.23953830
3 day(s) R2 : -15.89704847
3 day(s) Pearson r : -0.00058495
3 day(s) QLIKE : 0.02992349
5 day(s) MAE : 0.23243579
5 day(s) RMSE : 0.23962873
5 day(s) R2 : -15.91100724
5 day(s) Pearson r : -0.00142483
5 day(s) QLIKE : 0.02991979
10 day(s) MAE : 0.23282017
10 day(s) RMSE : 0.24002700
10 day(s) R2 : -15.90655728
10 day(s) Pearson r : -0.00366818
10 day(s) QLIKE : 0.03001085
20 day(s) MAE : 0.23367059
20 day(s) RMSE : 0.24090050
20 day(s) R2 : -15.91381394
20 day(s) Pearson r : -0.00660574
20 day(s) QLIKE : 0.03017665
full horizon MAE : 0.23367059
full horizon RMSE : 0.24090050
full horizon R2 : -15.91381394
full horizon Pearson r : -0.00660574
full horizon QLIKE : 0.03017665
--- Task 6 ---
1 day(s) MAE : 1.50310610
1 day(s) RMSE : 1.91623454
1 day(s) R2 : -0.00173968
1 day(s) Pearson r : 0.09998421
1 day(s) QLIKE : 0.08064532
3 day(s) MAE : 1.43997722
3 day(s) RMSE : 1.93631179
3 day(s) R2 : -0.02241071
3 day(s) Pearson r : 0.00235037
3 day(s) QLIKE : 0.08120932
5 day(s) MAE : 1.45198560
5 day(s) RMSE : 1.93055630
5 day(s) R2 : -0.01597223
5 day(s) Pearson r : -0.00044356
5 day(s) QLIKE : 0.08123932
10 day(s) MAE : 1.42779203
10 day(s) RMSE : 1.93966475
10 day(s) R2 : -0.02466820
10 day(s) Pearson r : 0.00721687
10 day(s) QLIKE : 0.08165337
20 day(s) MAE : 1.38456770
20 day(s) RMSE : 1.98541610
20 day(s) R2 : -0.07339969
20 day(s) Pearson r : 0.02086458
20 day(s) QLIKE : 0.08324364
full horizon MAE : 1.38456770
full horizon RMSE : 1.98541610
full horizon R2 : -0.07339969
full horizon Pearson r : 0.02086458
full horizon QLIKE : 0.08324364
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GE/Custom_KAN_LSTM_H20.pkl
Saved y_true min=0.591065, max=527.531
Saved y_pred min=2.48686, max=5.2922
=== BAC | H=1 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.142355974179488
Min value: -0.42151023550938554
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.107742786547419
Min value: -11.49125811580691
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.800014951166767
Min value: -0.36472825885137616
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.2542888447212457
Min value: -1.9794140488614782
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.111754968787256
Min value: -0.38915813676977
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.207108669727662
Min value: -11.49125811580691
Epoch 001 | phase=1 | train_loss=4.9526 | val_main=0.561224
Epoch 002 | phase=1 | train_loss=4.8670 | val_main=0.561167
Epoch 003 | phase=1 | train_loss=5.0016 | val_main=0.561091
Epoch 004 | phase=1 | train_loss=4.6199 | val_main=0.561106
Epoch 005 | phase=1 | train_loss=4.4244 | val_main=0.561048
Epoch 006 | phase=1 | train_loss=4.5209 | val_main=0.561019
Epoch 007 | phase=1 | train_loss=4.3559 | val_main=0.561027
Epoch 008 | phase=1 | train_loss=4.1812 | val_main=0.560951
Epoch 009 | phase=1 | train_loss=4.4389 | val_main=0.560660
Epoch 010 | phase=1 | train_loss=4.0400 | val_main=0.560935
Epoch 011 | phase=1 | train_loss=3.9436 | val_main=0.560706
Epoch 012 | phase=1 | train_loss=3.9575 | val_main=0.560858
Epoch 013 | phase=1 | train_loss=3.8274 | val_main=0.560862
Epoch 014 | phase=1 | train_loss=3.7241 | val_main=0.560823
Epoch 015 | phase=1 | train_loss=3.5603 | val_main=0.560818
Epoch 016 | phase=0 | train_loss=4.4668 | val_main=0.530514
Epoch 017 | phase=0 | train_loss=4.2924 | val_main=0.472941
Epoch 018 | phase=0 | train_loss=4.1999 | val_main=0.483452
Epoch 019 | phase=0 | train_loss=3.7790 | val_main=0.478276
Epoch 020 | phase=0 | train_loss=3.7421 | val_main=0.469554
Epoch 021 | phase=0 | train_loss=3.3840 | val_main=0.501593
Epoch 022 | phase=0 | train_loss=3.4689 | val_main=0.493420
Epoch 023 | phase=0 | train_loss=3.4739 | val_main=0.482293
Epoch 024 | phase=0 | train_loss=3.0567 | val_main=0.500483
Epoch 025 | phase=0 | train_loss=3.1525 | val_main=0.506236
Epoch 026 | phase=0 | train_loss=3.0683 | val_main=0.490539
Epoch 027 | phase=0 | train_loss=2.9381 | val_main=0.533375
Epoch 028 | phase=0 | train_loss=3.3865 | val_main=0.477815
Epoch 029 | phase=0 | train_loss=2.7216 | val_main=0.511413
Epoch 030 | phase=0 | train_loss=2.8267 | val_main=0.518688
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 6
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.21876415
1 day(s) RMSE : 7.40398113
1 day(s) R2 : 0.09028098
1 day(s) Pearson r : 0.36025642
1 day(s) QLIKE : 0.33964769
full horizon MAE : 2.21876415
full horizon RMSE : 7.40398113
full horizon R2 : 0.09028098
full horizon Pearson r : 0.36025642
full horizon QLIKE : 0.33964769
--- Task 2 ---
1 day(s) MAE : 0.13082751
1 day(s) RMSE : 0.16365420
1 day(s) R2 : -14.24392601
1 day(s) Pearson r : -0.04182469
1 day(s) QLIKE : 11.22750171
full horizon MAE : 0.13082751
full horizon RMSE : 0.16365420
full horizon R2 : -14.24392601
full horizon Pearson r : -0.04182469
full horizon QLIKE : 11.22750171
--- Task 3 ---
1 day(s) MAE : 0.32589114
1 day(s) RMSE : 0.38367963
1 day(s) R2 : -0.16284002
1 day(s) Pearson r : 0.14608751
1 day(s) QLIKE : 6.86966061
full horizon MAE : 0.32589114
full horizon RMSE : 0.38367963
full horizon R2 : -0.16284002
full horizon Pearson r : 0.14608751
full horizon QLIKE : 6.86966061
--- Task 4 ---
1 day(s) MAE : 0.82547524
1 day(s) RMSE : 0.97408619
1 day(s) R2 : -0.78516268
1 day(s) Pearson r : 0.03934078
1 day(s) QLIKE : 0.71555974
full horizon MAE : 0.82547524
full horizon RMSE : 0.97408619
full horizon R2 : -0.78516268
full horizon Pearson r : 0.03934078
full horizon QLIKE : 0.71555974
--- Task 5 ---
1 day(s) MAE : 0.03735234
1 day(s) RMSE : 0.05129185
1 day(s) R2 : -0.56887407
1 day(s) Pearson r : 0.48563714
1 day(s) QLIKE : 6.19513565
full horizon MAE : 0.03735234
full horizon RMSE : 0.05129185
full horizon R2 : -0.56887407
full horizon Pearson r : 0.48563714
full horizon QLIKE : 6.19513565
--- Task 6 ---
1 day(s) MAE : 1.75708952
1 day(s) RMSE : 3.71954191
1 day(s) R2 : -12.00419343
1 day(s) Pearson r : -0.26617078
1 day(s) QLIKE : 0.06822721
full horizon MAE : 1.75708952
full horizon RMSE : 3.71954191
full horizon R2 : -12.00419343
full horizon Pearson r : -0.26617078
full horizon QLIKE : 0.06822721
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BAC/Custom_KAN_H1.pkl
Saved y_true min=0.587715, max=111.472
Saved y_pred min=1.67265, max=15.1327
=== BAC | H=1 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.142355974179488
Min value: -0.42151023550938554
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.107742786547419
Min value: -11.49125811580691
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.800014951166767
Min value: -0.36472825885137616
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.2542888447212457
Min value: -1.9794140488614782
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.111754968787256
Min value: -0.38915813676977
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.207108669727662
Min value: -11.49125811580691
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 131s 456ms/step - loss: 1.0130 - val_loss: 0.6371 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 8s 171ms/step - loss: 0.9819 - val_loss: 0.5580 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 188ms/step - loss: 0.9454 - val_loss: 0.4752 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 197ms/step - loss: 0.9255 - val_loss: 0.4638 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 195ms/step - loss: 0.9214 - val_loss: 0.4710 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 189ms/step - loss: 0.9181 - val_loss: 0.4994 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 189ms/step - loss: 0.9095 - val_loss: 0.4556 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 188ms/step - loss: 0.9097 - val_loss: 0.4333 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 187ms/step - loss: 0.8930 - val_loss: 0.4250 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 191ms/step - loss: 0.8843 - val_loss: 0.4209 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 10s 213ms/step - loss: 0.8859 - val_loss: 0.4250 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=12 tf_ratio=0.421 -> TF=ON
Epoch 12/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 192ms/step - loss: 0.8883 - val_loss: 0.4193 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=13 tf_ratio=0.368 -> TF=ON
Epoch 13/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 189ms/step - loss: 0.8898 - val_loss: 0.4159 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=14 tf_ratio=0.316 -> TF=ON
Epoch 14/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 188ms/step - loss: 0.8884 - val_loss: 0.4039 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=15 tf_ratio=0.263 -> TF=ON
Epoch 15/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 10s 224ms/step - loss: 0.8811 - val_loss: 0.3900 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=16 tf_ratio=0.211 -> TF=ON
Epoch 16/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 190ms/step - loss: 0.8761 - val_loss: 0.3932 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=17 tf_ratio=0.158 -> TF=ON
Epoch 17/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 190ms/step - loss: 0.8735 - val_loss: 0.3929 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=18 tf_ratio=0.105 -> TF=ON
Epoch 18/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 189ms/step - loss: 0.8725 - val_loss: 0.4055 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=19 tf_ratio=0.053 -> TF=ON
Epoch 19/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 188ms/step - loss: 0.8703 - val_loss: 0.3816 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=20 tf_ratio=0.000 -> TF=OFF
Epoch 20/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 189ms/step - loss: 0.8723 - val_loss: 0.3862 - learning_rate: 5.0000e-04
Restoring model weights from the end of the best epoch: 19.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 1
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 1.98465755
1 day(s) RMSE : 7.11367405
1 day(s) R2 : 0.16022180
1 day(s) Pearson r : 0.43776217
1 day(s) QLIKE : 0.27296818
full horizon MAE : 1.98465755
full horizon RMSE : 7.11367405
full horizon R2 : 0.16022180
full horizon Pearson r : 0.43776217
full horizon QLIKE : 0.27296818
--- Task 2 ---
1 day(s) MAE : 0.07046811
1 day(s) RMSE : 0.08164147
1 day(s) R2 : -2.79371040
1 day(s) Pearson r : 0.10880732
1 day(s) QLIKE : 2.85652642
full horizon MAE : 0.07046811
full horizon RMSE : 0.08164147
full horizon R2 : -2.79371040
full horizon Pearson r : 0.10880732
full horizon QLIKE : 2.85652642
--- Task 3 ---
1 day(s) MAE : 0.31458458
1 day(s) RMSE : 0.35822679
1 day(s) R2 : -0.01367471
1 day(s) Pearson r : 0.11727377
1 day(s) QLIKE : 6.83185088
full horizon MAE : 0.31458458
full horizon RMSE : 0.35822679
full horizon R2 : -0.01367471
full horizon Pearson r : 0.11727377
full horizon QLIKE : 6.83185088
--- Task 4 ---
1 day(s) MAE : 0.96626179
1 day(s) RMSE : 1.19557676
1 day(s) R2 : -1.68929194
1 day(s) Pearson r : -0.09753670
1 day(s) QLIKE : 0.47578254
full horizon MAE : 0.96626179
full horizon RMSE : 1.19557676
full horizon R2 : -1.68929194
full horizon Pearson r : -0.09753670
full horizon QLIKE : 0.47578254
--- Task 5 ---
1 day(s) MAE : 0.05084285
1 day(s) RMSE : 0.06448893
1 day(s) R2 : -1.48005719
1 day(s) Pearson r : -0.00260489
1 day(s) QLIKE : 8.42518401
full horizon MAE : 0.05084285
full horizon RMSE : 0.06448893
full horizon R2 : -1.48005719
full horizon Pearson r : -0.00260489
full horizon QLIKE : 8.42518401
--- Task 6 ---
1 day(s) MAE : 1.48002148
1 day(s) RMSE : 1.98035446
1 day(s) R2 : -2.68629739
1 day(s) Pearson r : -0.12405966
1 day(s) QLIKE : 0.03828055
full horizon MAE : 1.48002148
full horizon RMSE : 1.98035446
full horizon R2 : -2.68629739
full horizon Pearson r : -0.12405966
full horizon QLIKE : 0.03828055
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BAC/Custom_KAN_LSTM_H1.pkl
Saved y_true min=0.587715, max=111.472
Saved y_pred min=1.57626, max=25.6446
=== BAC | H=5 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.142355974179488
Min value: -0.42151023550938554
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.106232115580254
Min value: -11.491304399558798
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.800014951166767
Min value: -0.36472825885137616
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.253588840112363
Min value: -1.9710462778525513
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.111754968787256
Min value: -0.38915813676977
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.205991919409481
Min value: -11.491304399558798
Epoch 001 | phase=1 | train_loss=4.9608 | val_main=0.545846
Epoch 002 | phase=1 | train_loss=4.8887 | val_main=0.545850
Epoch 003 | phase=1 | train_loss=4.9592 | val_main=0.545835
Epoch 004 | phase=1 | train_loss=4.7590 | val_main=0.545765
Epoch 005 | phase=1 | train_loss=4.4544 | val_main=0.545720
Epoch 006 | phase=1 | train_loss=4.5743 | val_main=0.545616
Epoch 007 | phase=1 | train_loss=4.4008 | val_main=0.545554
Epoch 008 | phase=1 | train_loss=4.2387 | val_main=0.545521
Epoch 009 | phase=1 | train_loss=4.2967 | val_main=0.545559
Epoch 010 | phase=1 | train_loss=4.0212 | val_main=0.545552
Epoch 011 | phase=1 | train_loss=3.9427 | val_main=0.545646
Epoch 012 | phase=1 | train_loss=4.0139 | val_main=0.545717
Epoch 013 | phase=1 | train_loss=3.8533 | val_main=0.545851
Epoch 014 | phase=1 | train_loss=3.7618 | val_main=0.545951
Epoch 015 | phase=1 | train_loss=3.6038 | val_main=0.546010
Epoch 016 | phase=0 | train_loss=4.5601 | val_main=0.535201
Epoch 017 | phase=0 | train_loss=4.3909 | val_main=0.505597
Epoch 018 | phase=0 | train_loss=4.1172 | val_main=0.504342
Epoch 019 | phase=0 | train_loss=3.8271 | val_main=0.527927
Epoch 020 | phase=0 | train_loss=3.6379 | val_main=0.497503
Epoch 021 | phase=0 | train_loss=3.5965 | val_main=0.525054
Epoch 022 | phase=0 | train_loss=3.4969 | val_main=0.528197
Epoch 023 | phase=0 | train_loss=3.5554 | val_main=0.523914
Epoch 024 | phase=0 | train_loss=3.2221 | val_main=0.527437
Epoch 025 | phase=0 | train_loss=3.2021 | val_main=0.532239
Epoch 026 | phase=0 | train_loss=3.2224 | val_main=0.519378
Epoch 027 | phase=0 | train_loss=3.1668 | val_main=0.529019
Epoch 028 | phase=0 | train_loss=3.3193 | val_main=0.515560
Epoch 029 | phase=0 | train_loss=2.9513 | val_main=0.522586
Epoch 030 | phase=0 | train_loss=2.9092 | val_main=0.522638
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 30
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.18270130
1 day(s) RMSE : 7.44395402
1 day(s) R2 : 0.08043161
1 day(s) Pearson r : 0.36082149
1 day(s) QLIKE : 0.35228798
3 day(s) MAE : 2.25918357
3 day(s) RMSE : 7.60902325
3 day(s) R2 : 0.03981017
3 day(s) Pearson r : 0.24484494
3 day(s) QLIKE : 0.39561364
5 day(s) MAE : 2.31575953
5 day(s) RMSE : 7.72861629
5 day(s) R2 : 0.00969592
5 day(s) Pearson r : 0.16282191
5 day(s) QLIKE : 0.42301109
full horizon MAE : 2.31575953
full horizon RMSE : 7.72861629
full horizon R2 : 0.00969592
full horizon Pearson r : 0.16282191
full horizon QLIKE : 0.42301109
--- Task 2 ---
1 day(s) MAE : 0.10867727
1 day(s) RMSE : 0.14486375
1 day(s) R2 : -10.94433524
1 day(s) Pearson r : -0.01677645
1 day(s) QLIKE : 13.21323228
3 day(s) MAE : 0.10526507
3 day(s) RMSE : 0.13500638
3 day(s) R2 : -9.45863205
3 day(s) Pearson r : -0.01915310
3 day(s) QLIKE : 13.12658592
5 day(s) MAE : 0.10958367
5 day(s) RMSE : 0.14231834
5 day(s) R2 : -10.71375158
5 day(s) Pearson r : -0.01869816
5 day(s) QLIKE : 13.08543450
full horizon MAE : 0.10958367
full horizon RMSE : 0.14231834
full horizon R2 : -10.71375158
full horizon Pearson r : -0.01869816
full horizon QLIKE : 13.08543450
--- Task 3 ---
1 day(s) MAE : 0.31232802
1 day(s) RMSE : 0.37564997
1 day(s) R2 : -0.11467738
1 day(s) Pearson r : 0.19452757
1 day(s) QLIKE : 6.85341585
3 day(s) MAE : 0.31462650
3 day(s) RMSE : 0.37763323
3 day(s) R2 : -0.12642251
3 day(s) Pearson r : 0.19146907
3 day(s) QLIKE : 6.84985210
5 day(s) MAE : 0.31629084
5 day(s) RMSE : 0.38005361
5 day(s) R2 : -0.14086344
5 day(s) Pearson r : 0.17880762
5 day(s) QLIKE : 6.85300999
full horizon MAE : 0.31629084
full horizon RMSE : 0.38005361
full horizon R2 : -0.14086344
full horizon Pearson r : 0.17880762
full horizon QLIKE : 6.85300999
--- Task 4 ---
1 day(s) MAE : 0.76485129
1 day(s) RMSE : 0.93651184
1 day(s) R2 : -0.65009735
1 day(s) Pearson r : 0.10229135
1 day(s) QLIKE : 1.00349636
3 day(s) MAE : 0.76521506
3 day(s) RMSE : 0.93768434
3 day(s) R2 : -0.65611012
3 day(s) Pearson r : 0.10983806
3 day(s) QLIKE : 1.01056388
5 day(s) MAE : 0.76404773
5 day(s) RMSE : 0.93487645
5 day(s) R2 : -0.64806505
5 day(s) Pearson r : 0.10885924
5 day(s) QLIKE : 1.02972328
full horizon MAE : 0.76404773
full horizon RMSE : 0.93487645
full horizon R2 : -0.64806505
full horizon Pearson r : 0.10885924
full horizon QLIKE : 1.02972328
--- Task 5 ---
1 day(s) MAE : 0.03860649
1 day(s) RMSE : 0.04944255
1 day(s) R2 : -0.45778359
1 day(s) Pearson r : 0.61673544
1 day(s) QLIKE : 5.95688339
3 day(s) MAE : 0.03879512
3 day(s) RMSE : 0.05016314
3 day(s) R2 : -0.50559273
3 day(s) Pearson r : 0.58891949
3 day(s) QLIKE : 6.01982210
5 day(s) MAE : 0.03947484
5 day(s) RMSE : 0.05134332
5 day(s) R2 : -0.58126195
5 day(s) Pearson r : 0.57827273
5 day(s) QLIKE : 6.00109497
full horizon MAE : 0.03947484
full horizon RMSE : 0.05134332
full horizon R2 : -0.58126195
full horizon Pearson r : 0.57827273
full horizon QLIKE : 6.00109497
--- Task 6 ---
1 day(s) MAE : 1.73060874
1 day(s) RMSE : 3.59874331
1 day(s) R2 : -11.17324183
1 day(s) Pearson r : -0.27209082
1 day(s) QLIKE : 0.06991260
3 day(s) MAE : 1.74280416
3 day(s) RMSE : 3.63976403
3 day(s) R2 : -11.37294333
3 day(s) Pearson r : -0.26851498
3 day(s) QLIKE : 0.07063474
5 day(s) MAE : 1.75808483
5 day(s) RMSE : 3.65160014
5 day(s) R2 : -11.38173709
5 day(s) Pearson r : -0.26522029
5 day(s) QLIKE : 0.07138147
full horizon MAE : 1.75808483
full horizon RMSE : 3.65160014
full horizon R2 : -11.38173709
full horizon Pearson r : -0.26522029
full horizon QLIKE : 0.07138147
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BAC/Custom_KAN_H5.pkl
Saved y_true min=0.587715, max=111.472
Saved y_pred min=1.62862, max=17.6604
=== BAC | H=5 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.142355974179488
Min value: -0.42151023550938554
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.106232115580254
Min value: -11.491304399558798
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.800014951166767
Min value: -0.36472825885137616
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.253588840112363
Min value: -1.9710462778525513
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.111754968787256
Min value: -0.38915813676977
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.205991919409481
Min value: -11.491304399558798
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 162s 481ms/step - loss: 0.8532 - val_loss: 0.5739 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 10s 212ms/step - loss: 0.5766 - val_loss: 0.4571 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 12s 251ms/step - loss: 0.5103 - val_loss: 0.5189 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 244ms/step - loss: 0.4804 - val_loss: 0.4528 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 250ms/step - loss: 0.4514 - val_loss: 0.4842 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 246ms/step - loss: 0.4320 - val_loss: 0.4611 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 247ms/step - loss: 0.4208 - val_loss: 0.4785 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 249ms/step - loss: 0.4146 - val_loss: 0.5208 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 237ms/step - loss: 0.3834
Epoch 9: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 244ms/step - loss: 0.4092 - val_loss: 0.5503 - learning_rate: 5.0000e-04
Epoch 9: early stopping
Restoring model weights from the end of the best epoch: 4.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 5
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.16254479
1 day(s) RMSE : 7.61616483
1 day(s) R2 : 0.03739228
1 day(s) Pearson r : 0.29405977
1 day(s) QLIKE : 0.32518651
3 day(s) MAE : 2.15970029
3 day(s) RMSE : 7.74345658
3 day(s) R2 : 0.00558192
3 day(s) Pearson r : 0.22022808
3 day(s) QLIKE : 0.37056787
5 day(s) MAE : 2.17472647
5 day(s) RMSE : 7.79888227
5 day(s) R2 : -0.00839296
5 day(s) Pearson r : 0.16489381
5 day(s) QLIKE : 0.39194049
full horizon MAE : 2.17472647
full horizon RMSE : 7.79888227
full horizon R2 : -0.00839296
full horizon Pearson r : 0.16489381
full horizon QLIKE : 0.39194049
--- Task 2 ---
1 day(s) MAE : 0.07004484
1 day(s) RMSE : 0.08089273
1 day(s) R2 : -2.72444489
1 day(s) Pearson r : 0.04919071
1 day(s) QLIKE : 2.65586125
3 day(s) MAE : 0.07158585
3 day(s) RMSE : 0.08255797
3 day(s) R2 : -2.91096817
3 day(s) Pearson r : 0.01076867
3 day(s) QLIKE : 7.31794794
5 day(s) MAE : 0.07197767
5 day(s) RMSE : 0.08294115
5 day(s) R2 : -2.97845444
5 day(s) Pearson r : 0.00918905
5 day(s) QLIKE : 11.83037043
full horizon MAE : 0.07197767
full horizon RMSE : 0.08294115
full horizon R2 : -2.97845444
full horizon Pearson r : 0.00918905
full horizon QLIKE : 11.83037043
--- Task 3 ---
1 day(s) MAE : 0.32691785
1 day(s) RMSE : 0.37556135
1 day(s) R2 : -0.11415151
1 day(s) Pearson r : 0.16302681
1 day(s) QLIKE : 6.83534931
3 day(s) MAE : 0.32354858
3 day(s) RMSE : 0.36334012
3 day(s) R2 : -0.04276788
3 day(s) Pearson r : 0.02406011
3 day(s) QLIKE : 6.84299555
5 day(s) MAE : 0.32291458
5 day(s) RMSE : 0.35995652
5 day(s) R2 : -0.02339674
5 day(s) Pearson r : 0.04753250
5 day(s) QLIKE : 6.84355992
full horizon MAE : 0.32291458
full horizon RMSE : 0.35995652
full horizon R2 : -0.02339674
full horizon Pearson r : 0.04753250
full horizon QLIKE : 6.84355992
--- Task 4 ---
1 day(s) MAE : 0.90330908
1 day(s) RMSE : 1.12813065
1 day(s) R2 : -1.39442822
1 day(s) Pearson r : -0.04231339
1 day(s) QLIKE : 0.37180528
3 day(s) MAE : 0.83071082
3 day(s) RMSE : 1.03584752
3 day(s) R2 : -1.02100577
3 day(s) Pearson r : -0.03073603
3 day(s) QLIKE : 0.43479715
5 day(s) MAE : 0.77029336
5 day(s) RMSE : 0.94577899
5 day(s) R2 : -0.68672869
5 day(s) Pearson r : -0.01925831
5 day(s) QLIKE : 0.48275681
full horizon MAE : 0.77029336
full horizon RMSE : 0.94577899
full horizon R2 : -0.68672869
full horizon Pearson r : -0.01925831
full horizon QLIKE : 0.48275681
--- Task 5 ---
1 day(s) MAE : 0.05242022
1 day(s) RMSE : 0.06647485
1 day(s) R2 : -1.63515516
1 day(s) Pearson r : -0.07844138
1 day(s) QLIKE : 5.91447046
3 day(s) MAE : 0.05264951
3 day(s) RMSE : 0.06664330
3 day(s) R2 : -1.65736422
3 day(s) Pearson r : -0.01727581
3 day(s) QLIKE : 13.74495787
5 day(s) MAE : 0.05286777
5 day(s) RMSE : 0.06679022
5 day(s) R2 : -1.67585004
5 day(s) Pearson r : -0.01479129
5 day(s) QLIKE : 11.68814819
full horizon MAE : 0.05286777
full horizon RMSE : 0.06679022
full horizon R2 : -1.67585004
full horizon Pearson r : -0.01479129
full horizon QLIKE : 11.68814819
--- Task 6 ---
1 day(s) MAE : 1.34912549
1 day(s) RMSE : 1.70931726
1 day(s) R2 : -1.74631194
1 day(s) Pearson r : -0.20179339
1 day(s) QLIKE : 0.02592959
3 day(s) MAE : 1.19200360
3 day(s) RMSE : 1.51059534
3 day(s) R2 : -1.13119332
3 day(s) Pearson r : -0.16144436
3 day(s) QLIKE : 0.02882274
5 day(s) MAE : 1.13819544
5 day(s) RMSE : 1.47310240
5 day(s) R2 : -1.01502772
5 day(s) Pearson r : -0.13360383
5 day(s) QLIKE : 0.03369750
full horizon MAE : 1.13819544
full horizon RMSE : 1.47310240
full horizon R2 : -1.01502772
full horizon Pearson r : -0.13360383
full horizon QLIKE : 0.03369750
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BAC/Custom_KAN_LSTM_H5.pkl
Saved y_true min=0.587715, max=111.472
Saved y_pred min=2.08499, max=5.06627
=== BAC | H=10 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.142355974179488
Min value: -0.42151023550938554
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.103196331248472
Min value: -11.491352783545967
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.800014951166767
Min value: -0.36472825885137616
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.2515405379791398
Min value: -1.9606954808877406
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.111754968787256
Min value: -0.38915813676977
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.203435974440148
Min value: -11.491352783545967
Epoch 001 | phase=1 | train_loss=5.0010 | val_main=0.536720
Epoch 002 | phase=1 | train_loss=4.9013 | val_main=0.536765
Epoch 003 | phase=1 | train_loss=4.9571 | val_main=0.536868
Epoch 004 | phase=1 | train_loss=4.7704 | val_main=0.536950
Epoch 005 | phase=1 | train_loss=4.4548 | val_main=0.536915
Epoch 006 | phase=1 | train_loss=4.5139 | val_main=0.537046
Epoch 007 | phase=1 | train_loss=4.3809 | val_main=0.536997
Epoch 008 | phase=1 | train_loss=4.2676 | val_main=0.537097
Epoch 009 | phase=1 | train_loss=4.2648 | val_main=0.537233
Epoch 010 | phase=1 | train_loss=4.0298 | val_main=0.536860
Epoch 011 | phase=1 | train_loss=3.9483 | val_main=0.537115
Epoch 012 | phase=1 | train_loss=4.0520 | val_main=0.537000
Epoch 013 | phase=1 | train_loss=3.8559 | val_main=0.536965
Epoch 014 | phase=1 | train_loss=3.7924 | val_main=0.536917
Epoch 015 | phase=1 | train_loss=3.6161 | val_main=0.537068
Epoch 016 | phase=0 | train_loss=4.6228 | val_main=0.529338
Epoch 017 | phase=0 | train_loss=4.3395 | val_main=0.501402
Epoch 018 | phase=0 | train_loss=4.2134 | val_main=0.505107
Epoch 019 | phase=0 | train_loss=3.9097 | val_main=0.553127
Epoch 020 | phase=0 | train_loss=3.7150 | val_main=0.513077
Epoch 021 | phase=0 | train_loss=3.6330 | val_main=0.507749
Epoch 022 | phase=0 | train_loss=3.6073 | val_main=0.509842
Epoch 023 | phase=0 | train_loss=3.6310 | val_main=0.517197
Epoch 024 | phase=0 | train_loss=3.3245 | val_main=0.518156
Epoch 025 | phase=0 | train_loss=3.3517 | val_main=0.522236
Epoch 026 | phase=0 | train_loss=3.3343 | val_main=0.508254
Epoch 027 | phase=0 | train_loss=3.3106 | val_main=0.527277
Epoch 028 | phase=0 | train_loss=3.3546 | val_main=0.509757
Epoch 029 | phase=0 | train_loss=3.1087 | val_main=0.520470
Epoch 030 | phase=0 | train_loss=3.1270 | val_main=0.513141
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 60
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.34590783
1 day(s) RMSE : 7.75802991
1 day(s) R2 : 0.00119762
1 day(s) Pearson r : 0.15966105
1 day(s) QLIKE : 0.40754764
3 day(s) MAE : 2.35499479
3 day(s) RMSE : 7.78203057
3 day(s) R2 : -0.00435013
3 day(s) Pearson r : 0.07288586
3 day(s) QLIKE : 0.42043632
5 day(s) MAE : 2.36866853
5 day(s) RMSE : 7.79731201
5 day(s) R2 : -0.00798694
5 day(s) Pearson r : 0.00352125
5 day(s) QLIKE : 0.42884842
10 day(s) MAE : 2.37775464
10 day(s) RMSE : 7.80807994
10 day(s) R2 : -0.01067697
10 day(s) Pearson r : -0.04459571
10 day(s) QLIKE : 0.43318596
full horizon MAE : 2.37775464
full horizon RMSE : 7.80807994
full horizon R2 : -0.01067697
full horizon Pearson r : -0.04459571
full horizon QLIKE : 0.43318596
--- Task 2 ---
1 day(s) MAE : 0.07206634
1 day(s) RMSE : 0.08125629
1 day(s) R2 : -2.75799751
1 day(s) Pearson r : -0.14273978
1 day(s) QLIKE : 11.71580640
3 day(s) MAE : 0.07337047
3 day(s) RMSE : 0.08332631
3 day(s) R2 : -2.98410330
3 day(s) Pearson r : -0.13625783
3 day(s) QLIKE : 11.89378264
5 day(s) MAE : 0.08245666
5 day(s) RMSE : 0.09588868
5 day(s) R2 : -4.31751808
5 day(s) Pearson r : -0.12128773
5 day(s) QLIKE : 12.12633643
10 day(s) MAE : 0.08453744
10 day(s) RMSE : 0.09770019
10 day(s) R2 : -4.60736754
10 day(s) Pearson r : -0.12449508
10 day(s) QLIKE : 12.23010252
full horizon MAE : 0.08453744
full horizon RMSE : 0.09770019
full horizon R2 : -4.60736754
full horizon Pearson r : -0.12449508
full horizon QLIKE : 12.23010252
--- Task 3 ---
1 day(s) MAE : 0.31034323
1 day(s) RMSE : 0.37152759
1 day(s) R2 : -0.09034673
1 day(s) Pearson r : 0.26719008
1 day(s) QLIKE : 6.82437096
3 day(s) MAE : 0.31479138
3 day(s) RMSE : 0.37737315
3 day(s) R2 : -0.12487151
3 day(s) Pearson r : 0.24346146
3 day(s) QLIKE : 6.82687393
5 day(s) MAE : 0.31539033
5 day(s) RMSE : 0.37839908
5 day(s) R2 : -0.13095177
5 day(s) Pearson r : 0.23487974
5 day(s) QLIKE : 6.82939444
10 day(s) MAE : 0.31598961
10 day(s) RMSE : 0.38033026
10 day(s) R2 : -0.14272052
10 day(s) Pearson r : 0.21105051
10 day(s) QLIKE : 6.83703672
full horizon MAE : 0.31598961
full horizon RMSE : 0.38033026
full horizon R2 : -0.14272052
full horizon Pearson r : 0.21105051
full horizon QLIKE : 6.83703672
--- Task 4 ---
1 day(s) MAE : 0.75512644
1 day(s) RMSE : 0.94823929
1 day(s) R2 : -0.69168273
1 day(s) Pearson r : 0.21882662
1 day(s) QLIKE : 0.57376015
3 day(s) MAE : 0.76538425
3 day(s) RMSE : 0.95755977
3 day(s) R2 : -0.72706097
3 day(s) Pearson r : 0.22125383
3 day(s) QLIKE : 0.55829232
5 day(s) MAE : 0.75174942
5 day(s) RMSE : 0.94373346
5 day(s) R2 : -0.67944048
5 day(s) Pearson r : 0.21529906
5 day(s) QLIKE : 0.58602024
10 day(s) MAE : 0.73747537
10 day(s) RMSE : 0.93020271
10 day(s) R2 : -0.63613332
10 day(s) Pearson r : 0.22373957
10 day(s) QLIKE : 0.61186191
full horizon MAE : 0.73747537
full horizon RMSE : 0.93020271
full horizon R2 : -0.63613332
full horizon Pearson r : 0.22373957
full horizon QLIKE : 0.61186191
--- Task 5 ---
1 day(s) MAE : 0.03614182
1 day(s) RMSE : 0.04662917
1 day(s) R2 : -0.29660202
1 day(s) Pearson r : 0.52471613
1 day(s) QLIKE : 6.08611143
3 day(s) MAE : 0.03549608
3 day(s) RMSE : 0.04585899
3 day(s) R2 : -0.25830837
3 day(s) Pearson r : 0.51913458
3 day(s) QLIKE : 6.04060621
5 day(s) MAE : 0.03652988
5 day(s) RMSE : 0.04620915
5 day(s) R2 : -0.28083127
5 day(s) Pearson r : 0.53011738
5 day(s) QLIKE : 5.96966409
10 day(s) MAE : 0.03636241
10 day(s) RMSE : 0.04585370
10 day(s) R2 : -0.27040750
10 day(s) Pearson r : 0.53787404
10 day(s) QLIKE : 5.79205217
full horizon MAE : 0.03636241
full horizon RMSE : 0.04585370
full horizon R2 : -0.27040750
full horizon Pearson r : 0.53787404
full horizon QLIKE : 5.79205217
--- Task 6 ---
1 day(s) MAE : 1.82638128
1 day(s) RMSE : 2.96022589
1 day(s) R2 : -7.23671833
1 day(s) Pearson r : -0.26402920
1 day(s) QLIKE : 0.08031078
3 day(s) MAE : 1.91284644
3 day(s) RMSE : 3.16769413
3 day(s) R2 : -8.37158494
3 day(s) Pearson r : -0.27116945
3 day(s) QLIKE : 0.08522938
5 day(s) MAE : 1.93444349
5 day(s) RMSE : 3.21251542
5 day(s) R2 : -8.58309053
5 day(s) Pearson r : -0.26871955
5 day(s) QLIKE : 0.08617828
10 day(s) MAE : 1.96810134
10 day(s) RMSE : 3.25252692
10 day(s) R2 : -8.66210283
10 day(s) Pearson r : -0.26942197
10 day(s) QLIKE : 0.08838694
full horizon MAE : 1.96810134
full horizon RMSE : 3.25252692
full horizon R2 : -8.66210283
full horizon Pearson r : -0.26942197
full horizon QLIKE : 0.08838694
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BAC/Custom_KAN_H10.pkl
Saved y_true min=0.587715, max=111.472
Saved y_pred min=2.50056, max=4.26984
=== BAC | H=10 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.142355974179488
Min value: -0.42151023550938554
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.103196331248472
Min value: -11.491352783545967
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.800014951166767
Min value: -0.36472825885137616
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.2515405379791398
Min value: -1.9606954808877406
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.111754968787256
Min value: -0.38915813676977
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.203435974440148
Min value: -11.491352783545967
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 143s 490ms/step - loss: 0.8209 - val_loss: 0.5610 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 249ms/step - loss: 0.5139 - val_loss: 0.5171 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 333ms/step - loss: 0.4406 - val_loss: 0.5513 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 18s 387ms/step - loss: 0.4006 - val_loss: 0.8866 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 17s 372ms/step - loss: 0.3810 - val_loss: 0.8487 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 454ms/step - loss: 0.4069 - val_loss: 0.5751 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 359ms/step - loss: 0.3744
Epoch 7: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 17s 369ms/step - loss: 0.3709 - val_loss: 0.5524 - learning_rate: 5.0000e-04
Epoch 7: early stopping
Restoring model weights from the end of the best epoch: 2.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 10
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.61792440
1 day(s) RMSE : 7.76164787
1 day(s) R2 : 0.00026582
1 day(s) Pearson r : 0.35168474
1 day(s) QLIKE : 0.42234177
3 day(s) MAE : 2.53037497
3 day(s) RMSE : 7.77124332
3 day(s) R2 : -0.00156766
3 day(s) Pearson r : 0.00296058
3 day(s) QLIKE : 0.42529610
5 day(s) MAE : 2.40686350
5 day(s) RMSE : 7.80222085
5 day(s) R2 : -0.00925650
5 day(s) Pearson r : -0.00053861
5 day(s) QLIKE : 0.43496490
10 day(s) MAE : 2.30011117
10 day(s) RMSE : 7.85221073
10 day(s) R2 : -0.02213383
10 day(s) Pearson r : -0.00134547
10 day(s) QLIKE : 0.43950601
full horizon MAE : 2.30011117
full horizon RMSE : 7.85221073
full horizon R2 : -0.02213383
full horizon Pearson r : -0.00134547
full horizon QLIKE : 0.43950601
--- Task 2 ---
1 day(s) MAE : 0.07030682
1 day(s) RMSE : 0.08119597
1 day(s) R2 : -2.75242069
1 day(s) Pearson r : 0.08582186
1 day(s) QLIKE : 2.63841790
3 day(s) MAE : 0.07066192
3 day(s) RMSE : 0.08153817
3 day(s) R2 : -2.81494383
3 day(s) Pearson r : 0.05230699
3 day(s) QLIKE : 2.89788945
5 day(s) MAE : 0.07139619
5 day(s) RMSE : 0.08230157
5 day(s) R2 : -2.91733359
5 day(s) Pearson r : 0.04223065
5 day(s) QLIKE : 13.21461445
10 day(s) MAE : 0.07180376
10 day(s) RMSE : 0.08265384
10 day(s) R2 : -3.01323160
10 day(s) Pearson r : 0.03012506
10 day(s) QLIKE : 8.75563277
full horizon MAE : 0.07180376
full horizon RMSE : 0.08265384
full horizon R2 : -3.01323160
full horizon Pearson r : 0.03012506
full horizon QLIKE : 8.75563277
--- Task 3 ---
1 day(s) MAE : 0.32394873
1 day(s) RMSE : 0.36058464
1 day(s) R2 : -0.02706259
1 day(s) Pearson r : 0.04522192
1 day(s) QLIKE : 6.83803996
3 day(s) MAE : 0.32331031
3 day(s) RMSE : 0.35806194
3 day(s) R2 : -0.01269167
3 day(s) Pearson r : 0.00139543
3 day(s) QLIKE : 6.83956108
5 day(s) MAE : 0.32422904
5 day(s) RMSE : 0.35781382
5 day(s) R2 : -0.01124913
5 day(s) Pearson r : 0.00283365
5 day(s) QLIKE : 6.84192918
10 day(s) MAE : 0.32939485
10 day(s) RMSE : 0.36259803
10 day(s) R2 : -0.03864978
10 day(s) Pearson r : 0.01782435
10 day(s) QLIKE : 6.84697748
full horizon MAE : 0.32939485
full horizon RMSE : 0.36259803
full horizon R2 : -0.03864978
full horizon Pearson r : 0.01782435
full horizon QLIKE : 6.84697748
--- Task 4 ---
1 day(s) MAE : 0.96121378
1 day(s) RMSE : 1.18639477
1 day(s) R2 : -1.64814324
1 day(s) Pearson r : 0.03934531
1 day(s) QLIKE : 0.37004759
3 day(s) MAE : 0.86428306
3 day(s) RMSE : 1.07632842
3 day(s) R2 : -1.18205405
3 day(s) Pearson r : 0.00315169
3 day(s) QLIKE : 0.45606930
5 day(s) MAE : 0.78101028
5 day(s) RMSE : 0.95750475
5 day(s) R2 : -0.72881208
5 day(s) Pearson r : 0.00146129
5 day(s) QLIKE : 0.56441939
10 day(s) MAE : 0.72238635
10 day(s) RMSE : 0.85251688
10 day(s) R2 : -0.37426174
10 day(s) Pearson r : 0.00241930
10 day(s) QLIKE : 0.59540425
full horizon MAE : 0.72238635
full horizon RMSE : 0.85251688
full horizon R2 : -0.37426174
full horizon Pearson r : 0.00241930
full horizon QLIKE : 0.59540425
--- Task 5 ---
1 day(s) MAE : 0.05240711
1 day(s) RMSE : 0.06645369
1 day(s) R2 : -1.63347726
1 day(s) Pearson r : -0.12386370
1 day(s) QLIKE : 5.74400210
3 day(s) MAE : 0.05261741
3 day(s) RMSE : 0.06659527
3 day(s) R2 : -1.65353534
3 day(s) Pearson r : -0.01051259
3 day(s) QLIKE : 6.46084475
5 day(s) MAE : 0.05284851
5 day(s) RMSE : 0.06676147
5 day(s) R2 : -1.67354681
5 day(s) Pearson r : -0.01018956
5 day(s) QLIKE : 14.16369397
10 day(s) MAE : 0.05336868
10 day(s) RMSE : 0.06709525
10 day(s) R2 : -1.72005593
10 day(s) Pearson r : -0.01267468
10 day(s) QLIKE : 11.32019127
full horizon MAE : 0.05336868
full horizon RMSE : 0.06709525
full horizon R2 : -1.72005593
full horizon Pearson r : -0.01267468
full horizon QLIKE : 11.32019127
--- Task 6 ---
1 day(s) MAE : 2.29643825
1 day(s) RMSE : 2.50475036
1 day(s) R2 : -4.89703093
1 day(s) Pearson r : 0.19121087
1 day(s) QLIKE : 0.02058462
3 day(s) MAE : 2.05240935
3 day(s) RMSE : 2.28244379
3 day(s) R2 : -3.86549186
3 day(s) Pearson r : 0.04246470
3 day(s) QLIKE : 0.02096699
5 day(s) MAE : 1.65575480
5 day(s) RMSE : 1.95012368
5 day(s) R2 : -2.53133939
5 day(s) Pearson r : 0.00385013
5 day(s) QLIKE : 0.03044219
10 day(s) MAE : 1.39327189
10 day(s) RMSE : 1.70956402
10 day(s) R2 : -1.66931970
10 day(s) Pearson r : 0.00728075
10 day(s) QLIKE : 0.05017669
full horizon MAE : 1.39327189
full horizon RMSE : 1.70956402
full horizon R2 : -1.66931970
full horizon Pearson r : 0.00728075
full horizon QLIKE : 0.05017669
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BAC/Custom_KAN_LSTM_H10.pkl
Saved y_true min=0.587715, max=111.472
Saved y_pred min=2.21556, max=3.8027
=== BAC | H=20 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.142355974179488
Min value: -0.42151023550938554
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.103268362353512
Min value: -11.491398632817031
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.800014951166767
Min value: -0.36472825885137616
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.2516894965633285
Min value: -1.94034481938301
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.111754968787256
Min value: -0.38915813676977
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.203545386302637
Min value: -11.491398632817031
Epoch 001 | phase=1 | train_loss=5.0668 | val_main=0.539934
Epoch 002 | phase=1 | train_loss=4.9315 | val_main=0.539984
Epoch 003 | phase=1 | train_loss=5.0042 | val_main=0.540100
Epoch 004 | phase=1 | train_loss=4.9010 | val_main=0.540134
Epoch 005 | phase=1 | train_loss=4.5863 | val_main=0.539918
Epoch 006 | phase=1 | train_loss=4.5680 | val_main=0.540063
Epoch 007 | phase=1 | train_loss=4.4653 | val_main=0.539838
Epoch 008 | phase=1 | train_loss=4.3096 | val_main=0.540001
Epoch 009 | phase=1 | train_loss=4.2756 | val_main=0.540173
Epoch 010 | phase=1 | train_loss=4.0859 | val_main=0.539798
Epoch 011 | phase=1 | train_loss=4.0025 | val_main=0.540084
Epoch 012 | phase=1 | train_loss=4.1234 | val_main=0.540001
Epoch 013 | phase=1 | train_loss=3.8998 | val_main=0.540044
Epoch 014 | phase=1 | train_loss=3.8417 | val_main=0.540019
Epoch 015 | phase=1 | train_loss=3.6804 | val_main=0.540006
Epoch 016 | phase=0 | train_loss=4.7309 | val_main=0.536237
Epoch 017 | phase=0 | train_loss=4.4614 | val_main=0.515261
Epoch 018 | phase=0 | train_loss=4.3773 | val_main=0.530009
Epoch 019 | phase=0 | train_loss=4.1156 | val_main=0.581082
Epoch 020 | phase=0 | train_loss=3.8890 | val_main=0.544786
Epoch 021 | phase=0 | train_loss=3.8467 | val_main=0.537501
Epoch 022 | phase=0 | train_loss=3.7569 | val_main=0.535718
Epoch 023 | phase=0 | train_loss=3.8449 | val_main=0.541523
Epoch 024 | phase=0 | train_loss=3.5522 | val_main=0.543480
Epoch 025 | phase=0 | train_loss=3.5228 | val_main=0.555115
Epoch 026 | phase=0 | train_loss=3.4756 | val_main=0.566396
Epoch 027 | phase=0 | train_loss=3.4451 | val_main=0.576659
Epoch 028 | phase=0 | train_loss=3.4579 | val_main=0.560898
Epoch 029 | phase=0 | train_loss=3.3072 | val_main=0.591512
Epoch 030 | phase=0 | train_loss=3.2482 | val_main=0.591284
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 120
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.30621634
1 day(s) RMSE : 7.74946106
1 day(s) R2 : 0.00340278
1 day(s) Pearson r : 0.22546728
1 day(s) QLIKE : 0.39833677
3 day(s) MAE : 2.33237235
3 day(s) RMSE : 7.77608271
3 day(s) R2 : -0.00281546
3 day(s) Pearson r : 0.13138949
3 day(s) QLIKE : 0.41333628
5 day(s) MAE : 2.34401869
5 day(s) RMSE : 7.79083744
5 day(s) R2 : -0.00631365
5 day(s) Pearson r : 0.05498443
5 day(s) QLIKE : 0.42153248
10 day(s) MAE : 2.35034990
10 day(s) RMSE : 7.80704324
10 day(s) R2 : -0.01040861
10 day(s) Pearson r : -0.02063303
10 day(s) QLIKE : 0.42934899
20 day(s) MAE : 2.36062174
20 day(s) RMSE : 7.81097809
20 day(s) R2 : -0.01176057
20 day(s) Pearson r : -0.05425925
20 day(s) QLIKE : 0.43000817
full horizon MAE : 2.36062174
full horizon RMSE : 7.81097809
full horizon R2 : -0.01176057
full horizon Pearson r : -0.05425925
full horizon QLIKE : 0.43000817
--- Task 2 ---
1 day(s) MAE : 0.05908268
1 day(s) RMSE : 0.06822362
1 day(s) R2 : -1.64918321
1 day(s) Pearson r : -0.01087365
1 day(s) QLIKE : 9.56081151
3 day(s) MAE : 0.05881067
3 day(s) RMSE : 0.06754039
3 day(s) R2 : -1.61754055
3 day(s) Pearson r : -0.02167353
3 day(s) QLIKE : 9.58699470
5 day(s) MAE : 0.06324227
5 day(s) RMSE : 0.07342627
5 day(s) R2 : -2.11800764
5 day(s) Pearson r : -0.02993110
5 day(s) QLIKE : 9.85102063
10 day(s) MAE : 0.06509059
10 day(s) RMSE : 0.07516596
10 day(s) R2 : -2.31902604
10 day(s) Pearson r : -0.04016992
10 day(s) QLIKE : 10.22771802
20 day(s) MAE : 0.06804632
20 day(s) RMSE : 0.08016317
20 day(s) R2 : -2.86379608
20 day(s) Pearson r : -0.06726053
20 day(s) QLIKE : 9.94153877
full horizon MAE : 0.06804632
full horizon RMSE : 0.08016317
full horizon R2 : -2.86379608
full horizon Pearson r : -0.06726053
full horizon QLIKE : 9.94153877
--- Task 3 ---
1 day(s) MAE : 0.31381848
1 day(s) RMSE : 0.37867126
1 day(s) R2 : -0.13267982
1 day(s) Pearson r : 0.21149946
1 day(s) QLIKE : 6.82831597
3 day(s) MAE : 0.31567088
3 day(s) RMSE : 0.38158962
3 day(s) R2 : -0.15014879
3 day(s) Pearson r : 0.19909437
3 day(s) QLIKE : 6.83039079
5 day(s) MAE : 0.31549558
5 day(s) RMSE : 0.38186279
5 day(s) R2 : -0.15175110
5 day(s) Pearson r : 0.19215820
5 day(s) QLIKE : 6.83363268
10 day(s) MAE : 0.31717277
10 day(s) RMSE : 0.38574733
10 day(s) R2 : -0.17550400
10 day(s) Pearson r : 0.16492785
10 day(s) QLIKE : 6.84227266
20 day(s) MAE : 0.31732398
20 day(s) RMSE : 0.38717770
20 day(s) R2 : -0.18363342
20 day(s) Pearson r : 0.14738760
20 day(s) QLIKE : 6.84872049
full horizon MAE : 0.31732398
full horizon RMSE : 0.38717770
full horizon R2 : -0.18363342
full horizon Pearson r : 0.14738760
full horizon QLIKE : 6.84872049
--- Task 4 ---
1 day(s) MAE : 0.81891207
1 day(s) RMSE : 1.01523587
1 day(s) R2 : -0.93917468
1 day(s) Pearson r : 0.15072295
1 day(s) QLIKE : 0.55906894
3 day(s) MAE : 0.80940210
3 day(s) RMSE : 1.00392833
3 day(s) R2 : -0.89837194
3 day(s) Pearson r : 0.15225935
3 day(s) QLIKE : 0.58705643
5 day(s) MAE : 0.79780174
5 day(s) RMSE : 0.99032482
5 day(s) R2 : -0.84935906
5 day(s) Pearson r : 0.15813838
5 day(s) QLIKE : 0.59762807
10 day(s) MAE : 0.77649047
10 day(s) RMSE : 0.96775698
10 day(s) R2 : -0.77090846
10 day(s) Pearson r : 0.17306089
10 day(s) QLIKE : 0.63099985
20 day(s) MAE : 0.75676972
20 day(s) RMSE : 0.94925502
20 day(s) R2 : -0.71262000
20 day(s) Pearson r : 0.21438220
20 day(s) QLIKE : 0.63175612
full horizon MAE : 0.75676972
full horizon RMSE : 0.94925502
full horizon R2 : -0.71262000
full horizon Pearson r : 0.21438220
full horizon QLIKE : 0.63175612
--- Task 5 ---
1 day(s) MAE : 0.03393362
1 day(s) RMSE : 0.04283346
1 day(s) R2 : -0.09410181
1 day(s) Pearson r : 0.58157407
1 day(s) QLIKE : 6.03120779
3 day(s) MAE : 0.03662793
3 day(s) RMSE : 0.04572905
3 day(s) R2 : -0.25118769
3 day(s) Pearson r : 0.56401463
3 day(s) QLIKE : 6.01110399
5 day(s) MAE : 0.03753888
5 day(s) RMSE : 0.04642153
5 day(s) R2 : -0.29263191
5 day(s) Pearson r : 0.55974853
5 day(s) QLIKE : 5.95176416
10 day(s) MAE : 0.03742993
10 day(s) RMSE : 0.04609014
10 day(s) R2 : -0.28354284
10 day(s) Pearson r : 0.56276750
10 day(s) QLIKE : 5.79677805
20 day(s) MAE : 0.03856730
20 day(s) RMSE : 0.04724975
20 day(s) R2 : -0.37467305
20 day(s) Pearson r : 0.56830236
20 day(s) QLIKE : 5.47496037
full horizon MAE : 0.03856730
full horizon RMSE : 0.04724975
full horizon R2 : -0.37467305
full horizon Pearson r : 0.56830236
full horizon QLIKE : 5.47496037
--- Task 6 ---
1 day(s) MAE : 1.64973608
1 day(s) RMSE : 2.42131750
1 day(s) R2 : -4.51071552
1 day(s) Pearson r : -0.30045540
1 day(s) QLIKE : 0.06921057
3 day(s) MAE : 1.70839140
3 day(s) RMSE : 2.51032310
3 day(s) R2 : -4.88553317
3 day(s) Pearson r : -0.31025651
3 day(s) QLIKE : 0.07290938
5 day(s) MAE : 1.73109746
5 day(s) RMSE : 2.55060369
5 day(s) R2 : -5.04089264
5 day(s) Pearson r : -0.31678469
5 day(s) QLIKE : 0.07420467
10 day(s) MAE : 1.76259562
10 day(s) RMSE : 2.59192191
10 day(s) R2 : -5.13583512
10 day(s) Pearson r : -0.32471327
10 day(s) QLIKE : 0.07606841
20 day(s) MAE : 1.79594364
20 day(s) RMSE : 2.64762141
20 day(s) R2 : -5.20184345
20 day(s) Pearson r : -0.32415207
20 day(s) QLIKE : 0.07897685
full horizon MAE : 1.79594364
full horizon RMSE : 2.64762141
full horizon R2 : -5.20184345
full horizon Pearson r : -0.32415207
full horizon QLIKE : 0.07897685
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BAC/Custom_KAN_H20.pkl
Saved y_true min=0.587715, max=111.472
Saved y_pred min=2.43049, max=4.47631
=== BAC | H=20 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.142355974179488
Min value: -0.42151023550938554
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.103268362353512
Min value: -11.491398632817031
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.800014951166767
Min value: -0.36472825885137616
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.2516894965633285
Min value: -1.94034481938301
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.111754968787256
Min value: -0.38915813676977
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.203545386302637
Min value: -11.491398632817031
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 147s 570ms/step - loss: 0.8130 - val_loss: 0.4972 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 325ms/step - loss: 0.4822 - val_loss: 0.9885 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 456ms/step - loss: 0.3974 - val_loss: 1.6913 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 456ms/step - loss: 0.3598 - val_loss: 0.5860 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 462ms/step - loss: 0.3828 - val_loss: 0.5746 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 552ms/step - loss: 0.3375
Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 26s 566ms/step - loss: 0.3393 - val_loss: 0.5025 - learning_rate: 5.0000e-04
Epoch 6: early stopping
Restoring model weights from the end of the best epoch: 1.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 20
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.45825875
1 day(s) RMSE : 7.77151281
1 day(s) R2 : -0.00227709
1 day(s) Pearson r : 0.49840841
1 day(s) QLIKE : 0.42290087
3 day(s) MAE : 2.34487538
3 day(s) RMSE : 7.79718136
3 day(s) R2 : -0.00826467
3 day(s) Pearson r : 0.00844745
3 day(s) QLIKE : 0.42613528
5 day(s) MAE : 2.29761018
5 day(s) RMSE : 7.81581143
5 day(s) R2 : -0.01277558
5 day(s) Pearson r : 0.00371511
5 day(s) QLIKE : 0.42757047
10 day(s) MAE : 2.28878469
10 day(s) RMSE : 7.82057971
10 day(s) R2 : -0.01391550
10 day(s) Pearson r : 0.00061047
10 day(s) QLIKE : 0.42549425
20 day(s) MAE : 2.30827473
20 day(s) RMSE : 7.81276179
20 day(s) R2 : -0.01222271
20 day(s) Pearson r : 0.00090955
20 day(s) QLIKE : 0.42280907
full horizon MAE : 2.30827473
full horizon RMSE : 7.81276179
full horizon R2 : -0.01222271
full horizon Pearson r : 0.00090955
full horizon QLIKE : 0.42280907
--- Task 2 ---
1 day(s) MAE : 0.07210984
1 day(s) RMSE : 0.08318265
1 day(s) R2 : -2.93829295
1 day(s) Pearson r : -0.08682322
1 day(s) QLIKE : 2.64269584
3 day(s) MAE : 0.07255141
3 day(s) RMSE : 0.08362155
3 day(s) R2 : -3.01238560
3 day(s) Pearson r : -0.00053045
3 day(s) QLIKE : 7.19684871
5 day(s) MAE : 0.07255700
5 day(s) RMSE : 0.08357799
5 day(s) R2 : -3.03978425
5 day(s) Pearson r : 0.00148602
5 day(s) QLIKE : 8.14458341
10 day(s) MAE : 0.07238416
10 day(s) RMSE : 0.08329179
10 day(s) R2 : -3.07542224
10 day(s) Pearson r : 0.00351051
10 day(s) QLIKE : 6.81972014
20 day(s) MAE : 0.07199732
20 day(s) RMSE : 0.08273224
20 day(s) R2 : -3.11541830
20 day(s) Pearson r : 0.00519323
20 day(s) QLIKE : 5.10378877
full horizon MAE : 0.07199732
full horizon RMSE : 0.08273224
full horizon R2 : -3.11541830
full horizon Pearson r : 0.00519323
full horizon QLIKE : 5.10378877
--- Task 3 ---
1 day(s) MAE : 0.32307302
1 day(s) RMSE : 0.35731368
1 day(s) R2 : -0.00851361
1 day(s) Pearson r : -0.06487902
1 day(s) QLIKE : 6.83839007
3 day(s) MAE : 0.32574428
3 day(s) RMSE : 0.36503071
3 day(s) R2 : -0.05249424
3 day(s) Pearson r : -0.00664508
3 day(s) QLIKE : 6.84090282
5 day(s) MAE : 0.32569589
5 day(s) RMSE : 0.36541725
5 day(s) R2 : -0.05468321
5 day(s) Pearson r : -0.00087579
5 day(s) QLIKE : 6.84116715
10 day(s) MAE : 0.32479314
10 day(s) RMSE : 0.36492222
10 day(s) R2 : -0.05200761
10 day(s) Pearson r : 0.02194557
10 day(s) QLIKE : 6.84101756
20 day(s) MAE : 0.32542892
20 day(s) RMSE : 0.36523767
20 day(s) R2 : -0.05328929
20 day(s) Pearson r : 0.01182756
20 day(s) QLIKE : 6.84194674
full horizon MAE : 0.32542892
full horizon RMSE : 0.36523767
full horizon R2 : -0.05328929
full horizon Pearson r : 0.01182756
full horizon QLIKE : 6.84194674
--- Task 4 ---
1 day(s) MAE : 0.96436980
1 day(s) RMSE : 1.18956516
1 day(s) R2 : -1.66231532
1 day(s) Pearson r : -0.01803279
1 day(s) QLIKE : 0.37018160
3 day(s) MAE : 1.01819352
3 day(s) RMSE : 1.24189180
3 day(s) R2 : -1.90498175
3 day(s) Pearson r : 0.00012404
3 day(s) QLIKE : 0.42091940
5 day(s) MAE : 1.05046637
5 day(s) RMSE : 1.27238385
5 day(s) R2 : -2.05282709
5 day(s) Pearson r : 0.00015178
5 day(s) QLIKE : 0.50423131
10 day(s) MAE : 1.08994967
10 day(s) RMSE : 1.30799188
10 day(s) R2 : -2.23499478
10 day(s) Pearson r : -0.00154953
10 day(s) QLIKE : 0.67390053
20 day(s) MAE : 1.11711669
20 day(s) RMSE : 1.33109570
20 day(s) R2 : -2.36754780
20 day(s) Pearson r : -0.00301744
20 day(s) QLIKE : 0.66052151
full horizon MAE : 1.11711669
full horizon RMSE : 1.33109570
full horizon R2 : -2.36754780
full horizon Pearson r : -0.00301744
full horizon QLIKE : 0.66052151
--- Task 5 ---
1 day(s) MAE : 0.05242829
1 day(s) RMSE : 0.06648523
1 day(s) R2 : -1.63597794
1 day(s) Pearson r : 0.08516192
1 day(s) QLIKE : 5.74001161
3 day(s) MAE : 0.05262091
3 day(s) RMSE : 0.06660075
3 day(s) R2 : -1.65397220
3 day(s) Pearson r : 0.02330990
3 day(s) QLIKE : 5.69158093
5 day(s) MAE : 0.05284613
5 day(s) RMSE : 0.06675824
5 day(s) R2 : -1.67328789
5 day(s) Pearson r : 0.00032298
5 day(s) QLIKE : 6.22868637
10 day(s) MAE : 0.05336735
10 day(s) RMSE : 0.06709344
10 day(s) R2 : -1.71990928
10 day(s) Pearson r : -0.00927312
10 day(s) QLIKE : 7.74703897
20 day(s) MAE : 0.05426103
20 day(s) RMSE : 0.06758356
20 day(s) R2 : -1.81243538
20 day(s) Pearson r : -0.01633503
20 day(s) QLIKE : 7.06541674
full horizon MAE : 0.05426103
full horizon RMSE : 0.06758356
full horizon R2 : -1.81243538
full horizon Pearson r : -0.01633503
full horizon QLIKE : 7.06541674
--- Task 6 ---
1 day(s) MAE : 2.25717880
1 day(s) RMSE : 2.46874799
1 day(s) R2 : -4.72872572
1 day(s) Pearson r : 0.01429237
1 day(s) QLIKE : 0.02064932
3 day(s) MAE : 2.01612684
3 day(s) RMSE : 2.25915029
3 day(s) R2 : -3.76668898
3 day(s) Pearson r : -0.05516634
3 day(s) QLIKE : 0.02158987
5 day(s) MAE : 2.39744499
5 day(s) RMSE : 2.69881179
5 day(s) R2 : -5.76332638
5 day(s) Pearson r : -0.03267971
5 day(s) QLIKE : 0.02534568
10 day(s) MAE : 3.79945258
10 day(s) RMSE : 4.23268176
10 day(s) R2 : -15.36293058
10 day(s) Pearson r : -0.03267159
10 day(s) QLIKE : 0.03813104
20 day(s) MAE : 6.20525741
20 day(s) RMSE : 6.85946247
20 day(s) R2 : -40.62835022
20 day(s) Pearson r : -0.04584378
20 day(s) QLIKE : 0.05965078
full horizon MAE : 6.20525741
full horizon RMSE : 6.85946247
full horizon R2 : -40.62835022
full horizon Pearson r : -0.04584378
full horizon QLIKE : 0.05965078
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BAC/Custom_KAN_LSTM_H20.pkl
Saved y_true min=0.587715, max=111.472
Saved y_pred min=2.52635, max=3.26592
=== C | H=1 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 21.8091406863669
Min value: -0.37248441284063266
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.256737301866204
Min value: -7.108340099155793
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.018765610501212
Min value: -0.35593837785433546
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.6474166417153167
Min value: -1.6879442751977214
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.615585669042774
Min value: -0.3593653801213122
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.113246907180985
Min value: -7.108340099155793
Epoch 001 | phase=1 | train_loss=5.0056 | val_main=0.488385
Epoch 002 | phase=1 | train_loss=4.8877 | val_main=0.488343
Epoch 003 | phase=1 | train_loss=4.8845 | val_main=0.488344
Epoch 004 | phase=1 | train_loss=4.5950 | val_main=0.488368
Epoch 005 | phase=1 | train_loss=4.5100 | val_main=0.488407
Epoch 006 | phase=1 | train_loss=4.6296 | val_main=0.488445
Epoch 007 | phase=1 | train_loss=4.3200 | val_main=0.488430
Epoch 008 | phase=1 | train_loss=4.2823 | val_main=0.488468
Epoch 009 | phase=1 | train_loss=4.3086 | val_main=0.488463
Epoch 010 | phase=1 | train_loss=4.1987 | val_main=0.488463
Epoch 011 | phase=1 | train_loss=4.1276 | val_main=0.488523
Epoch 012 | phase=1 | train_loss=4.1219 | val_main=0.488492
Epoch 013 | phase=1 | train_loss=3.8886 | val_main=0.488451
Epoch 014 | phase=1 | train_loss=3.8819 | val_main=0.488340
Epoch 015 | phase=1 | train_loss=3.9105 | val_main=0.488411
Epoch 016 | phase=0 | train_loss=4.8173 | val_main=0.472623
Epoch 017 | phase=0 | train_loss=4.5359 | val_main=0.439199
Epoch 018 | phase=0 | train_loss=4.3766 | val_main=0.422840
Epoch 019 | phase=0 | train_loss=4.3872 | val_main=0.443229
Epoch 020 | phase=0 | train_loss=4.3786 | val_main=0.427480
Epoch 021 | phase=0 | train_loss=4.2194 | val_main=0.419022
Epoch 022 | phase=0 | train_loss=4.1837 | val_main=0.419580
Epoch 023 | phase=0 | train_loss=4.1226 | val_main=0.414117
Epoch 024 | phase=0 | train_loss=3.9117 | val_main=0.402422
Epoch 025 | phase=0 | train_loss=3.8785 | val_main=0.411370
Epoch 026 | phase=0 | train_loss=4.0096 | val_main=0.396730
Epoch 027 | phase=0 | train_loss=3.7312 | val_main=0.393099
Epoch 028 | phase=0 | train_loss=3.7006 | val_main=0.392369
Epoch 029 | phase=0 | train_loss=3.4928 | val_main=0.384685
Epoch 030 | phase=0 | train_loss=3.3388 | val_main=0.387590
Epoch 031 | phase=2 | train_loss=0.3218 | val_main=0.382467
Epoch 032 | phase=2 | train_loss=0.3218 | val_main=0.378817
Epoch 033 | phase=2 | train_loss=0.3202 | val_main=0.383628
Epoch 034 | phase=2 | train_loss=0.3078 | val_main=0.387886
Epoch 035 | phase=2 | train_loss=0.3053 | val_main=0.385183
Epoch 036 | phase=2 | train_loss=0.3109 | val_main=0.388244
Epoch 037 | phase=2 | train_loss=0.2942 | val_main=0.374739
Epoch 038 | phase=2 | train_loss=0.2915 | val_main=0.380514
Epoch 039 | phase=2 | train_loss=0.2985 | val_main=0.397301
Epoch 040 | phase=2 | train_loss=0.2885 | val_main=0.380675
Epoch 041 | phase=2 | train_loss=0.2899 | val_main=0.380754
Epoch 042 | phase=2 | train_loss=0.2884 | val_main=0.381789
Epoch 043 | phase=2 | train_loss=0.2735 | val_main=0.396656
Epoch 044 | phase=2 | train_loss=0.2683 | val_main=0.407307
Epoch 045 | phase=2 | train_loss=0.2709 | val_main=0.390572
Epoch 046 | phase=2 | train_loss=0.2616 | val_main=0.407675
Epoch 047 | phase=2 | train_loss=0.2425 | val_main=0.402662
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 6
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.97808843
1 day(s) RMSE : 8.97214962
1 day(s) R2 : 0.09649050
1 day(s) Pearson r : 0.48336487
1 day(s) QLIKE : 0.27438794
full horizon MAE : 2.97808843
full horizon RMSE : 8.97214962
full horizon R2 : 0.09649050
full horizon Pearson r : 0.48336487
full horizon QLIKE : 0.27438794
--- Task 2 ---
1 day(s) MAE : 0.05938099
1 day(s) RMSE : 0.08202204
1 day(s) R2 : -4.43107542
1 day(s) Pearson r : -0.13146192
1 day(s) QLIKE : 18.09899247
full horizon MAE : 0.05938099
full horizon RMSE : 0.08202204
full horizon R2 : -4.43107542
full horizon Pearson r : -0.13146192
full horizon QLIKE : 18.09899247
--- Task 3 ---
1 day(s) MAE : 0.33854043
1 day(s) RMSE : 0.45211198
1 day(s) R2 : -0.30033083
1 day(s) Pearson r : 0.10694129
1 day(s) QLIKE : 8.10399153
full horizon MAE : 0.33854043
full horizon RMSE : 0.45211198
full horizon R2 : -0.30033083
full horizon Pearson r : 0.10694129
full horizon QLIKE : 8.10399153
--- Task 4 ---
1 day(s) MAE : 0.86596509
1 day(s) RMSE : 1.14621760
1 day(s) R2 : -0.65183380
1 day(s) Pearson r : 0.16132257
1 day(s) QLIKE : 1.88000620
full horizon MAE : 0.86596509
full horizon RMSE : 1.14621760
full horizon R2 : -0.65183380
full horizon Pearson r : 0.16132257
full horizon QLIKE : 1.88000620
--- Task 5 ---
1 day(s) MAE : 0.08753991
1 day(s) RMSE : 0.10423969
1 day(s) R2 : -1.95152429
1 day(s) Pearson r : -0.15915185
1 day(s) QLIKE : 17.71438864
full horizon MAE : 0.08753991
full horizon RMSE : 0.10423969
full horizon R2 : -1.95152429
full horizon Pearson r : -0.15915185
full horizon QLIKE : 17.71438864
--- Task 6 ---
1 day(s) MAE : 7.05709593
1 day(s) RMSE : 21.29746208
1 day(s) R2 : -56.32277766
1 day(s) Pearson r : -0.00291057
1 day(s) QLIKE : 0.17337443
full horizon MAE : 7.05709593
full horizon RMSE : 21.29746208
full horizon R2 : -56.32277766
full horizon Pearson r : -0.00291057
full horizon QLIKE : 0.17337443
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/C/Custom_KAN_H1.pkl
Saved y_true min=0.525001, max=134.647
Saved y_pred min=1.10925, max=86.6316
=== C | H=1 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 21.8091406863669
Min value: -0.37248441284063266
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.256737301866204
Min value: -7.108340099155793
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.018765610501212
Min value: -0.35593837785433546
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.6474166417153167
Min value: -1.6879442751977214
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.615585669042774
Min value: -0.3593653801213122
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.113246907180985
Min value: -7.108340099155793
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 161s 514ms/step - loss: 1.0165 - val_loss: 0.6904 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 191ms/step - loss: 0.9739 - val_loss: 0.4960 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 10s 217ms/step - loss: 0.9275 - val_loss: 0.5271 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 203ms/step - loss: 0.9216 - val_loss: 0.4732 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 186ms/step - loss: 0.8982 - val_loss: 0.4854 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 189ms/step - loss: 0.8941 - val_loss: 0.4349 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 192ms/step - loss: 0.8881 - val_loss: 0.4334 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 190ms/step - loss: 0.8785 - val_loss: 0.4261 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 191ms/step - loss: 0.8745 - val_loss: 0.4122 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 187ms/step - loss: 0.8682 - val_loss: 0.4108 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 188ms/step - loss: 0.8666 - val_loss: 0.4142 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=12 tf_ratio=0.421 -> TF=ON
Epoch 12/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 188ms/step - loss: 0.8614 - val_loss: 0.4215 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=13 tf_ratio=0.368 -> TF=ON
Epoch 13/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 187ms/step - loss: 0.8917 - val_loss: 0.4484 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=14 tf_ratio=0.316 -> TF=ON
Epoch 14/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 199ms/step - loss: 0.8729 - val_loss: 0.4046 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=15 tf_ratio=0.263 -> TF=ON
Epoch 15/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 204ms/step - loss: 0.8582 - val_loss: 0.4250 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=16 tf_ratio=0.211 -> TF=ON
Epoch 16/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 203ms/step - loss: 0.8570 - val_loss: 0.4041 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=17 tf_ratio=0.158 -> TF=ON
Epoch 17/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 200ms/step - loss: 0.8634 - val_loss: 0.5353 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=18 tf_ratio=0.105 -> TF=ON
Epoch 18/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 10s 208ms/step - loss: 0.9469 - val_loss: 0.5095 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=19 tf_ratio=0.053 -> TF=ON
Epoch 19/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 9s 199ms/step - loss: 0.9154 - val_loss: 0.4584 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=20 tf_ratio=0.000 -> TF=OFF
Epoch 20/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 10s 211ms/step - loss: 0.8899 - val_loss: 0.4230 - learning_rate: 5.0000e-04
Restoring model weights from the end of the best epoch: 16.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 1
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.71354516
1 day(s) RMSE : 8.55226051
1 day(s) R2 : 0.17907865
1 day(s) Pearson r : 0.47996262
1 day(s) QLIKE : 0.31168247
full horizon MAE : 2.71354516
full horizon RMSE : 8.55226051
full horizon R2 : 0.17907865
full horizon Pearson r : 0.47996262
full horizon QLIKE : 0.31168247
--- Task 2 ---
1 day(s) MAE : 0.04754049
1 day(s) RMSE : 0.05922844
1 day(s) R2 : -1.83194742
1 day(s) Pearson r : 0.12100419
1 day(s) QLIKE : 6.53467860
full horizon MAE : 0.04754049
full horizon RMSE : 0.05922844
full horizon R2 : -1.83194742
full horizon Pearson r : 0.12100419
full horizon QLIKE : 6.53467860
--- Task 3 ---
1 day(s) MAE : 0.40761475
1 day(s) RMSE : 0.45700569
1 day(s) R2 : -0.32863302
1 day(s) Pearson r : -0.07555107
1 day(s) QLIKE : 8.25434363
full horizon MAE : 0.40761475
full horizon RMSE : 0.45700569
full horizon R2 : -0.32863302
full horizon Pearson r : -0.07555107
full horizon QLIKE : 8.25434363
--- Task 4 ---
1 day(s) MAE : 0.90181846
1 day(s) RMSE : 1.24244526
1 day(s) R2 : -0.94082656
1 day(s) Pearson r : 0.19495360
1 day(s) QLIKE : 0.74399784
full horizon MAE : 0.90181846
full horizon RMSE : 1.24244526
full horizon R2 : -0.94082656
full horizon Pearson r : 0.19495360
full horizon QLIKE : 0.74399784
--- Task 5 ---
1 day(s) MAE : 0.09356806
1 day(s) RMSE : 0.11114221
1 day(s) R2 : -2.35535254
1 day(s) Pearson r : -0.28737256
1 day(s) QLIKE : 7.94342332
full horizon MAE : 0.09356806
full horizon RMSE : 0.11114221
full horizon R2 : -2.35535254
full horizon Pearson r : -0.28737256
full horizon QLIKE : 7.94342332
--- Task 6 ---
1 day(s) MAE : 2.58831605
1 day(s) RMSE : 3.37535219
1 day(s) R2 : -0.43982499
1 day(s) Pearson r : -0.10458086
1 day(s) QLIKE : 0.09655576
full horizon MAE : 2.58831605
full horizon RMSE : 3.37535219
full horizon R2 : -0.43982499
full horizon Pearson r : -0.10458086
full horizon QLIKE : 0.09655576
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/C/Custom_KAN_LSTM_H1.pkl
Saved y_true min=0.525001, max=134.647
Saved y_pred min=1.55051, max=24.4493
=== C | H=5 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 21.8091406863669
Min value: -0.37248441284063266
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.257460407279653
Min value: -7.1082338908403395
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.018765610501212
Min value: -0.35593837785433546
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.645753754809614
Min value: -1.6819253820445499
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.615585669042774
Min value: -0.3593653801213122
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.110793676955011
Min value: -7.1082338908403395
Epoch 001 | phase=1 | train_loss=5.1348 | val_main=0.481562
Epoch 002 | phase=1 | train_loss=4.8754 | val_main=0.481572
Epoch 003 | phase=1 | train_loss=4.9494 | val_main=0.481574
Epoch 004 | phase=1 | train_loss=4.6809 | val_main=0.481574
Epoch 005 | phase=1 | train_loss=4.4876 | val_main=0.481596
Epoch 006 | phase=1 | train_loss=4.4977 | val_main=0.481579
Epoch 007 | phase=1 | train_loss=4.3906 | val_main=0.481586
Epoch 008 | phase=1 | train_loss=4.3351 | val_main=0.481553
Epoch 009 | phase=1 | train_loss=4.2749 | val_main=0.481590
Epoch 010 | phase=1 | train_loss=4.1970 | val_main=0.481591
Epoch 011 | phase=1 | train_loss=4.1502 | val_main=0.481571
Epoch 012 | phase=1 | train_loss=4.0699 | val_main=0.481575
Epoch 013 | phase=1 | train_loss=3.8895 | val_main=0.481574
Epoch 014 | phase=1 | train_loss=3.8299 | val_main=0.481596
Epoch 015 | phase=1 | train_loss=3.7789 | val_main=0.481558
Epoch 016 | phase=0 | train_loss=4.7464 | val_main=0.471777
Epoch 017 | phase=0 | train_loss=4.4867 | val_main=0.448215
Epoch 018 | phase=0 | train_loss=4.3209 | val_main=0.429790
Epoch 019 | phase=0 | train_loss=4.1133 | val_main=0.467571
Epoch 020 | phase=0 | train_loss=3.9313 | val_main=0.448557
Epoch 021 | phase=0 | train_loss=3.7340 | val_main=0.444195
Epoch 022 | phase=0 | train_loss=3.7076 | val_main=0.474255
Epoch 023 | phase=0 | train_loss=3.8438 | val_main=0.462772
Epoch 024 | phase=0 | train_loss=3.4673 | val_main=0.459105
Epoch 025 | phase=0 | train_loss=3.3287 | val_main=0.446839
Epoch 026 | phase=0 | train_loss=3.2801 | val_main=0.449734
Epoch 027 | phase=0 | train_loss=3.0986 | val_main=0.450819
Epoch 028 | phase=0 | train_loss=3.0742 | val_main=0.441678
Epoch 029 | phase=0 | train_loss=2.9843 | val_main=0.447876
Epoch 030 | phase=0 | train_loss=2.8416 | val_main=0.451159
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 30
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.81258959
1 day(s) RMSE : 9.35761595
1 day(s) R2 : 0.01718870
1 day(s) Pearson r : 0.27578490
1 day(s) QLIKE : 0.36929527
3 day(s) MAE : 2.87125284
3 day(s) RMSE : 9.45231513
3 day(s) R2 : 0.00208583
3 day(s) Pearson r : 0.19698482
3 day(s) QLIKE : 0.39813070
5 day(s) MAE : 2.90324622
5 day(s) RMSE : 9.52228146
5 day(s) R2 : -0.00951808
5 day(s) Pearson r : 0.14233115
5 day(s) QLIKE : 0.41698181
full horizon MAE : 2.90324622
full horizon RMSE : 9.52228146
full horizon R2 : -0.00951808
full horizon Pearson r : 0.14233115
full horizon QLIKE : 0.41698181
--- Task 2 ---
1 day(s) MAE : 0.04206224
1 day(s) RMSE : 0.05136770
1 day(s) R2 : -1.13012434
1 day(s) Pearson r : 0.15102364
1 day(s) QLIKE : 11.41834592
3 day(s) MAE : 0.04244987
3 day(s) RMSE : 0.05219884
3 day(s) R2 : -1.19749764
3 day(s) Pearson r : 0.14261654
3 day(s) QLIKE : 11.41596392
5 day(s) MAE : 0.04275618
5 day(s) RMSE : 0.05290557
5 day(s) R2 : -1.25596257
5 day(s) Pearson r : 0.13970412
5 day(s) QLIKE : 11.54016127
full horizon MAE : 0.04275618
full horizon RMSE : 0.05290557
full horizon R2 : -1.25596257
full horizon Pearson r : 0.13970412
full horizon QLIKE : 11.54016127
--- Task 3 ---
1 day(s) MAE : 0.36425044
1 day(s) RMSE : 0.47223992
1 day(s) R2 : -0.41868905
1 day(s) Pearson r : -0.03455183
1 day(s) QLIKE : 8.11384863
3 day(s) MAE : 0.36026840
3 day(s) RMSE : 0.46306724
3 day(s) R2 : -0.36548531
3 day(s) Pearson r : 0.05272144
3 day(s) QLIKE : 8.10812927
5 day(s) MAE : 0.36028695
5 day(s) RMSE : 0.46244175
5 day(s) R2 : -0.36290629
5 day(s) Pearson r : 0.05727163
5 day(s) QLIKE : 8.10779243
full horizon MAE : 0.36028695
full horizon RMSE : 0.46244175
full horizon R2 : -0.36290629
full horizon Pearson r : 0.05727163
full horizon QLIKE : 8.10779243
--- Task 4 ---
1 day(s) MAE : 0.89649474
1 day(s) RMSE : 1.17508840
1 day(s) R2 : -0.73609417
1 day(s) Pearson r : -0.00219743
1 day(s) QLIKE : 1.53954155
3 day(s) MAE : 0.89545139
3 day(s) RMSE : 1.17317134
3 day(s) R2 : -0.73390448
3 day(s) Pearson r : 0.00934464
3 day(s) QLIKE : 1.48551277
5 day(s) MAE : 0.89650666
5 day(s) RMSE : 1.17484503
5 day(s) R2 : -0.74209832
5 day(s) Pearson r : 0.01075368
5 day(s) QLIKE : 1.49397613
full horizon MAE : 0.89650666
full horizon RMSE : 1.17484503
full horizon R2 : -0.74209832
full horizon Pearson r : 0.01075368
full horizon QLIKE : 1.49397613
--- Task 5 ---
1 day(s) MAE : 0.09159247
1 day(s) RMSE : 0.10872541
1 day(s) R2 : -2.21101416
1 day(s) Pearson r : -0.18877933
1 day(s) QLIKE : 17.29055009
3 day(s) MAE : 0.09194021
3 day(s) RMSE : 0.10897440
3 day(s) R2 : -2.24261361
3 day(s) Pearson r : -0.14620361
3 day(s) QLIKE : 17.21749604
5 day(s) MAE : 0.09244945
5 day(s) RMSE : 0.10937557
5 day(s) R2 : -2.28274109
5 day(s) Pearson r : -0.14966856
5 day(s) QLIKE : 17.12659951
full horizon MAE : 0.09244945
full horizon RMSE : 0.10937557
full horizon R2 : -2.28274109
full horizon Pearson r : -0.14966856
full horizon QLIKE : 17.12659951
--- Task 6 ---
1 day(s) MAE : 6.05820615
1 day(s) RMSE : 12.67037239
1 day(s) R2 : -19.28851580
1 day(s) Pearson r : -0.01374934
1 day(s) QLIKE : 0.17782813
3 day(s) MAE : 6.01609116
3 day(s) RMSE : 12.67439291
3 day(s) R2 : -19.23688495
3 day(s) Pearson r : -0.01619856
3 day(s) QLIKE : 0.17729891
5 day(s) MAE : 5.99097351
5 day(s) RMSE : 12.54791814
5 day(s) R2 : -18.77164835
5 day(s) Pearson r : -0.01816486
5 day(s) QLIKE : 0.17645853
full horizon MAE : 5.99097351
full horizon RMSE : 12.54791814
full horizon R2 : -18.77164835
full horizon Pearson r : -0.01816486
full horizon QLIKE : 0.17645853
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/C/Custom_KAN_H5.pkl
Saved y_true min=0.525001, max=134.647
Saved y_pred min=2.41624, max=8.33239
=== C | H=5 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 21.8091406863669
Min value: -0.37248441284063266
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.257460407279653
Min value: -7.1082338908403395
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.018765610501212
Min value: -0.35593837785433546
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.645753754809614
Min value: -1.6819253820445499
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.615585669042774
Min value: -0.3593653801213122
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.110793676955011
Min value: -7.1082338908403395
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 160s 472ms/step - loss: 0.8441 - val_loss: 0.6285 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 10s 208ms/step - loss: 0.5623 - val_loss: 0.4711 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 244ms/step - loss: 0.4862 - val_loss: 0.5405 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 249ms/step - loss: 0.4461 - val_loss: 0.5599 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 242ms/step - loss: 0.4282 - val_loss: 0.6426 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 245ms/step - loss: 0.4234 - val_loss: 0.5290 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 239ms/step - loss: 0.4157
Epoch 7: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 246ms/step - loss: 0.4070 - val_loss: 0.5497 - learning_rate: 5.0000e-04
Epoch 7: early stopping
Restoring model weights from the end of the best epoch: 2.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 5
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.69396727
1 day(s) RMSE : 9.48835959
1 day(s) R2 : -0.01046663
1 day(s) Pearson r : 0.36704942
1 day(s) QLIKE : 0.36577407
3 day(s) MAE : 2.75971011
3 day(s) RMSE : 9.55402852
3 day(s) R2 : -0.01950620
3 day(s) Pearson r : 0.27247095
3 day(s) QLIKE : 0.37428451
5 day(s) MAE : 2.85310445
5 day(s) RMSE : 9.66477620
5 day(s) R2 : -0.03995770
5 day(s) Pearson r : 0.17985183
5 day(s) QLIKE : 0.40507111
full horizon MAE : 2.85310445
full horizon RMSE : 9.66477620
full horizon R2 : -0.03995770
full horizon Pearson r : 0.17985183
full horizon QLIKE : 0.40507111
--- Task 2 ---
1 day(s) MAE : 0.04761363
1 day(s) RMSE : 0.05878679
1 day(s) R2 : -1.78987143
1 day(s) Pearson r : -0.20715884
1 day(s) QLIKE : 6.34240586
3 day(s) MAE : 0.04791912
3 day(s) RMSE : 0.05931054
3 day(s) R2 : -1.83707212
3 day(s) Pearson r : -0.00698003
3 day(s) QLIKE : 12.67930482
5 day(s) MAE : 0.04802197
5 day(s) RMSE : 0.05946244
5 day(s) R2 : -1.84980047
5 day(s) Pearson r : -0.00549590
5 day(s) QLIKE : 14.28998983
full horizon MAE : 0.04802197
full horizon RMSE : 0.05946244
full horizon R2 : -1.84980047
full horizon Pearson r : -0.00549590
full horizon QLIKE : 14.28998983
--- Task 3 ---
1 day(s) MAE : 0.46502720
1 day(s) RMSE : 0.50976105
1 day(s) R2 : -0.65308475
1 day(s) Pearson r : -0.07147644
1 day(s) QLIKE : 8.11236399
3 day(s) MAE : 0.51199517
3 day(s) RMSE : 0.60672156
3 day(s) R2 : -1.34410885
3 day(s) Pearson r : -0.00726741
3 day(s) QLIKE : 8.89525420
5 day(s) MAE : 0.52587318
5 day(s) RMSE : 0.63448540
5 day(s) R2 : -1.56563682
5 day(s) Pearson r : -0.00232908
5 day(s) QLIKE : 9.56655877
full horizon MAE : 0.52587318
full horizon RMSE : 0.63448540
full horizon R2 : -1.56563682
full horizon Pearson r : -0.00232908
full horizon QLIKE : 9.56655877
--- Task 4 ---
1 day(s) MAE : 0.89898252
1 day(s) RMSE : 1.24035384
1 day(s) R2 : -0.93429804
1 day(s) Pearson r : -0.06987622
1 day(s) QLIKE : 0.75891139
3 day(s) MAE : 0.91167226
3 day(s) RMSE : 1.25578999
3 day(s) R2 : -0.98671839
3 day(s) Pearson r : -0.00645656
3 day(s) QLIKE : 0.76808254
5 day(s) MAE : 0.90780847
5 day(s) RMSE : 1.24942050
5 day(s) R2 : -0.97028362
5 day(s) Pearson r : -0.01572968
5 day(s) QLIKE : 0.80512244
full horizon MAE : 0.90780847
full horizon RMSE : 1.24942050
full horizon R2 : -0.97028362
full horizon Pearson r : -0.01572968
full horizon QLIKE : 0.80512244
--- Task 5 ---
1 day(s) MAE : 0.09541525
1 day(s) RMSE : 0.11306706
1 day(s) R2 : -2.47258008
1 day(s) Pearson r : 0.01627748
1 day(s) QLIKE : 2.98989303
3 day(s) MAE : 0.09554367
3 day(s) RMSE : 0.11304494
3 day(s) R2 : -2.48938170
3 day(s) Pearson r : -0.03638540
3 day(s) QLIKE : 5.49485270
5 day(s) MAE : 0.08704359
5 day(s) RMSE : 0.10455213
5 day(s) R2 : -1.99958867
5 day(s) Pearson r : -0.04870840
5 day(s) QLIKE : 18.76738862
full horizon MAE : 0.08704359
full horizon RMSE : 0.10455213
full horizon R2 : -1.99958867
full horizon Pearson r : -0.04870840
full horizon QLIKE : 18.76738862
--- Task 6 ---
1 day(s) MAE : 2.45225463
1 day(s) RMSE : 2.98823339
1 day(s) R2 : -0.12849736
1 day(s) Pearson r : -0.15384484
1 day(s) QLIKE : 0.07527270
3 day(s) MAE : 2.39435286
3 day(s) RMSE : 3.11571541
3 day(s) R2 : -0.22293928
3 day(s) Pearson r : -0.12518770
3 day(s) QLIKE : 0.08531419
5 day(s) MAE : 2.37420276
5 day(s) RMSE : 3.25329064
5 day(s) R2 : -0.32906203
5 day(s) Pearson r : -0.12376945
5 day(s) QLIKE : 0.09381364
full horizon MAE : 2.37420276
full horizon RMSE : 3.25329064
full horizon R2 : -0.32906203
full horizon Pearson r : -0.12376945
full horizon QLIKE : 0.09381364
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/C/Custom_KAN_LSTM_H5.pkl
Saved y_true min=0.525001, max=134.647
Saved y_pred min=1.90066, max=5.50519
=== C | H=10 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 21.8091406863669
Min value: -0.37248441284063266
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.258708812864733
Min value: -7.108083650974834
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.018765610501212
Min value: -0.35593837785433546
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.6439291882044116
Min value: -1.6744596134591672
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.615585669042774
Min value: -0.3593653801213122
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.108415972443237
Min value: -7.108083650974834
Epoch 001 | phase=1 | train_loss=5.1072 | val_main=0.475461
Epoch 002 | phase=1 | train_loss=4.8907 | val_main=0.475508
Epoch 003 | phase=1 | train_loss=4.9400 | val_main=0.475549
Epoch 004 | phase=1 | train_loss=4.7280 | val_main=0.475528
Epoch 005 | phase=1 | train_loss=4.5189 | val_main=0.475489
Epoch 006 | phase=1 | train_loss=4.5013 | val_main=0.475496
Epoch 007 | phase=1 | train_loss=4.4361 | val_main=0.475449
Epoch 008 | phase=1 | train_loss=4.3720 | val_main=0.475415
Epoch 009 | phase=1 | train_loss=4.2968 | val_main=0.475425
Epoch 010 | phase=1 | train_loss=4.2373 | val_main=0.475434
Epoch 011 | phase=1 | train_loss=4.1423 | val_main=0.475399
Epoch 012 | phase=1 | train_loss=4.1129 | val_main=0.475491
Epoch 013 | phase=1 | train_loss=3.8901 | val_main=0.475497
Epoch 014 | phase=1 | train_loss=3.8268 | val_main=0.475574
Epoch 015 | phase=1 | train_loss=3.7647 | val_main=0.475539
Epoch 016 | phase=0 | train_loss=4.7099 | val_main=0.468047
Epoch 017 | phase=0 | train_loss=4.4455 | val_main=0.447530
Epoch 018 | phase=0 | train_loss=4.3422 | val_main=0.432141
Epoch 019 | phase=0 | train_loss=4.0866 | val_main=0.481243
Epoch 020 | phase=0 | train_loss=3.9383 | val_main=0.461131
Epoch 021 | phase=0 | train_loss=3.7340 | val_main=0.446223
Epoch 022 | phase=0 | train_loss=3.6728 | val_main=0.469967
Epoch 023 | phase=0 | train_loss=3.7023 | val_main=0.474429
Epoch 024 | phase=0 | train_loss=3.4589 | val_main=0.488177
Epoch 025 | phase=0 | train_loss=3.3468 | val_main=0.491572
Epoch 026 | phase=0 | train_loss=3.3162 | val_main=0.495334
Epoch 027 | phase=0 | train_loss=3.1559 | val_main=0.501951
Epoch 028 | phase=0 | train_loss=3.0838 | val_main=0.504740
Epoch 029 | phase=0 | train_loss=2.9824 | val_main=0.508911
Epoch 030 | phase=0 | train_loss=2.8697 | val_main=0.516703
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 60
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.75687601
1 day(s) RMSE : 9.41009338
1 day(s) R2 : 0.00613460
1 day(s) Pearson r : 0.30048067
1 day(s) QLIKE : 0.37109330
3 day(s) MAE : 2.80449502
3 day(s) RMSE : 9.49034508
3 day(s) R2 : -0.00596023
3 day(s) Pearson r : 0.22781027
3 day(s) QLIKE : 0.39325458
5 day(s) MAE : 2.84439045
5 day(s) RMSE : 9.55325999
5 day(s) R2 : -0.01609723
5 day(s) Pearson r : 0.15825624
5 day(s) QLIKE : 0.41146128
10 day(s) MAE : 2.88794081
10 day(s) RMSE : 9.61866700
10 day(s) R2 : -0.02765411
10 day(s) Pearson r : 0.08317723
10 day(s) QLIKE : 0.42968427
full horizon MAE : 2.88794081
full horizon RMSE : 9.61866700
full horizon R2 : -0.02765411
full horizon Pearson r : 0.08317723
full horizon QLIKE : 0.42968427
--- Task 2 ---
1 day(s) MAE : 0.04285748
1 day(s) RMSE : 0.05380755
1 day(s) R2 : -1.33728187
1 day(s) Pearson r : 0.13992955
1 day(s) QLIKE : 10.80045566
3 day(s) MAE : 0.04387259
3 day(s) RMSE : 0.05483868
3 day(s) R2 : -1.42538486
3 day(s) Pearson r : 0.12564812
3 day(s) QLIKE : 11.18085511
5 day(s) MAE : 0.04401007
5 day(s) RMSE : 0.05495928
5 day(s) R2 : -1.43450739
5 day(s) Pearson r : 0.12259989
5 day(s) QLIKE : 11.25653447
10 day(s) MAE : 0.04410025
10 day(s) RMSE : 0.05496162
10 day(s) R2 : -1.42859963
10 day(s) Pearson r : 0.12820534
10 day(s) QLIKE : 11.24008313
full horizon MAE : 0.04410025
full horizon RMSE : 0.05496162
full horizon R2 : -1.42859963
full horizon Pearson r : 0.12820534
full horizon QLIKE : 11.24008313
--- Task 3 ---
1 day(s) MAE : 0.35944364
1 day(s) RMSE : 0.46567036
1 day(s) R2 : -0.37949150
1 day(s) Pearson r : 0.01510768
1 day(s) QLIKE : 8.11134702
3 day(s) MAE : 0.35481170
3 day(s) RMSE : 0.46289419
3 day(s) R2 : -0.36446495
3 day(s) Pearson r : 0.07260277
3 day(s) QLIKE : 8.10500204
5 day(s) MAE : 0.35363441
5 day(s) RMSE : 0.45932601
5 day(s) R2 : -0.34460277
5 day(s) Pearson r : 0.09887697
5 day(s) QLIKE : 8.10300449
10 day(s) MAE : 0.35192149
10 day(s) RMSE : 0.45631530
10 day(s) R2 : -0.32973092
10 day(s) Pearson r : 0.10138083
10 day(s) QLIKE : 8.10233020
full horizon MAE : 0.35192149
full horizon RMSE : 0.45631530
full horizon R2 : -0.32973092
full horizon Pearson r : 0.10138083
full horizon QLIKE : 8.10233020
--- Task 4 ---
1 day(s) MAE : 0.89333479
1 day(s) RMSE : 1.16822661
1 day(s) R2 : -0.71587794
1 day(s) Pearson r : 0.00518326
1 day(s) QLIKE : 1.83567341
3 day(s) MAE : 0.89323022
3 day(s) RMSE : 1.16757208
3 day(s) R2 : -0.71739295
3 day(s) Pearson r : 0.01636380
3 day(s) QLIKE : 1.80769906
5 day(s) MAE : 0.89407502
5 day(s) RMSE : 1.16809848
5 day(s) R2 : -0.72214776
5 day(s) Pearson r : 0.02183845
5 day(s) QLIKE : 1.77303893
10 day(s) MAE : 0.89575092
10 day(s) RMSE : 1.17222413
10 day(s) R2 : -0.74156892
10 day(s) Pearson r : -0.00264865
10 day(s) QLIKE : 1.83906240
full horizon MAE : 0.89575092
full horizon RMSE : 1.17222413
full horizon R2 : -0.74156892
full horizon Pearson r : -0.00264865
full horizon QLIKE : 1.83906240
--- Task 5 ---
1 day(s) MAE : 0.09261480
1 day(s) RMSE : 0.10970537
1 day(s) R2 : -2.26915754
1 day(s) Pearson r : -0.11753302
1 day(s) QLIKE : 16.22674390
3 day(s) MAE : 0.09286057
3 day(s) RMSE : 0.10994249
3 day(s) R2 : -2.30048196
3 day(s) Pearson r : -0.09434213
3 day(s) QLIKE : 16.17299012
5 day(s) MAE : 0.09315099
5 day(s) RMSE : 0.11020612
5 day(s) R2 : -2.33278530
5 day(s) Pearson r : -0.07041597
5 day(s) QLIKE : 16.13134171
10 day(s) MAE : 0.09414490
10 day(s) RMSE : 0.11105979
10 day(s) R2 : -2.42566862
10 day(s) Pearson r : -0.04529818
10 day(s) QLIKE : 15.93317347
full horizon MAE : 0.09414490
full horizon RMSE : 0.11105979
full horizon R2 : -2.42566862
full horizon Pearson r : -0.04529818
full horizon QLIKE : 15.93317347
--- Task 6 ---
1 day(s) MAE : 8.57915605
1 day(s) RMSE : 16.84642485
1 day(s) R2 : -34.86633635
1 day(s) Pearson r : -0.02953881
1 day(s) QLIKE : 0.23078084
3 day(s) MAE : 8.81805706
3 day(s) RMSE : 17.42709524
3 day(s) R2 : -37.25950437
3 day(s) Pearson r : -0.03391761
3 day(s) QLIKE : 0.23319892
5 day(s) MAE : 8.86578581
5 day(s) RMSE : 17.51997630
5 day(s) R2 : -37.54486714
5 day(s) Pearson r : -0.03647334
5 day(s) QLIKE : 0.23257488
10 day(s) MAE : 8.77599253
10 day(s) RMSE : 17.17604598
10 day(s) R2 : -35.79102513
10 day(s) Pearson r : -0.03597465
10 day(s) QLIKE : 0.22894757
full horizon MAE : 8.77599253
full horizon RMSE : 17.17604598
full horizon R2 : -35.79102513
full horizon Pearson r : -0.03597465
full horizon QLIKE : 0.22894757
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/C/Custom_KAN_H10.pkl
Saved y_true min=0.525001, max=134.647
Saved y_pred min=2.28983, max=7.66594
=== C | H=10 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 21.8091406863669
Min value: -0.37248441284063266
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.258708812864733
Min value: -7.108083650974834
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.018765610501212
Min value: -0.35593837785433546
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.6439291882044116
Min value: -1.6744596134591672
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.615585669042774
Min value: -0.3593653801213122
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.108415972443237
Min value: -7.108083650974834
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 425s 404ms/step - loss: 0.8125 - val_loss: 0.5196 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 11s 246ms/step - loss: 0.4910 - val_loss: 0.5119 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 325ms/step - loss: 0.4164 - val_loss: 0.7699 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 320ms/step - loss: 0.3869 - val_loss: 0.7491 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 316ms/step - loss: 0.3731 - val_loss: 0.8067 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 315ms/step - loss: 0.3578 - val_loss: 0.7559 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 308ms/step - loss: 0.3544
Epoch 7: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 15s 317ms/step - loss: 0.3418 - val_loss: 0.7327 - learning_rate: 5.0000e-04
Epoch 7: early stopping
Restoring model weights from the end of the best epoch: 2.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 10
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.86444877
1 day(s) RMSE : 9.50768393
1 day(s) R2 : -0.01458673
1 day(s) Pearson r : 0.43263680
1 day(s) QLIKE : 0.42243525
3 day(s) MAE : 2.88638044
3 day(s) RMSE : 9.53737094
3 day(s) R2 : -0.01595425
3 day(s) Pearson r : 0.10195349
3 day(s) QLIKE : 0.42391406
5 day(s) MAE : 2.87337046
5 day(s) RMSE : 9.59963357
5 day(s) R2 : -0.02598588
5 day(s) Pearson r : 0.04277518
5 day(s) QLIKE : 0.43240128
10 day(s) MAE : 2.93984828
10 day(s) RMSE : 9.73968935
10 day(s) R2 : -0.05367674
10 day(s) Pearson r : 0.01033502
10 day(s) QLIKE : 0.45746464
full horizon MAE : 2.93984828
full horizon RMSE : 9.73968935
full horizon R2 : -0.05367674
full horizon Pearson r : 0.01033502
full horizon QLIKE : 0.45746464
--- Task 2 ---
1 day(s) MAE : 0.04784451
1 day(s) RMSE : 0.05922374
1 day(s) R2 : -1.83149804
1 day(s) Pearson r : 0.10112701
1 day(s) QLIKE : 6.32539975
3 day(s) MAE : 0.04801034
3 day(s) RMSE : 0.05948061
3 day(s) R2 : -1.85336630
3 day(s) Pearson r : 0.00977339
3 day(s) QLIKE : 12.80200919
5 day(s) MAE : 0.04807670
5 day(s) RMSE : 0.05956428
5 day(s) R2 : -1.85957073
5 day(s) Pearson r : 0.00623712
5 day(s) QLIKE : 12.16040223
10 day(s) MAE : 0.04820381
10 day(s) RMSE : 0.05971062
10 day(s) R2 : -1.86642172
10 day(s) Pearson r : 0.00307702
10 day(s) QLIKE : 9.65534967
full horizon MAE : 0.04820381
full horizon RMSE : 0.05971062
full horizon R2 : -1.86642172
full horizon Pearson r : 0.00307702
full horizon QLIKE : 9.65534967
--- Task 3 ---
1 day(s) MAE : 0.40512963
1 day(s) RMSE : 0.42586146
1 day(s) R2 : -0.15371489
1 day(s) Pearson r : 0.04719178
1 day(s) QLIKE : 8.10475013
3 day(s) MAE : 0.38944571
3 day(s) RMSE : 0.41317065
3 day(s) R2 : -0.08707088
3 day(s) Pearson r : -0.00855839
3 day(s) QLIKE : 8.10886338
5 day(s) MAE : 0.40165718
5 day(s) RMSE : 0.42833697
5 day(s) R2 : -0.16929218
5 day(s) Pearson r : -0.00356796
5 day(s) QLIKE : 8.12746397
10 day(s) MAE : 0.46473319
10 day(s) RMSE : 0.54422583
10 day(s) R2 : -0.89143748
10 day(s) Pearson r : 0.00320659
10 day(s) QLIKE : 11.65067107
full horizon MAE : 0.46473319
full horizon RMSE : 0.54422583
full horizon R2 : -0.89143748
full horizon Pearson r : 0.00320659
full horizon QLIKE : 11.65067107
--- Task 4 ---
1 day(s) MAE : 0.90721581
1 day(s) RMSE : 1.25122170
1 day(s) R2 : -0.96834280
1 day(s) Pearson r : -0.08311230
1 day(s) QLIKE : 0.75900153
3 day(s) MAE : 0.89455100
3 day(s) RMSE : 1.23283106
3 day(s) R2 : -0.91473824
3 day(s) Pearson r : -0.00234118
3 day(s) QLIKE : 0.77200297
5 day(s) MAE : 0.89424917
5 day(s) RMSE : 1.23100686
5 day(s) R2 : -0.91263651
5 day(s) Pearson r : -0.00378255
5 day(s) QLIKE : 0.76566025
10 day(s) MAE : 0.88722795
10 day(s) RMSE : 1.21705750
10 day(s) R2 : -0.87733401
10 day(s) Pearson r : -0.02560954
10 day(s) QLIKE : 0.78229871
full horizon MAE : 0.88722795
full horizon RMSE : 1.21705750
full horizon R2 : -0.87733401
full horizon Pearson r : -0.02560954
full horizon QLIKE : 0.78229871
--- Task 5 ---
1 day(s) MAE : 0.09537291
1 day(s) RMSE : 0.11302090
1 day(s) R2 : -2.46974543
1 day(s) Pearson r : 0.15699271
1 day(s) QLIKE : 2.97427285
3 day(s) MAE : 0.09433927
3 day(s) RMSE : 0.11173776
3 day(s) R2 : -2.40915004
3 day(s) Pearson r : 0.00111850
3 day(s) QLIKE : 4.07047820
5 day(s) MAE : 0.09020567
5 day(s) RMSE : 0.10706623
5 day(s) R2 : -2.14558145
5 day(s) Pearson r : 0.01019528
5 day(s) QLIKE : 4.75006086
10 day(s) MAE : 0.07386198
10 day(s) RMSE : 0.08982772
10 day(s) R2 : -1.24105436
10 day(s) Pearson r : 0.01710720
10 day(s) QLIKE : 5.56723877
full horizon MAE : 0.07386198
full horizon RMSE : 0.08982772
full horizon R2 : -1.24105436
full horizon Pearson r : 0.01710720
full horizon QLIKE : 5.56723877
--- Task 6 ---
1 day(s) MAE : 2.56234880
1 day(s) RMSE : 3.00519508
1 day(s) R2 : -0.14134478
1 day(s) Pearson r : -0.14606834
1 day(s) QLIKE : 0.07121525
3 day(s) MAE : 2.62162212
3 day(s) RMSE : 3.06274207
3 day(s) R2 : -0.18170801
3 day(s) Pearson r : -0.10533276
3 day(s) QLIKE : 0.07260597
5 day(s) MAE : 2.58102661
5 day(s) RMSE : 3.04259337
5 day(s) R2 : -0.16248499
5 day(s) Pearson r : -0.07014008
5 day(s) QLIKE : 0.07376350
10 day(s) MAE : 2.37507478
10 day(s) RMSE : 3.07596353
10 day(s) R2 : -0.17993440
10 day(s) Pearson r : -0.03443858
10 day(s) QLIKE : 0.08944535
full horizon MAE : 2.37507478
full horizon RMSE : 3.07596353
full horizon R2 : -0.17993440
full horizon Pearson r : -0.03443858
full horizon QLIKE : 0.08944535
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/C/Custom_KAN_LSTM_H10.pkl
Saved y_true min=0.525001, max=134.647
Saved y_pred min=1.9107, max=4.20924
=== C | H=20 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2018
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 21.8091406863669
Min value: -0.37248441284063266
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.261857345977599
Min value: -7.10781787362285
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.018765610501212
Min value: -0.35593837785433546
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.643530346347732
Min value: -1.6643841678427629
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.615585669042774
Min value: -0.3593653801213122
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.108493380986118
Min value: -7.10781787362285
Epoch 001 | phase=1 | train_loss=5.0594 | val_main=0.477759
Epoch 002 | phase=1 | train_loss=4.9460 | val_main=0.477816
Epoch 003 | phase=1 | train_loss=4.9455 | val_main=0.477896
Epoch 004 | phase=1 | train_loss=4.8750 | val_main=0.477904
Epoch 005 | phase=1 | train_loss=4.6277 | val_main=0.477783
Epoch 006 | phase=1 | train_loss=4.5487 | val_main=0.477868
Epoch 007 | phase=1 | train_loss=4.4581 | val_main=0.477752
Epoch 008 | phase=1 | train_loss=4.4056 | val_main=0.477741
Epoch 009 | phase=1 | train_loss=4.3175 | val_main=0.477828
Epoch 010 | phase=1 | train_loss=4.2595 | val_main=0.477648
Epoch 011 | phase=1 | train_loss=4.1199 | val_main=0.477672
Epoch 012 | phase=1 | train_loss=4.1041 | val_main=0.477792
Epoch 013 | phase=1 | train_loss=3.8882 | val_main=0.477751
Epoch 014 | phase=1 | train_loss=3.7979 | val_main=0.477913
Epoch 015 | phase=1 | train_loss=3.6843 | val_main=0.477831
Epoch 016 | phase=0 | train_loss=4.5835 | val_main=0.472573
Epoch 017 | phase=0 | train_loss=4.3468 | val_main=0.454521
Epoch 018 | phase=0 | train_loss=4.2603 | val_main=0.440720
Epoch 019 | phase=0 | train_loss=3.9442 | val_main=0.515534
Epoch 020 | phase=0 | train_loss=3.7751 | val_main=0.500241
Epoch 021 | phase=0 | train_loss=3.6935 | val_main=0.506281
Epoch 022 | phase=0 | train_loss=3.6109 | val_main=0.523840
Epoch 023 | phase=0 | train_loss=3.6362 | val_main=0.526603
Epoch 024 | phase=0 | train_loss=3.4111 | val_main=0.543340
Epoch 025 | phase=0 | train_loss=3.2900 | val_main=0.550049
Epoch 026 | phase=0 | train_loss=3.3329 | val_main=0.568856
Epoch 027 | phase=0 | train_loss=3.1979 | val_main=0.574094
Epoch 028 | phase=0 | train_loss=3.1224 | val_main=0.572261
Epoch 029 | phase=0 | train_loss=3.0316 | val_main=0.588008
Epoch 030 | phase=0 | train_loss=2.9750 | val_main=0.588131
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 120
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.79908752
1 day(s) RMSE : 9.36745275
1 day(s) R2 : 0.01512134
1 day(s) Pearson r : 0.27770720
1 day(s) QLIKE : 0.37179724
3 day(s) MAE : 2.84848915
3 day(s) RMSE : 9.47442347
3 day(s) R2 : -0.00258773
3 day(s) Pearson r : 0.20221246
3 day(s) QLIKE : 0.39717069
5 day(s) MAE : 2.88421330
5 day(s) RMSE : 9.52867787
5 day(s) R2 : -0.01087478
5 day(s) Pearson r : 0.14752651
5 day(s) QLIKE : 0.41218318
10 day(s) MAE : 2.92601498
10 day(s) RMSE : 9.59210727
10 day(s) R2 : -0.02198668
10 day(s) Pearson r : 0.07916104
10 day(s) QLIKE : 0.42880035
20 day(s) MAE : 2.95265978
20 day(s) RMSE : 9.62752967
20 day(s) R2 : -0.02873320
20 day(s) Pearson r : 0.03453393
20 day(s) QLIKE : 0.43681860
full horizon MAE : 2.95265978
full horizon RMSE : 9.62752967
full horizon R2 : -0.02873320
full horizon Pearson r : 0.03453393
full horizon QLIKE : 0.43681860
--- Task 2 ---
1 day(s) MAE : 0.04605167
1 day(s) RMSE : 0.05652458
1 day(s) R2 : -1.57928508
1 day(s) Pearson r : 0.10114606
1 day(s) QLIKE : 10.36176947
3 day(s) MAE : 0.04684801
3 day(s) RMSE : 0.05848394
3 day(s) R2 : -1.75854442
3 day(s) Pearson r : 0.08757062
3 day(s) QLIKE : 10.46015496
5 day(s) MAE : 0.04657109
5 day(s) RMSE : 0.05822069
5 day(s) R2 : -1.73201879
5 day(s) Pearson r : 0.08097165
5 day(s) QLIKE : 10.58860050
10 day(s) MAE : 0.04539091
10 day(s) RMSE : 0.05677932
10 day(s) R2 : -1.59189465
10 day(s) Pearson r : 0.10163745
10 day(s) QLIKE : 10.91881043
20 day(s) MAE : 0.04314326
20 day(s) RMSE : 0.05420691
20 day(s) R2 : -1.36370895
20 day(s) Pearson r : 0.12765531
20 day(s) QLIKE : 11.26722821
full horizon MAE : 0.04314326
full horizon RMSE : 0.05420691
full horizon R2 : -1.36370895
full horizon Pearson r : 0.12765531
full horizon QLIKE : 11.26722821
--- Task 3 ---
1 day(s) MAE : 0.34618562
1 day(s) RMSE : 0.45123722
1 day(s) R2 : -0.29530386
1 day(s) Pearson r : -0.00343574
1 day(s) QLIKE : 8.10848135
3 day(s) MAE : 0.35105368
3 day(s) RMSE : 0.46069360
3 day(s) R2 : -0.35152252
3 day(s) Pearson r : 0.05303426
3 day(s) QLIKE : 8.10582277
5 day(s) MAE : 0.35050834
5 day(s) RMSE : 0.45738129
5 day(s) R2 : -0.33324117
5 day(s) Pearson r : 0.07990378
5 day(s) QLIKE : 8.10440189
10 day(s) MAE : 0.34691906
10 day(s) RMSE : 0.45145800
10 day(s) R2 : -0.30157263
10 day(s) Pearson r : 0.11194583
10 day(s) QLIKE : 8.10167867
20 day(s) MAE : 0.34642497
20 day(s) RMSE : 0.45203979
20 day(s) R2 : -0.30579113
20 day(s) Pearson r : 0.07863451
20 day(s) QLIKE : 8.10536868
full horizon MAE : 0.34642497
full horizon RMSE : 0.45203979
full horizon R2 : -0.30579113
full horizon Pearson r : 0.07863451
full horizon QLIKE : 8.10536868
--- Task 4 ---
1 day(s) MAE : 0.87579439
1 day(s) RMSE : 1.14645948
1 day(s) R2 : -0.65253102
1 day(s) Pearson r : 0.11281698
1 day(s) QLIKE : 1.59474037
3 day(s) MAE : 0.87554933
3 day(s) RMSE : 1.14452791
3 day(s) R2 : -0.65027018
3 day(s) Pearson r : 0.11678296
3 day(s) QLIKE : 1.54145219
5 day(s) MAE : 0.87547930
5 day(s) RMSE : 1.14225321
5 day(s) R2 : -0.64678261
5 day(s) Pearson r : 0.11759082
5 day(s) QLIKE : 1.52873657
10 day(s) MAE : 0.87793399
10 day(s) RMSE : 1.14489433
10 day(s) R2 : -0.66130801
10 day(s) Pearson r : 0.07942450
10 day(s) QLIKE : 1.56586619
20 day(s) MAE : 0.87281511
20 day(s) RMSE : 1.14512376
20 day(s) R2 : -0.67091998
20 day(s) Pearson r : 0.06729956
20 day(s) QLIKE : 1.54162125
full horizon MAE : 0.87281511
full horizon RMSE : 1.14512376
full horizon R2 : -0.67091998
full horizon Pearson r : 0.06729956
full horizon QLIKE : 1.54162125
--- Task 5 ---
1 day(s) MAE : 0.09181071
1 day(s) RMSE : 0.10881783
1 day(s) R2 : -2.21647542
1 day(s) Pearson r : -0.00702690
1 day(s) QLIKE : 16.41001066
3 day(s) MAE : 0.09222536
3 day(s) RMSE : 0.10939793
3 day(s) R2 : -2.26786745
3 day(s) Pearson r : -0.01956715
3 day(s) QLIKE : 16.35031574
5 day(s) MAE : 0.09282414
5 day(s) RMSE : 0.10988344
5 day(s) R2 : -2.31329749
5 day(s) Pearson r : -0.02245796
5 day(s) QLIKE : 16.17676682
10 day(s) MAE : 0.09391447
10 day(s) RMSE : 0.11079072
10 day(s) R2 : -2.40908960
10 day(s) Pearson r : -0.00201191
10 day(s) QLIKE : 15.88844059
20 day(s) MAE : 0.09602308
20 day(s) RMSE : 0.11244777
20 day(s) R2 : -2.59890523
20 day(s) Pearson r : 0.01209877
20 day(s) QLIKE : 15.31222530
full horizon MAE : 0.09602308
full horizon RMSE : 0.11244777
full horizon R2 : -2.59890523
full horizon Pearson r : 0.01209877
full horizon QLIKE : 15.31222530
--- Task 6 ---
1 day(s) MAE : 14.52232725
1 day(s) RMSE : 26.58473431
1 day(s) R2 : -88.31741962
1 day(s) Pearson r : -0.07910425
1 day(s) QLIKE : 0.38653082
3 day(s) MAE : 14.35607613
3 day(s) RMSE : 26.74119586
3 day(s) R2 : -89.08468829
3 day(s) Pearson r : -0.07648542
3 day(s) QLIKE : 0.38099025
5 day(s) MAE : 14.52241238
5 day(s) RMSE : 27.26163423
5 day(s) R2 : -92.32613869
5 day(s) Pearson r : -0.07416201
5 day(s) QLIKE : 0.38225422
10 day(s) MAE : 14.94328514
10 day(s) RMSE : 28.05476513
10 day(s) R2 : -97.15417453
10 day(s) Pearson r : -0.06918519
10 day(s) QLIKE : 0.38613410
20 day(s) MAE : 14.32650769
20 day(s) RMSE : 26.29399743
20 day(s) R2 : -83.98417017
20 day(s) Pearson r : -0.06566913
20 day(s) QLIKE : 0.37112246
full horizon MAE : 14.32650769
full horizon RMSE : 26.29399743
full horizon R2 : -83.98417017
full horizon Pearson r : -0.06566913
full horizon QLIKE : 0.37112246
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/C/Custom_KAN_H20.pkl
Saved y_true min=0.525001, max=134.647
Saved y_pred min=2.25159, max=7.9084
=== C | H=20 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2018
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1453, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 21.8091406863669
Min value: -0.37248441284063266
Checking X_time_train_core:
Shape: (1453, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.800915059933457
Checking y_train_core (log_mse scaled):
Shape: (1453, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 5.261857345977599
Min value: -7.10781787362285
Checking X_price_val:
Shape: (161, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.018765610501212
Min value: -0.35593837785433546
Checking X_time_val:
Shape: (161, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5949981610828599
Checking y_val (log_mse scaled):
Shape: (161, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.643530346347732
Min value: -1.6643841678427629
Checking X_price_test:
Shape: (404, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.615585669042774
Min value: -0.3593653801213122
Checking X_time_test:
Shape: (404, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.481940465687973
Min value: -1.5797626144150565
Checking y_test (log_mse scaled):
Shape: (404, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.108493380986118
Min value: -7.10781787362285
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 109s 517ms/step - loss: 0.8023 - val_loss: 0.4728 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 14s 313ms/step - loss: 0.4596 - val_loss: 0.4865 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 455ms/step - loss: 0.3820 - val_loss: 0.7147 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 22s 477ms/step - loss: 0.3502 - val_loss: 0.7938 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 22s 486ms/step - loss: 0.3296 - val_loss: 0.8101 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
46/46 ━━━━━━━━━━━━━━━━━━━━ 0s 438ms/step - loss: 0.3231
Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
46/46 ━━━━━━━━━━━━━━━━━━━━ 21s 451ms/step - loss: 0.3248 - val_loss: 0.8366 - learning_rate: 5.0000e-04
Epoch 6: early stopping
Restoring model weights from the end of the best epoch: 1.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 20
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 2.85991588
1 day(s) RMSE : 9.52586378
1 day(s) R2 : -0.01847047
1 day(s) Pearson r : -0.38635034
1 day(s) QLIKE : 0.43025248
3 day(s) MAE : 2.90890065
3 day(s) RMSE : 9.54555235
3 day(s) R2 : -0.01769802
3 day(s) Pearson r : 0.01181776
3 day(s) QLIKE : 0.43198048
5 day(s) MAE : 2.91443877
5 day(s) RMSE : 9.57446994
5 day(s) R2 : -0.02061407
5 day(s) Pearson r : 0.00474146
5 day(s) QLIKE : 0.43427245
10 day(s) MAE : 2.92045005
10 day(s) RMSE : 9.60115429
10 day(s) R2 : -0.02391541
10 day(s) Pearson r : 0.00307696
10 day(s) QLIKE : 0.43474130
20 day(s) MAE : 2.93472500
20 day(s) RMSE : 9.60854443
20 day(s) R2 : -0.02467993
20 day(s) Pearson r : 0.00462733
20 day(s) QLIKE : 0.43277833
full horizon MAE : 2.93472500
full horizon RMSE : 9.60854443
full horizon R2 : -0.02467993
full horizon Pearson r : 0.00462733
full horizon QLIKE : 0.43277833
--- Task 2 ---
1 day(s) MAE : 0.04790468
1 day(s) RMSE : 0.05933481
1 day(s) R2 : -1.84212916
1 day(s) Pearson r : 0.15491253
1 day(s) QLIKE : 6.32586953
3 day(s) MAE : 0.04803090
3 day(s) RMSE : 0.05951759
3 day(s) R2 : -1.85691506
3 day(s) Pearson r : 0.00156028
3 day(s) QLIKE : 10.92491631
5 day(s) MAE : 0.04808903
5 day(s) RMSE : 0.05958644
5 day(s) R2 : -1.86169863
5 day(s) Pearson r : 0.00042791
5 day(s) QLIKE : 11.78791560
10 day(s) MAE : 0.04820997
10 day(s) RMSE : 0.05972167
10 day(s) R2 : -1.86748299
10 day(s) Pearson r : -0.00081694
10 day(s) QLIKE : 10.41373059
20 day(s) MAE : 0.04821613
20 day(s) RMSE : 0.05972639
20 day(s) R2 : -1.86957323
20 day(s) Pearson r : -0.00056575
20 day(s) QLIKE : 8.77163221
full horizon MAE : 0.04821613
full horizon RMSE : 0.05972639
full horizon R2 : -1.86957323
full horizon Pearson r : -0.00056575
full horizon QLIKE : 8.77163221
--- Task 3 ---
1 day(s) MAE : 0.44311935
1 day(s) RMSE : 0.47420316
1 day(s) R2 : -0.43050940
1 day(s) Pearson r : 0.00018964
1 day(s) QLIKE : 8.10491166
3 day(s) MAE : 0.44568900
3 day(s) RMSE : 0.47805818
3 day(s) R2 : -0.45532647
3 day(s) Pearson r : -0.02941107
3 day(s) QLIKE : 8.10606462
5 day(s) MAE : 0.44383446
5 day(s) RMSE : 0.47558486
5 day(s) R2 : -0.44147780
5 day(s) Pearson r : -0.02047093
5 day(s) QLIKE : 8.10680892
10 day(s) MAE : 0.41295804
10 day(s) RMSE : 0.44832361
10 day(s) R2 : -0.28356218
10 day(s) Pearson r : 0.01227319
10 day(s) QLIKE : 8.14947327
20 day(s) MAE : 0.38520061
20 day(s) RMSE : 0.44414584
20 day(s) R2 : -0.26058339
20 day(s) Pearson r : 0.00192712
20 day(s) QLIKE : 8.30886586
full horizon MAE : 0.38520061
full horizon RMSE : 0.44414584
full horizon R2 : -0.26058339
full horizon Pearson r : 0.00192712
full horizon QLIKE : 8.30886586
--- Task 4 ---
1 day(s) MAE : 0.91484698
1 day(s) RMSE : 1.26119804
1 day(s) R2 : -0.99985624
1 day(s) Pearson r : -0.00430465
1 day(s) QLIKE : 0.75853718
3 day(s) MAE : 0.92830664
3 day(s) RMSE : 1.27501590
3 day(s) R2 : -1.04801664
3 day(s) Pearson r : 0.00528653
3 day(s) QLIKE : 0.79244523
5 day(s) MAE : 0.93973464
5 day(s) RMSE : 1.28599246
5 day(s) R2 : -1.08731664
5 day(s) Pearson r : 0.00363529
5 day(s) QLIKE : 0.80835937
10 day(s) MAE : 0.96529254
10 day(s) RMSE : 1.30750444
10 day(s) R2 : -1.16673448
10 day(s) Pearson r : -0.00121205
10 day(s) QLIKE : 1.13854302
20 day(s) MAE : 0.98846781
20 day(s) RMSE : 1.32541954
20 day(s) R2 : -1.23850227
20 day(s) Pearson r : -0.00482949
20 day(s) QLIKE : 1.25539802
full horizon MAE : 0.98846781
full horizon RMSE : 1.32541954
full horizon R2 : -1.23850227
full horizon Pearson r : -0.00482949
full horizon QLIKE : 1.25539802
--- Task 5 ---
1 day(s) MAE : 0.09541013
1 day(s) RMSE : 0.11306167
1 day(s) R2 : -2.47224958
1 day(s) Pearson r : -0.14310877
1 day(s) QLIKE : 2.97845345
3 day(s) MAE : 0.09571892
3 day(s) RMSE : 0.11323314
3 day(s) R2 : -2.50100981
3 day(s) Pearson r : -0.01401595
3 day(s) QLIKE : 2.99086019
5 day(s) MAE : 0.09604450
5 day(s) RMSE : 0.11342463
5 day(s) R2 : -2.53029237
5 day(s) Pearson r : -0.00696806
5 day(s) QLIKE : 2.98924592
10 day(s) MAE : 0.09619621
10 day(s) RMSE : 0.11320709
10 day(s) R2 : -2.55941723
10 day(s) Pearson r : 0.00827639
10 day(s) QLIKE : 3.68924468
20 day(s) MAE : 0.09495995
20 day(s) RMSE : 0.11120804
20 day(s) R2 : -2.51998766
20 day(s) Pearson r : 0.03214793
20 day(s) QLIKE : 4.34758043
full horizon MAE : 0.09495995
full horizon RMSE : 0.11120804
full horizon R2 : -2.51998766
full horizon Pearson r : 0.03214793
full horizon QLIKE : 4.34758043
--- Task 6 ---
1 day(s) MAE : 2.63798043
1 day(s) RMSE : 3.05530628
1 day(s) R2 : -0.17972566
1 day(s) Pearson r : 0.11015519
1 day(s) QLIKE : 0.07085534
3 day(s) MAE : 3.18019697
3 day(s) RMSE : 3.57812916
3 day(s) R2 : -0.61287740
3 day(s) Pearson r : -0.00189960
3 day(s) QLIKE : 0.07457867
5 day(s) MAE : 3.99490845
5 day(s) RMSE : 4.54833107
5 day(s) R2 : -1.59778774
5 day(s) Pearson r : -0.00180272
5 day(s) QLIKE : 0.08349651
10 day(s) MAE : 6.64178661
10 day(s) RMSE : 7.62179630
10 day(s) R2 : -6.24452935
10 day(s) Pearson r : -0.00391547
10 day(s) QLIKE : 0.11007501
20 day(s) MAE : 9.78483721
20 day(s) RMSE : 10.81017058
20 day(s) R2 : -13.36447969
20 day(s) Pearson r : -0.00828981
20 day(s) QLIKE : 0.12350550
full horizon MAE : 9.78483721
full horizon RMSE : 10.81017058
full horizon R2 : -13.36447969
full horizon Pearson r : -0.00828981
full horizon QLIKE : 0.12350550
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/C/Custom_KAN_LSTM_H20.pkl
Saved y_true min=0.525001, max=134.647
Saved y_pred min=3.17274, max=3.62561
=== BTCUSDT | H=1 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2399
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1727, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 27.572355514245807
Min value: -0.39529821705307827
Checking X_time_train_core:
Shape: (1727, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.8178490491226165
Checking y_train_core (log_mse scaled):
Shape: (1727, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.551349524903824
Min value: -20.89631563497493
Checking X_price_val:
Shape: (192, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.1802886452741648
Min value: -0.3973706893292718
Checking X_time_val:
Shape: (192, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_val (log_mse scaled):
Shape: (192, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3448190912377083
Min value: -4.2413266524913515
Checking X_price_test:
Shape: (480, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.92735513679459
Min value: -0.3866725144992877
Checking X_time_test:
Shape: (480, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_test (log_mse scaled):
Shape: (480, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5802586573962314
Min value: -109.72965053174772
Epoch 001 | phase=1 | train_loss=4.9619 | val_main=1.949324
Epoch 002 | phase=1 | train_loss=4.9367 | val_main=1.949362
Epoch 003 | phase=1 | train_loss=4.8141 | val_main=1.949575
Epoch 004 | phase=1 | train_loss=4.6283 | val_main=1.950402
Epoch 005 | phase=1 | train_loss=4.4513 | val_main=1.951415
Epoch 006 | phase=1 | train_loss=4.5535 | val_main=1.951785
Epoch 007 | phase=1 | train_loss=4.3106 | val_main=1.951692
Epoch 008 | phase=1 | train_loss=4.2149 | val_main=1.951611
Epoch 009 | phase=1 | train_loss=4.1726 | val_main=1.951826
Epoch 010 | phase=1 | train_loss=4.0538 | val_main=1.952064
Epoch 011 | phase=1 | train_loss=4.0276 | val_main=1.951197
Epoch 012 | phase=1 | train_loss=3.8476 | val_main=1.951272
Epoch 013 | phase=1 | train_loss=3.6162 | val_main=1.951518
Epoch 014 | phase=1 | train_loss=3.4065 | val_main=1.953311
Epoch 015 | phase=1 | train_loss=2.8490 | val_main=1.954090
Epoch 016 | phase=0 | train_loss=3.4172 | val_main=1.813849
Epoch 017 | phase=0 | train_loss=2.8808 | val_main=1.206948
Epoch 018 | phase=0 | train_loss=2.3493 | val_main=0.930464
Epoch 019 | phase=0 | train_loss=2.0559 | val_main=0.885287
Epoch 020 | phase=0 | train_loss=1.8430 | val_main=0.873508
Epoch 021 | phase=0 | train_loss=1.7791 | val_main=0.850038
Epoch 022 | phase=0 | train_loss=1.6151 | val_main=0.819467
Epoch 023 | phase=0 | train_loss=1.5631 | val_main=0.867410
Epoch 024 | phase=0 | train_loss=1.4488 | val_main=0.815839
Epoch 025 | phase=0 | train_loss=1.3600 | val_main=0.776645
Epoch 026 | phase=0 | train_loss=1.2990 | val_main=0.778339
Epoch 027 | phase=0 | train_loss=1.2109 | val_main=0.802437
Epoch 028 | phase=0 | train_loss=1.1480 | val_main=0.896909
Epoch 029 | phase=0 | train_loss=1.0978 | val_main=0.800879
Epoch 030 | phase=0 | train_loss=1.0263 | val_main=0.837132
Epoch 031 | phase=2 | train_loss=0.2780 | val_main=0.857954
Epoch 032 | phase=2 | train_loss=0.2690 | val_main=0.783860
Epoch 033 | phase=2 | train_loss=0.2565 | val_main=0.822531
Epoch 034 | phase=2 | train_loss=0.2385 | val_main=0.876996
Epoch 035 | phase=2 | train_loss=0.2292 | val_main=0.803932
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 6
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 3.99689241
1 day(s) RMSE : 8.87809756
1 day(s) R2 : 0.06590336
1 day(s) Pearson r : 0.37813751
1 day(s) QLIKE : 0.25580831
full horizon MAE : 3.99689241
full horizon RMSE : 8.87809756
full horizon R2 : 0.06590336
full horizon Pearson r : 0.37813751
full horizon QLIKE : 0.25580831
--- Task 2 ---
1 day(s) MAE : 0.12522921
1 day(s) RMSE : 0.23789846
1 day(s) R2 : -0.06450087
1 day(s) Pearson r : -0.11008888
1 day(s) QLIKE : 20.94522227
full horizon MAE : 0.12522921
full horizon RMSE : 0.23789846
full horizon R2 : -0.06450087
full horizon Pearson r : -0.11008888
full horizon QLIKE : 20.94522227
--- Task 3 ---
1 day(s) MAE : 0.20875851
1 day(s) RMSE : 0.30113533
1 day(s) R2 : -0.64278885
1 day(s) Pearson r : -0.06593835
1 day(s) QLIKE : 0.17548291
full horizon MAE : 0.20875851
full horizon RMSE : 0.30113533
full horizon R2 : -0.64278885
full horizon Pearson r : -0.06593835
full horizon QLIKE : 0.17548291
--- Task 4 ---
1 day(s) MAE : 1.01029246
1 day(s) RMSE : 2.29848735
1 day(s) R2 : -0.06045923
1 day(s) Pearson r : -0.05249756
1 day(s) QLIKE : 5.34298519
full horizon MAE : 1.01029246
full horizon RMSE : 2.29848735
full horizon R2 : -0.06045923
full horizon Pearson r : -0.05249756
full horizon QLIKE : 5.34298519
--- Task 5 ---
1 day(s) MAE : 0.10102501
1 day(s) RMSE : 0.11512802
1 day(s) R2 : -3.30053214
1 day(s) Pearson r : 0.09771618
1 day(s) QLIKE : 4.13264647
full horizon MAE : 0.10102501
full horizon RMSE : 0.11512802
full horizon R2 : -3.30053214
full horizon Pearson r : 0.09771618
full horizon QLIKE : 4.13264647
--- Task 6 ---
1 day(s) MAE : 1.07600708
1 day(s) RMSE : 1.31503076
1 day(s) R2 : -0.96975623
1 day(s) Pearson r : -0.16049800
1 day(s) QLIKE : 0.07400128
full horizon MAE : 1.07600708
full horizon RMSE : 1.31503076
full horizon R2 : -0.96975623
full horizon Pearson r : -0.16049800
full horizon QLIKE : 0.07400128
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BTCUSDT/Custom_KAN_H1.pkl
Saved y_true min=0.505764, max=131.49
Saved y_pred min=0.973505, max=51.0523
=== BTCUSDT | H=1 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2399
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1727, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 27.572355514245807
Min value: -0.39529821705307827
Checking X_time_train_core:
Shape: (1727, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.8178490491226165
Checking y_train_core (log_mse scaled):
Shape: (1727, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.551349524903824
Min value: -20.89631563497493
Checking X_price_val:
Shape: (192, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.1802886452741648
Min value: -0.3973706893292718
Checking X_time_val:
Shape: (192, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_val (log_mse scaled):
Shape: (192, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3448190912377083
Min value: -4.2413266524913515
Checking X_price_test:
Shape: (480, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.92735513679459
Min value: -0.3866725144992877
Checking X_time_test:
Shape: (480, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_test (log_mse scaled):
Shape: (480, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5802586573962314
Min value: -109.72965053174772
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 326s 463ms/step - loss: 1.0056 - val_loss: 1.6621 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 9s 174ms/step - loss: 0.9704 - val_loss: 1.7061 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 190ms/step - loss: 0.9420 - val_loss: 1.6756 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 188ms/step - loss: 0.9190 - val_loss: 1.0481 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 189ms/step - loss: 0.9057 - val_loss: 1.2995 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 187ms/step - loss: 0.8963 - val_loss: 0.9975 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 188ms/step - loss: 0.8975 - val_loss: 0.9580 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 186ms/step - loss: 0.8958 - val_loss: 1.0102 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 189ms/step - loss: 0.8915 - val_loss: 0.9875 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 185ms/step - loss: 0.8846 - val_loss: 0.9575 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 185ms/step - loss: 0.8748 - val_loss: 1.1311 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=12 tf_ratio=0.421 -> TF=ON
Epoch 12/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 186ms/step - loss: 0.8814 - val_loss: 1.1296 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=13 tf_ratio=0.368 -> TF=ON
Epoch 13/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 186ms/step - loss: 0.8856 - val_loss: 1.0030 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=14 tf_ratio=0.316 -> TF=ON
Epoch 14/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 187ms/step - loss: 0.8848 - val_loss: 0.9057 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=15 tf_ratio=0.263 -> TF=ON
Epoch 15/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 188ms/step - loss: 0.8774 - val_loss: 1.0175 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=16 tf_ratio=0.211 -> TF=ON
Epoch 16/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 186ms/step - loss: 0.8679 - val_loss: 1.0047 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=17 tf_ratio=0.158 -> TF=ON
Epoch 17/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 189ms/step - loss: 0.8713 - val_loss: 0.9151 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=18 tf_ratio=0.105 -> TF=ON
Epoch 18/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 12s 217ms/step - loss: 0.8680 - val_loss: 1.2609 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=19 tf_ratio=0.053 -> TF=ON
Epoch 19/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 0s 188ms/step - loss: 0.8670
Epoch 19: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
54/54 ━━━━━━━━━━━━━━━━━━━━ 10s 194ms/step - loss: 0.8939 - val_loss: 1.0361 - learning_rate: 5.0000e-04
Epoch 19: early stopping
Restoring model weights from the end of the best epoch: 14.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 1
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.45645212
1 day(s) RMSE : 9.13137586
1 day(s) R2 : 0.01184648
1 day(s) Pearson r : 0.31176761
1 day(s) QLIKE : 0.32361205
full horizon MAE : 4.45645212
full horizon RMSE : 9.13137586
full horizon R2 : 0.01184648
full horizon Pearson r : 0.31176761
full horizon QLIKE : 0.32361205
--- Task 2 ---
1 day(s) MAE : 0.08936264
1 day(s) RMSE : 0.24505932
1 day(s) R2 : -0.12954935
1 day(s) Pearson r : -0.03794293
1 day(s) QLIKE : 3.57849947
full horizon MAE : 0.08936264
full horizon RMSE : 0.24505932
full horizon R2 : -0.12954935
full horizon Pearson r : -0.03794293
full horizon QLIKE : 3.57849947
--- Task 3 ---
1 day(s) MAE : 0.18000615
1 day(s) RMSE : 0.24963807
1 day(s) R2 : -0.12896363
1 day(s) Pearson r : -0.02917219
1 day(s) QLIKE : 0.14270441
full horizon MAE : 0.18000615
full horizon RMSE : 0.24963807
full horizon R2 : -0.12896363
full horizon Pearson r : -0.02917219
full horizon QLIKE : 0.14270441
--- Task 4 ---
1 day(s) MAE : 0.95190070
1 day(s) RMSE : 2.40956885
1 day(s) R2 : -0.16543599
1 day(s) Pearson r : 0.11071001
1 day(s) QLIKE : 1.19749746
full horizon MAE : 0.95190070
full horizon RMSE : 2.40956885
full horizon R2 : -0.16543599
full horizon Pearson r : 0.11071001
full horizon QLIKE : 1.19749746
--- Task 5 ---
1 day(s) MAE : 0.10188222
1 day(s) RMSE : 0.11602593
1 day(s) R2 : -3.36787590
1 day(s) Pearson r : -0.02185372
1 day(s) QLIKE : 1.62015561
full horizon MAE : 0.10188222
full horizon RMSE : 0.11602593
full horizon R2 : -3.36787590
full horizon Pearson r : -0.02185372
full horizon QLIKE : 1.62015561
--- Task 6 ---
1 day(s) MAE : 0.84278382
1 day(s) RMSE : 1.00083520
1 day(s) R2 : -0.14094787
1 day(s) Pearson r : 0.14875183
1 day(s) QLIKE : 0.06336785
full horizon MAE : 0.84278382
full horizon RMSE : 1.00083520
full horizon R2 : -0.14094787
full horizon Pearson r : 0.14875183
full horizon QLIKE : 0.06336785
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BTCUSDT/Custom_KAN_LSTM_H1.pkl
Saved y_true min=0.505764, max=131.49
Saved y_pred min=2.36666, max=36.5761
=== BTCUSDT | H=5 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2399
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1727, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 27.572355514245807
Min value: -0.39529821705307827
Checking X_time_train_core:
Shape: (1727, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.8178490491226165
Checking y_train_core (log_mse scaled):
Shape: (1727, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.5541968122841485
Min value: -20.88291388736243
Checking X_price_val:
Shape: (192, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.1802886452741648
Min value: -0.3973706893292718
Checking X_time_val:
Shape: (192, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_val (log_mse scaled):
Shape: (192, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.345979297163924
Min value: -4.245202118935104
Checking X_price_test:
Shape: (480, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.92735513679459
Min value: -0.3866725144992877
Checking X_time_test:
Shape: (480, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_test (log_mse scaled):
Shape: (480, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5815988770306517
Min value: -109.67386610990627
Epoch 001 | phase=1 | train_loss=5.1067 | val_main=1.966552
Epoch 002 | phase=1 | train_loss=4.9556 | val_main=1.966579
Epoch 003 | phase=1 | train_loss=4.8628 | val_main=1.966770
Epoch 004 | phase=1 | train_loss=4.6538 | val_main=1.966857
Epoch 005 | phase=1 | train_loss=4.4947 | val_main=1.967437
Epoch 006 | phase=1 | train_loss=4.4878 | val_main=1.967708
Epoch 007 | phase=1 | train_loss=4.3781 | val_main=1.967751
Epoch 008 | phase=1 | train_loss=4.2601 | val_main=1.967540
Epoch 009 | phase=1 | train_loss=4.2389 | val_main=1.967807
Epoch 010 | phase=1 | train_loss=4.1308 | val_main=1.967275
Epoch 011 | phase=1 | train_loss=4.1114 | val_main=1.967112
Epoch 012 | phase=1 | train_loss=3.9747 | val_main=1.967030
Epoch 013 | phase=1 | train_loss=3.8005 | val_main=1.966801
Epoch 014 | phase=1 | train_loss=3.4798 | val_main=1.966577
Epoch 015 | phase=1 | train_loss=3.0532 | val_main=1.965674
Epoch 016 | phase=0 | train_loss=3.6851 | val_main=1.882010
Epoch 017 | phase=0 | train_loss=3.4961 | val_main=1.544262
Epoch 018 | phase=0 | train_loss=3.0363 | val_main=1.046149
Epoch 019 | phase=0 | train_loss=2.8899 | val_main=1.011407
Epoch 020 | phase=0 | train_loss=2.6411 | val_main=0.993511
Epoch 021 | phase=0 | train_loss=2.5268 | val_main=1.036548
Epoch 022 | phase=0 | train_loss=2.4292 | val_main=0.996138
Epoch 023 | phase=0 | train_loss=2.4264 | val_main=0.994868
Epoch 024 | phase=0 | train_loss=2.3487 | val_main=1.074887
Epoch 025 | phase=0 | train_loss=2.1611 | val_main=1.035774
Epoch 026 | phase=0 | train_loss=2.0269 | val_main=1.132779
Epoch 027 | phase=0 | train_loss=1.9207 | val_main=1.095498
Epoch 028 | phase=0 | train_loss=1.8699 | val_main=1.147024
Epoch 029 | phase=0 | train_loss=1.8330 | val_main=1.157140
Epoch 030 | phase=0 | train_loss=1.7701 | val_main=1.278695
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 30
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.32155870
1 day(s) RMSE : 9.09525472
1 day(s) R2 : 0.01964873
1 day(s) Pearson r : 0.26370386
1 day(s) QLIKE : 0.33020517
3 day(s) MAE : 4.42780266
3 day(s) RMSE : 9.21733197
3 day(s) R2 : -0.00670508
3 day(s) Pearson r : 0.20914083
3 day(s) QLIKE : 0.35241421
5 day(s) MAE : 4.50829430
5 day(s) RMSE : 9.30881624
5 day(s) R2 : -0.02710981
5 day(s) Pearson r : 0.16253769
5 day(s) QLIKE : 0.36762041
full horizon MAE : 4.50829430
full horizon RMSE : 9.30881624
full horizon R2 : -0.02710981
full horizon Pearson r : 0.16253769
full horizon QLIKE : 0.36762041
--- Task 2 ---
1 day(s) MAE : 0.12419797
1 day(s) RMSE : 0.23674999
1 day(s) R2 : -0.05424776
1 day(s) Pearson r : -0.13281888
1 day(s) QLIKE : 20.74798964
3 day(s) MAE : 0.12083435
3 day(s) RMSE : 0.23652339
3 day(s) R2 : -0.05228187
3 day(s) Pearson r : -0.12856517
3 day(s) QLIKE : 20.45252864
5 day(s) MAE : 0.11944532
5 day(s) RMSE : 0.23620560
5 day(s) R2 : -0.04949837
5 day(s) Pearson r : -0.11651827
5 day(s) QLIKE : 20.01441203
full horizon MAE : 0.11944532
full horizon RMSE : 0.23620560
full horizon R2 : -0.04949837
full horizon Pearson r : -0.11651827
full horizon QLIKE : 20.01441203
--- Task 3 ---
1 day(s) MAE : 0.15538194
1 day(s) RMSE : 0.25847641
1 day(s) R2 : -0.21031981
1 day(s) Pearson r : -0.04380784
1 day(s) QLIKE : 0.15039563
3 day(s) MAE : 0.15936760
3 day(s) RMSE : 0.25865647
3 day(s) R2 : -0.21254007
3 day(s) Pearson r : -0.05253732
3 day(s) QLIKE : 0.15041565
5 day(s) MAE : 0.16059436
5 day(s) RMSE : 0.25927227
5 day(s) R2 : -0.21883872
5 day(s) Pearson r : -0.05206314
5 day(s) QLIKE : 0.15071995
full horizon MAE : 0.16059436
full horizon RMSE : 0.25927227
full horizon R2 : -0.21883872
full horizon Pearson r : -0.05206314
full horizon QLIKE : 0.15071995
--- Task 4 ---
1 day(s) MAE : 1.02965059
1 day(s) RMSE : 2.31898338
1 day(s) R2 : -0.07945618
1 day(s) Pearson r : -0.04504905
1 day(s) QLIKE : 8.29950327
3 day(s) MAE : 1.03406936
3 day(s) RMSE : 2.31616901
3 day(s) R2 : -0.07725551
3 day(s) Pearson r : -0.04398572
3 day(s) QLIKE : 8.31899924
5 day(s) MAE : 1.03812575
5 day(s) RMSE : 2.31174651
5 day(s) R2 : -0.07356225
5 day(s) Pearson r : -0.04064483
5 day(s) QLIKE : 8.46092194
full horizon MAE : 1.03812575
full horizon RMSE : 2.31174651
full horizon R2 : -0.07356225
full horizon Pearson r : -0.04064483
full horizon QLIKE : 8.46092194
--- Task 5 ---
1 day(s) MAE : 0.10184603
1 day(s) RMSE : 0.11598547
1 day(s) R2 : -3.36482943
1 day(s) Pearson r : 0.11927018
1 day(s) QLIKE : 3.03424335
3 day(s) MAE : 0.10188542
3 day(s) RMSE : 0.11601122
3 day(s) R2 : -3.36987900
3 day(s) Pearson r : 0.11811853
3 day(s) QLIKE : 3.07196006
5 day(s) MAE : 0.10192661
5 day(s) RMSE : 0.11603690
5 day(s) R2 : -3.37547946
5 day(s) Pearson r : 0.11583503
5 day(s) QLIKE : 3.06970227
full horizon MAE : 0.10192661
full horizon RMSE : 0.11603690
full horizon R2 : -3.37547946
full horizon Pearson r : 0.11583503
full horizon QLIKE : 3.06970227
--- Task 6 ---
1 day(s) MAE : 1.07096173
1 day(s) RMSE : 1.30395656
1 day(s) R2 : -0.93672030
1 day(s) Pearson r : -0.17981276
1 day(s) QLIKE : 0.07433467
3 day(s) MAE : 1.07322252
3 day(s) RMSE : 1.30400081
3 day(s) R2 : -0.93827117
3 day(s) Pearson r : -0.18151810
3 day(s) QLIKE : 0.07423962
5 day(s) MAE : 1.07938556
5 day(s) RMSE : 1.30831716
5 day(s) R2 : -0.95209647
5 day(s) Pearson r : -0.18358153
5 day(s) QLIKE : 0.07432418
full horizon MAE : 1.07938556
full horizon RMSE : 1.30831716
full horizon R2 : -0.95209647
full horizon Pearson r : -0.18358153
full horizon QLIKE : 0.07432418
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BTCUSDT/Custom_KAN_H5.pkl
Saved y_true min=0.505764, max=131.49
Saved y_pred min=2.52162, max=19.0213
=== BTCUSDT | H=5 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2399
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1727, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 27.572355514245807
Min value: -0.39529821705307827
Checking X_time_train_core:
Shape: (1727, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.8178490491226165
Checking y_train_core (log_mse scaled):
Shape: (1727, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.5541968122841485
Min value: -20.88291388736243
Checking X_price_val:
Shape: (192, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.1802886452741648
Min value: -0.3973706893292718
Checking X_time_val:
Shape: (192, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_val (log_mse scaled):
Shape: (192, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.345979297163924
Min value: -4.245202118935104
Checking X_price_test:
Shape: (480, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.92735513679459
Min value: -0.3866725144992877
Checking X_time_test:
Shape: (480, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_test (log_mse scaled):
Shape: (480, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5815988770306517
Min value: -109.67386610990627
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 165s 406ms/step - loss: 0.8001 - val_loss: 2.1970 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 12s 224ms/step - loss: 0.5008 - val_loss: 1.2214 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 13s 246ms/step - loss: 0.4477 - val_loss: 1.0595 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 13s 249ms/step - loss: 0.4300 - val_loss: 1.8890 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 13s 248ms/step - loss: 0.4173 - val_loss: 1.1321 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 13s 241ms/step - loss: 0.4071 - val_loss: 1.1852 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 13s 248ms/step - loss: 0.3972 - val_loss: 1.0617 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 13s 241ms/step - loss: 0.3927 - val_loss: 1.0052 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 13s 242ms/step - loss: 0.3940 - val_loss: 1.1075 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 13s 243ms/step - loss: 0.3824 - val_loss: 1.0477 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 13s 241ms/step - loss: 0.3799 - val_loss: 1.0129 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=12 tf_ratio=0.421 -> TF=ON
Epoch 12/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 13s 243ms/step - loss: 0.3781 - val_loss: 1.0360 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=13 tf_ratio=0.368 -> TF=ON
Epoch 13/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 0s 235ms/step - loss: 0.3464
Epoch 13: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
54/54 ━━━━━━━━━━━━━━━━━━━━ 13s 242ms/step - loss: 0.3687 - val_loss: 1.0622 - learning_rate: 5.0000e-04
Epoch 13: early stopping
Restoring model weights from the end of the best epoch: 8.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 5
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.37998011
1 day(s) RMSE : 9.32960953
1 day(s) R2 : -0.03152300
1 day(s) Pearson r : 0.27056371
1 day(s) QLIKE : 0.33685103
3 day(s) MAE : 4.66039185
3 day(s) RMSE : 9.57569521
3 day(s) R2 : -0.08650676
3 day(s) Pearson r : 0.22324885
3 day(s) QLIKE : 0.36156530
5 day(s) MAE : 4.78826706
5 day(s) RMSE : 9.74504535
5 day(s) R2 : -0.12563009
5 day(s) Pearson r : 0.17607040
5 day(s) QLIKE : 0.37064856
full horizon MAE : 4.78826706
full horizon RMSE : 9.74504535
full horizon R2 : -0.12563009
full horizon Pearson r : 0.17607040
full horizon QLIKE : 0.37064856
--- Task 2 ---
1 day(s) MAE : 0.08864709
1 day(s) RMSE : 0.24469260
1 day(s) R2 : -0.12617123
1 day(s) Pearson r : 0.00452437
1 day(s) QLIKE : 3.48209836
3 day(s) MAE : 0.09094408
3 day(s) RMSE : 0.24565945
3 day(s) R2 : -0.13514376
3 day(s) Pearson r : 0.03718493
3 day(s) QLIKE : 18.27182264
5 day(s) MAE : 0.09212537
5 day(s) RMSE : 0.24451071
5 day(s) R2 : -0.12459760
5 day(s) Pearson r : 0.04623455
5 day(s) QLIKE : 18.37324118
full horizon MAE : 0.09212537
full horizon RMSE : 0.24451071
full horizon R2 : -0.12459760
full horizon Pearson r : 0.04623455
full horizon QLIKE : 18.37324118
--- Task 3 ---
1 day(s) MAE : 0.15702659
1 day(s) RMSE : 0.23707718
1 day(s) R2 : -0.01821114
1 day(s) Pearson r : 0.00897044
1 day(s) QLIKE : 0.13956536
3 day(s) MAE : 0.12926117
3 day(s) RMSE : 0.24399567
3 day(s) R2 : -0.07898061
3 day(s) Pearson r : -0.01439370
3 day(s) QLIKE : 0.14145143
5 day(s) MAE : 0.12612569
5 day(s) RMSE : 0.25020883
5 day(s) R2 : -0.13511377
5 day(s) Pearson r : -0.01982481
5 day(s) QLIKE : 0.14158814
full horizon MAE : 0.12612569
full horizon RMSE : 0.25020883
full horizon R2 : -0.13511377
full horizon Pearson r : -0.01982481
full horizon QLIKE : 0.14158814
--- Task 4 ---
1 day(s) MAE : 0.94404606
1 day(s) RMSE : 2.39694921
1 day(s) R2 : -0.15326047
1 day(s) Pearson r : 0.19739123
1 day(s) QLIKE : 1.15003828
3 day(s) MAE : 0.94345598
3 day(s) RMSE : 2.39994009
3 day(s) R2 : -0.15658893
3 day(s) Pearson r : 0.13367198
3 day(s) QLIKE : 1.17792430
5 day(s) MAE : 0.95288359
5 day(s) RMSE : 2.40553834
5 day(s) R2 : -0.16244223
5 day(s) Pearson r : 0.08956047
5 day(s) QLIKE : 1.25135580
full horizon MAE : 0.95288359
full horizon RMSE : 2.40553834
full horizon R2 : -0.16244223
full horizon Pearson r : 0.08956047
full horizon QLIKE : 1.25135580
--- Task 5 ---
1 day(s) MAE : 0.10188225
1 day(s) RMSE : 0.11602528
1 day(s) R2 : -3.36782680
1 day(s) Pearson r : 0.04964819
1 day(s) QLIKE : 1.05849258
3 day(s) MAE : 0.10192428
3 day(s) RMSE : 0.11605318
3 day(s) R2 : -3.37304073
3 day(s) Pearson r : 0.03503306
3 day(s) QLIKE : 0.77728059
5 day(s) MAE : 0.10196556
5 day(s) RMSE : 0.11607818
5 day(s) R2 : -3.37859320
5 day(s) Pearson r : 0.03743300
5 day(s) QLIKE : 0.67486963
full horizon MAE : 0.10196556
full horizon RMSE : 0.11607818
full horizon R2 : -3.37859320
full horizon Pearson r : 0.03743300
full horizon QLIKE : 0.67486963
--- Task 6 ---
1 day(s) MAE : 0.85245926
1 day(s) RMSE : 1.03063945
1 day(s) R2 : -0.20991311
1 day(s) Pearson r : 0.11192787
1 day(s) QLIKE : 0.06336096
3 day(s) MAE : 0.87731532
3 day(s) RMSE : 1.07083279
3 day(s) R2 : -0.30707990
3 day(s) Pearson r : 0.11311351
3 day(s) QLIKE : 0.06362444
5 day(s) MAE : 0.89304913
5 day(s) RMSE : 1.09715089
5 day(s) R2 : -0.37280205
5 day(s) Pearson r : 0.10617994
5 day(s) QLIKE : 0.06386617
full horizon MAE : 0.89304913
full horizon RMSE : 1.09715089
full horizon R2 : -0.37280205
full horizon Pearson r : 0.10617994
full horizon QLIKE : 0.06386617
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BTCUSDT/Custom_KAN_LSTM_H5.pkl
Saved y_true min=0.505764, max=131.49
Saved y_pred min=2.26456, max=26.0744
=== BTCUSDT | H=10 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2399
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1727, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 27.572355514245807
Min value: -0.39529821705307827
Checking X_time_train_core:
Shape: (1727, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.8178490491226165
Checking y_train_core (log_mse scaled):
Shape: (1727, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.556915178048574
Min value: -20.868486718259952
Checking X_price_val:
Shape: (192, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.1802886452741648
Min value: -0.3973706893292718
Checking X_time_val:
Shape: (192, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_val (log_mse scaled):
Shape: (192, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3469968336435327
Min value: -4.249261289934393
Checking X_price_test:
Shape: (480, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.92735513679459
Min value: -0.3866725144992877
Checking X_time_test:
Shape: (480, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_test (log_mse scaled):
Shape: (480, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5827978941292526
Min value: -109.59273460949947
Epoch 001 | phase=1 | train_loss=5.0298 | val_main=1.949810
Epoch 002 | phase=1 | train_loss=4.9852 | val_main=1.949735
Epoch 003 | phase=1 | train_loss=4.8836 | val_main=1.948614
Epoch 004 | phase=1 | train_loss=4.6657 | val_main=1.948013
Epoch 005 | phase=1 | train_loss=4.5066 | val_main=1.945951
Epoch 006 | phase=1 | train_loss=4.4786 | val_main=1.946096
Epoch 007 | phase=1 | train_loss=4.4168 | val_main=1.945846
Epoch 008 | phase=1 | train_loss=4.3088 | val_main=1.946028
Epoch 009 | phase=1 | train_loss=4.2377 | val_main=1.945848
Epoch 010 | phase=1 | train_loss=4.1184 | val_main=1.945946
Epoch 011 | phase=1 | train_loss=4.1097 | val_main=1.946785
Epoch 012 | phase=1 | train_loss=4.0173 | val_main=1.947196
Epoch 013 | phase=1 | train_loss=3.8850 | val_main=1.947487
Epoch 014 | phase=1 | train_loss=3.5633 | val_main=1.946051
Epoch 015 | phase=1 | train_loss=3.2237 | val_main=1.945862
Epoch 016 | phase=0 | train_loss=3.8517 | val_main=1.864077
Epoch 017 | phase=0 | train_loss=3.6000 | val_main=1.502214
Epoch 018 | phase=0 | train_loss=3.2291 | val_main=1.035893
Epoch 019 | phase=0 | train_loss=3.0875 | val_main=0.991500
Epoch 020 | phase=0 | train_loss=2.8155 | val_main=1.024988
Epoch 021 | phase=0 | train_loss=2.7151 | val_main=1.114521
Epoch 022 | phase=0 | train_loss=2.5908 | val_main=1.026490
Epoch 023 | phase=0 | train_loss=2.6136 | val_main=1.085709
Epoch 024 | phase=0 | train_loss=2.4820 | val_main=1.242400
Epoch 025 | phase=0 | train_loss=2.2974 | val_main=1.247347
Epoch 026 | phase=0 | train_loss=2.2296 | val_main=1.250378
Epoch 027 | phase=0 | train_loss=2.0995 | val_main=1.261745
Epoch 028 | phase=0 | train_loss=2.0278 | val_main=1.248143
Epoch 029 | phase=0 | train_loss=1.9787 | val_main=1.351460
Epoch 030 | phase=0 | train_loss=1.9113 | val_main=1.455147
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 60
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.46841920
1 day(s) RMSE : 9.34664726
1 day(s) R2 : -0.03529398
1 day(s) Pearson r : 0.18461273
1 day(s) QLIKE : 0.36049401
3 day(s) MAE : 4.54869935
3 day(s) RMSE : 9.41913408
3 day(s) R2 : -0.05126877
3 day(s) Pearson r : 0.13698581
3 day(s) QLIKE : 0.37792058
5 day(s) MAE : 4.60240802
5 day(s) RMSE : 9.48156697
5 day(s) R2 : -0.06558524
5 day(s) Pearson r : 0.09720925
5 day(s) QLIKE : 0.39088521
10 day(s) MAE : 4.67468781
10 day(s) RMSE : 9.52560103
10 day(s) R2 : -0.07524695
10 day(s) Pearson r : 0.04463403
10 day(s) QLIKE : 0.40438674
full horizon MAE : 4.67468781
full horizon RMSE : 9.52560103
full horizon R2 : -0.07524695
full horizon Pearson r : 0.04463403
full horizon QLIKE : 0.40438674
--- Task 2 ---
1 day(s) MAE : 0.12099361
1 day(s) RMSE : 0.23714484
1 day(s) R2 : -0.05776722
1 day(s) Pearson r : -0.12231277
1 day(s) QLIKE : 15.31280369
3 day(s) MAE : 0.12913004
3 day(s) RMSE : 0.23729434
3 day(s) R2 : -0.05915293
3 day(s) Pearson r : -0.11832286
3 day(s) QLIKE : 15.12863930
5 day(s) MAE : 0.13129169
5 day(s) RMSE : 0.23745231
5 day(s) R2 : -0.06060624
5 day(s) Pearson r : -0.11320677
5 day(s) QLIKE : 14.77843094
10 day(s) MAE : 0.13362233
10 day(s) RMSE : 0.23655085
10 day(s) R2 : -0.05266620
10 day(s) Pearson r : -0.08164940
10 day(s) QLIKE : 14.21489509
full horizon MAE : 0.13362233
full horizon RMSE : 0.23655085
full horizon R2 : -0.05266620
full horizon Pearson r : -0.08164940
full horizon QLIKE : 14.21489509
--- Task 3 ---
1 day(s) MAE : 0.23111976
1 day(s) RMSE : 0.31178204
1 day(s) R2 : -0.76100466
1 day(s) Pearson r : -0.04063034
1 day(s) QLIKE : 0.17717095
3 day(s) MAE : 0.22976718
3 day(s) RMSE : 0.31113805
3 day(s) R2 : -0.75450923
3 day(s) Pearson r : -0.03908292
3 day(s) QLIKE : 0.17729272
5 day(s) MAE : 0.22987197
5 day(s) RMSE : 0.31068148
5 day(s) R2 : -0.75010802
5 day(s) Pearson r : -0.03665206
5 day(s) QLIKE : 0.17683439
10 day(s) MAE : 0.23084591
10 day(s) RMSE : 0.30795885
10 day(s) R2 : -0.72137841
10 day(s) Pearson r : -0.01774903
10 day(s) QLIKE : 0.17378386
full horizon MAE : 0.23084591
full horizon RMSE : 0.30795885
full horizon R2 : -0.72137841
full horizon Pearson r : -0.01774903
full horizon QLIKE : 0.17378386
--- Task 4 ---
1 day(s) MAE : 1.05547766
1 day(s) RMSE : 2.34647057
1 day(s) R2 : -0.10519769
1 day(s) Pearson r : -0.12694788
1 day(s) QLIKE : 1.54223853
3 day(s) MAE : 1.05996495
3 day(s) RMSE : 2.34656190
3 day(s) R2 : -0.10571260
3 day(s) Pearson r : -0.13652594
3 day(s) QLIKE : 1.51177112
5 day(s) MAE : 1.06284485
5 day(s) RMSE : 2.34636018
5 day(s) R2 : -0.10595173
5 day(s) Pearson r : -0.14559983
5 day(s) QLIKE : 1.53101951
10 day(s) MAE : 1.05825101
10 day(s) RMSE : 2.33491791
10 day(s) R2 : -0.09625197
10 day(s) Pearson r : -0.14396451
10 day(s) QLIKE : 1.53927370
full horizon MAE : 1.05825101
full horizon RMSE : 2.33491791
full horizon R2 : -0.09625197
full horizon Pearson r : -0.14396451
full horizon QLIKE : 1.53927370
--- Task 5 ---
1 day(s) MAE : 0.10183479
1 day(s) RMSE : 0.11596860
1 day(s) R2 : -3.36356026
1 day(s) Pearson r : 0.15714126
1 day(s) QLIKE : 3.29735790
3 day(s) MAE : 0.10187482
3 day(s) RMSE : 0.11599602
3 day(s) R2 : -3.36873422
3 day(s) Pearson r : 0.14764430
3 day(s) QLIKE : 3.30241644
5 day(s) MAE : 0.10191270
5 day(s) RMSE : 0.11601782
5 day(s) R2 : -3.37404069
5 day(s) Pearson r : 0.14501519
5 day(s) QLIKE : 3.34612649
10 day(s) MAE : 0.10202748
10 day(s) RMSE : 0.11608927
10 day(s) R2 : -3.38967891
10 day(s) Pearson r : 0.14523934
10 day(s) QLIKE : 3.31463005
full horizon MAE : 0.10202748
full horizon RMSE : 0.11608927
full horizon R2 : -3.38967891
full horizon Pearson r : 0.14523934
full horizon QLIKE : 3.31463005
--- Task 6 ---
1 day(s) MAE : 1.06773857
1 day(s) RMSE : 1.31292160
1 day(s) R2 : -0.96344276
1 day(s) Pearson r : -0.16267462
1 day(s) QLIKE : 0.07387049
3 day(s) MAE : 1.06979713
3 day(s) RMSE : 1.31287555
3 day(s) R2 : -0.96474383
3 day(s) Pearson r : -0.16701916
3 day(s) QLIKE : 0.07391207
5 day(s) MAE : 1.07207239
5 day(s) RMSE : 1.31200483
5 day(s) R2 : -0.96311648
5 day(s) Pearson r : -0.16582457
5 day(s) QLIKE : 0.07382041
10 day(s) MAE : 1.08060834
10 day(s) RMSE : 1.31820021
10 day(s) R2 : -0.98585885
10 day(s) Pearson r : -0.17062564
10 day(s) QLIKE : 0.07381569
full horizon MAE : 1.08060834
full horizon RMSE : 1.31820021
full horizon R2 : -0.98585885
full horizon Pearson r : -0.17062564
full horizon QLIKE : 0.07381569
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BTCUSDT/Custom_KAN_H10.pkl
Saved y_true min=0.505764, max=131.49
Saved y_pred min=2.67727, max=15.0112
=== BTCUSDT | H=10 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2399
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1727, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 27.572355514245807
Min value: -0.39529821705307827
Checking X_time_train_core:
Shape: (1727, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.8178490491226165
Checking y_train_core (log_mse scaled):
Shape: (1727, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.556915178048574
Min value: -20.868486718259952
Checking X_price_val:
Shape: (192, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.1802886452741648
Min value: -0.3973706893292718
Checking X_time_val:
Shape: (192, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_val (log_mse scaled):
Shape: (192, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3469968336435327
Min value: -4.249261289934393
Checking X_price_test:
Shape: (480, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.92735513679459
Min value: -0.3866725144992877
Checking X_time_test:
Shape: (480, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_test (log_mse scaled):
Shape: (480, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5827978941292526
Min value: -109.59273460949947
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 158s 536ms/step - loss: 0.7568 - val_loss: 2.3306 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 15s 274ms/step - loss: 0.4287 - val_loss: 2.1369 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 18s 336ms/step - loss: 0.3869 - val_loss: 2.5701 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 22s 413ms/step - loss: 0.3692 - val_loss: 1.5112 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 22s 403ms/step - loss: 0.3544 - val_loss: 1.5585 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 21s 390ms/step - loss: 0.3390 - val_loss: 2.2013 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 21s 391ms/step - loss: 0.3210 - val_loss: 1.3151 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 19s 354ms/step - loss: 0.3139 - val_loss: 1.3529 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 20s 368ms/step - loss: 0.3118 - val_loss: 1.0788 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 18s 341ms/step - loss: 0.2992 - val_loss: 1.0160 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 19s 349ms/step - loss: 0.3007 - val_loss: 2.6975 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=12 tf_ratio=0.421 -> TF=ON
Epoch 12/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 21s 395ms/step - loss: 0.2957 - val_loss: 1.0586 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=13 tf_ratio=0.368 -> TF=ON
Epoch 13/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 19s 343ms/step - loss: 0.2866 - val_loss: 1.1876 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=14 tf_ratio=0.316 -> TF=ON
Epoch 14/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 20s 371ms/step - loss: 0.2830 - val_loss: 1.1902 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=15 tf_ratio=0.263 -> TF=ON
Epoch 15/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 0s 356ms/step - loss: 0.2549
Epoch 15: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
54/54 ━━━━━━━━━━━━━━━━━━━━ 20s 365ms/step - loss: 0.2799 - val_loss: 1.1966 - learning_rate: 5.0000e-04
Epoch 15: early stopping
Restoring model weights from the end of the best epoch: 10.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 10
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.32967830
1 day(s) RMSE : 9.20767506
1 day(s) R2 : -0.00473597
1 day(s) Pearson r : 0.27456301
1 day(s) QLIKE : 0.34320530
3 day(s) MAE : 5.03509087
3 day(s) RMSE : 9.76230297
3 day(s) R2 : -0.12926630
3 day(s) Pearson r : 0.20317689
3 day(s) QLIKE : 0.41870079
5 day(s) MAE : 5.25859736
5 day(s) RMSE : 9.97053707
5 day(s) R2 : -0.17832494
5 day(s) Pearson r : 0.16404759
5 day(s) QLIKE : 0.43018618
10 day(s) MAE : 5.30697933
10 day(s) RMSE : 10.18888775
10 day(s) R2 : -0.23020362
10 day(s) Pearson r : 0.11161061
10 day(s) QLIKE : 0.41922734
full horizon MAE : 5.30697933
full horizon RMSE : 10.18888775
full horizon R2 : -0.23020362
full horizon Pearson r : 0.11161061
full horizon QLIKE : 0.41922734
--- Task 2 ---
1 day(s) MAE : 0.09032272
1 day(s) RMSE : 0.24538916
1 day(s) R2 : -0.13259205
1 day(s) Pearson r : 0.06749296
1 day(s) QLIKE : 3.38181918
3 day(s) MAE : 0.09510507
3 day(s) RMSE : 0.24837981
3 day(s) R2 : -0.16042346
3 day(s) Pearson r : -0.00246471
3 day(s) QLIKE : 4.24752113
5 day(s) MAE : 0.09762256
5 day(s) RMSE : 0.24978319
5 day(s) R2 : -0.17362079
5 day(s) Pearson r : -0.00148821
5 day(s) QLIKE : 16.17349950
10 day(s) MAE : 0.09954074
10 day(s) RMSE : 0.25083596
10 day(s) R2 : -0.18364443
10 day(s) Pearson r : -0.00099884
10 day(s) QLIKE : 9.98125563
full horizon MAE : 0.09954074
full horizon RMSE : 0.25083596
full horizon R2 : -0.18364443
full horizon Pearson r : -0.00099884
full horizon QLIKE : 9.98125563
--- Task 3 ---
1 day(s) MAE : 0.23265662
1 day(s) RMSE : 0.27098060
1 day(s) R2 : -0.33025449
1 day(s) Pearson r : -0.17298793
1 day(s) QLIKE : 0.14173906
3 day(s) MAE : 0.37226407
3 day(s) RMSE : 0.42747413
3 day(s) R2 : -2.31183803
3 day(s) Pearson r : -0.05459458
3 day(s) QLIKE : 0.25821640
5 day(s) MAE : 0.51646102
5 day(s) RMSE : 0.58919467
5 day(s) R2 : -5.29436218
5 day(s) Pearson r : -0.03888504
5 day(s) QLIKE : 1.00949542
10 day(s) MAE : 0.60052811
10 day(s) RMSE : 0.65552728
10 day(s) R2 : -6.79960329
10 day(s) Pearson r : -0.03050430
10 day(s) QLIKE : 0.62445733
full horizon MAE : 0.60052811
full horizon RMSE : 0.65552728
full horizon R2 : -6.79960329
full horizon Pearson r : -0.03050430
full horizon QLIKE : 0.62445733
--- Task 4 ---
1 day(s) MAE : 0.94532825
1 day(s) RMSE : 2.40511870
1 day(s) R2 : -0.16113515
1 day(s) Pearson r : 0.16392420
1 day(s) QLIKE : 1.18053512
3 day(s) MAE : 0.93542642
3 day(s) RMSE : 2.39195411
3 day(s) R2 : -0.14890447
3 day(s) Pearson r : -0.02321096
3 day(s) QLIKE : 1.24017962
5 day(s) MAE : 0.92726861
5 day(s) RMSE : 2.37672442
5 day(s) R2 : -0.13476117
5 day(s) Pearson r : -0.01299378
5 day(s) QLIKE : 1.26116145
10 day(s) MAE : 0.91821311
10 day(s) RMSE : 2.34959626
10 day(s) R2 : -0.11007836
10 day(s) Pearson r : -0.00692264
10 day(s) QLIKE : 1.28125426
full horizon MAE : 0.91821311
full horizon RMSE : 2.34959626
full horizon R2 : -0.11007836
full horizon Pearson r : -0.00692264
full horizon QLIKE : 1.28125426
--- Task 5 ---
1 day(s) MAE : 0.10187977
1 day(s) RMSE : 0.11602397
1 day(s) R2 : -3.36772785
1 day(s) Pearson r : -0.03157379
1 day(s) QLIKE : 1.39840007
3 day(s) MAE : 0.10191437
3 day(s) RMSE : 0.11604526
3 day(s) R2 : -3.37244381
3 day(s) Pearson r : -0.05218751
3 day(s) QLIKE : 1.72189558
5 day(s) MAE : 0.10194876
5 day(s) RMSE : 0.11606403
5 day(s) R2 : -3.37752594
5 day(s) Pearson r : -0.01453023
5 day(s) QLIKE : 1.77009613
10 day(s) MAE : 0.10206671
10 day(s) RMSE : 0.11613667
10 day(s) R2 : -3.39326406
10 day(s) Pearson r : -0.01215192
10 day(s) QLIKE : 1.60399984
full horizon MAE : 0.10206671
full horizon RMSE : 0.11613667
full horizon R2 : -3.39326406
full horizon Pearson r : -0.01215192
full horizon QLIKE : 1.60399984
--- Task 6 ---
1 day(s) MAE : 0.84344995
1 day(s) RMSE : 1.00842292
1 day(s) R2 : -0.15831338
1 day(s) Pearson r : 0.10767142
1 day(s) QLIKE : 0.06354944
3 day(s) MAE : 0.84955107
3 day(s) RMSE : 1.02046875
3 day(s) R2 : -0.18702054
3 day(s) Pearson r : 0.10738355
3 day(s) QLIKE : 0.06351213
5 day(s) MAE : 0.84550462
5 day(s) RMSE : 1.00997754
5 day(s) R2 : -0.16331849
5 day(s) Pearson r : 0.10726755
5 day(s) QLIKE : 0.06334066
10 day(s) MAE : 0.84510583
10 day(s) RMSE : 1.00809029
10 day(s) R2 : -0.16140712
10 day(s) Pearson r : 0.10826225
10 day(s) QLIKE : 0.06309717
full horizon MAE : 0.84510583
full horizon RMSE : 1.00809029
full horizon R2 : -0.16140712
full horizon Pearson r : 0.10826225
full horizon QLIKE : 0.06309717
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BTCUSDT/Custom_KAN_LSTM_H10.pkl
Saved y_true min=0.505764, max=131.49
Saved y_pred min=1.84483, max=23.7528
=== BTCUSDT | H=20 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 2399
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1727, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 27.572355514245807
Min value: -0.39529821705307827
Checking X_time_train_core:
Shape: (1727, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.8178490491226165
Checking y_train_core (log_mse scaled):
Shape: (1727, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.564043013270618
Min value: -20.83818392077683
Checking X_price_val:
Shape: (192, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.1802886452741648
Min value: -0.3973706893292718
Checking X_time_val:
Shape: (192, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_val (log_mse scaled):
Shape: (192, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3500532890780494
Min value: -4.25835726179007
Checking X_price_test:
Shape: (480, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.92735513679459
Min value: -0.3866725144992877
Checking X_time_test:
Shape: (480, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_test (log_mse scaled):
Shape: (480, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5862887708961093
Min value: -109.4415927913587
Epoch 001 | phase=1 | train_loss=5.0493 | val_main=1.916485
Epoch 002 | phase=1 | train_loss=5.0089 | val_main=1.916466
Epoch 003 | phase=1 | train_loss=4.9715 | val_main=1.915412
Epoch 004 | phase=1 | train_loss=4.7112 | val_main=1.914359
Epoch 005 | phase=1 | train_loss=4.5654 | val_main=1.911446
Epoch 006 | phase=1 | train_loss=4.4531 | val_main=1.911848
Epoch 007 | phase=1 | train_loss=4.3952 | val_main=1.911482
Epoch 008 | phase=1 | train_loss=4.3343 | val_main=1.911443
Epoch 009 | phase=1 | train_loss=4.2924 | val_main=1.911326
Epoch 010 | phase=1 | train_loss=4.1556 | val_main=1.911229
Epoch 011 | phase=1 | train_loss=4.1045 | val_main=1.911913
Epoch 012 | phase=1 | train_loss=4.0427 | val_main=1.912493
Epoch 013 | phase=1 | train_loss=3.6715 | val_main=1.912251
Epoch 014 | phase=1 | train_loss=3.3791 | val_main=1.911154
Epoch 015 | phase=1 | train_loss=3.1020 | val_main=1.911810
Epoch 016 | phase=0 | train_loss=3.8672 | val_main=1.859176
Epoch 017 | phase=0 | train_loss=3.6755 | val_main=1.568526
Epoch 018 | phase=0 | train_loss=3.3774 | val_main=1.152454
Epoch 019 | phase=0 | train_loss=3.2453 | val_main=1.052262
Epoch 020 | phase=0 | train_loss=3.0597 | val_main=1.060718
Epoch 021 | phase=0 | train_loss=2.9602 | val_main=1.164556
Epoch 022 | phase=0 | train_loss=2.8300 | val_main=1.153736
Epoch 023 | phase=0 | train_loss=2.7821 | val_main=1.247784
Epoch 024 | phase=0 | train_loss=2.6419 | val_main=1.334602
Epoch 025 | phase=0 | train_loss=2.4879 | val_main=1.423818
Epoch 026 | phase=0 | train_loss=2.4123 | val_main=1.485629
Epoch 027 | phase=0 | train_loss=2.2875 | val_main=1.330217
Epoch 028 | phase=0 | train_loss=2.2101 | val_main=1.272612
Epoch 029 | phase=0 | train_loss=2.1877 | val_main=1.364105
Epoch 030 | phase=0 | train_loss=2.1223 | val_main=1.520788
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 120
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 4.55576110
1 day(s) RMSE : 9.41134137
1 day(s) R2 : -0.04967544
1 day(s) Pearson r : 0.14832844
1 day(s) QLIKE : 0.37702536
3 day(s) MAE : 4.61521300
3 day(s) RMSE : 9.46109668
3 day(s) R2 : -0.06065651
3 day(s) Pearson r : 0.10767853
3 day(s) QLIKE : 0.39119797
5 day(s) MAE : 4.64228867
5 day(s) RMSE : 9.51026208
5 day(s) R2 : -0.07204480
5 day(s) Pearson r : 0.07659236
5 day(s) QLIKE : 0.40107959
10 day(s) MAE : 4.68962814
10 day(s) RMSE : 9.53237218
10 day(s) R2 : -0.07677614
10 day(s) Pearson r : 0.03939701
10 day(s) QLIKE : 0.41076681
20 day(s) MAE : 4.77487864
20 day(s) RMSE : 9.54526459
20 day(s) R2 : -0.08007218
20 day(s) Pearson r : -0.00372882
20 day(s) QLIKE : 0.41913431
full horizon MAE : 4.77487864
full horizon RMSE : 9.54526459
full horizon R2 : -0.08007218
full horizon Pearson r : -0.00372882
full horizon QLIKE : 0.41913431
--- Task 2 ---
1 day(s) MAE : 0.14746084
1 day(s) RMSE : 0.25347221
1 day(s) R2 : -0.20843540
1 day(s) Pearson r : -0.14340819
1 day(s) QLIKE : 18.35615076
3 day(s) MAE : 0.13851570
3 day(s) RMSE : 0.24445384
3 day(s) R2 : -0.12402929
3 day(s) Pearson r : -0.12626551
3 day(s) QLIKE : 18.41860985
5 day(s) MAE : 0.13841744
5 day(s) RMSE : 0.24409730
5 day(s) R2 : -0.12079798
5 day(s) Pearson r : -0.11062499
5 day(s) QLIKE : 17.98306682
10 day(s) MAE : 0.14613127
10 day(s) RMSE : 0.24557806
10 day(s) R2 : -0.13454253
10 day(s) Pearson r : -0.10626370
10 day(s) QLIKE : 17.47893326
20 day(s) MAE : 0.15061372
20 day(s) RMSE : 0.24428354
20 day(s) R2 : -0.12284490
20 day(s) Pearson r : -0.06740803
20 day(s) QLIKE : 16.94269192
full horizon MAE : 0.15061372
full horizon RMSE : 0.24428354
full horizon R2 : -0.12284490
full horizon Pearson r : -0.06740803
full horizon QLIKE : 16.94269192
--- Task 3 ---
1 day(s) MAE : 0.25643819
1 day(s) RMSE : 0.33656749
1 day(s) R2 : -1.05211948
1 day(s) Pearson r : -0.07660723
1 day(s) QLIKE : 0.19377279
3 day(s) MAE : 0.25723074
3 day(s) RMSE : 0.33881584
3 day(s) R2 : -1.08054356
3 day(s) Pearson r : -0.08040417
3 day(s) QLIKE : 0.19613411
5 day(s) MAE : 0.25896326
5 day(s) RMSE : 0.34006289
5 day(s) R2 : -1.09677873
5 day(s) Pearson r : -0.08333522
5 day(s) QLIKE : 0.19630275
10 day(s) MAE : 0.25863980
10 day(s) RMSE : 0.33745669
10 day(s) R2 : -1.06693621
10 day(s) Pearson r : -0.06945720
10 day(s) QLIKE : 0.19302514
20 day(s) MAE : 0.25317736
20 day(s) RMSE : 0.32339567
20 day(s) R2 : -0.90239125
20 day(s) Pearson r : -0.03419494
20 day(s) QLIKE : 0.18031805
full horizon MAE : 0.25317736
full horizon RMSE : 0.32339567
full horizon R2 : -0.90239125
full horizon Pearson r : -0.03419494
full horizon QLIKE : 0.18031805
--- Task 4 ---
1 day(s) MAE : 1.29526450
1 day(s) RMSE : 2.37135818
1 day(s) R2 : -0.12876635
1 day(s) Pearson r : -0.12955965
1 day(s) QLIKE : 1.58490220
3 day(s) MAE : 1.29444395
3 day(s) RMSE : 2.36877506
3 day(s) R2 : -0.12674561
3 day(s) Pearson r : -0.12796194
3 day(s) QLIKE : 1.57249457
5 day(s) MAE : 1.29806179
5 day(s) RMSE : 2.36664622
5 day(s) R2 : -0.12515795
5 day(s) Pearson r : -0.12868792
5 day(s) QLIKE : 1.55922555
10 day(s) MAE : 1.30924986
10 day(s) RMSE : 2.36444244
10 day(s) R2 : -0.12415098
10 day(s) Pearson r : -0.12663820
10 day(s) QLIKE : 1.52667806
20 day(s) MAE : 1.29086009
20 day(s) RMSE : 2.33703009
20 day(s) R2 : -0.10030993
20 day(s) Pearson r : -0.08501849
20 day(s) QLIKE : 1.41905273
full horizon MAE : 1.29086009
full horizon RMSE : 2.33703009
full horizon R2 : -0.10030993
full horizon Pearson r : -0.08501849
full horizon QLIKE : 1.41905273
--- Task 5 ---
1 day(s) MAE : 0.10187994
1 day(s) RMSE : 0.11602293
1 day(s) R2 : -3.36764941
1 day(s) Pearson r : 0.20395976
1 day(s) QLIKE : 3.15644154
3 day(s) MAE : 0.10191975
3 day(s) RMSE : 0.11604865
3 day(s) R2 : -3.37269909
3 day(s) Pearson r : 0.17569493
3 day(s) QLIKE : 3.25236781
5 day(s) MAE : 0.10196102
5 day(s) RMSE : 0.11607377
5 day(s) R2 : -3.37826079
5 day(s) Pearson r : 0.17036797
5 day(s) QLIKE : 3.29776166
10 day(s) MAE : 0.10207101
10 day(s) RMSE : 0.11613962
10 day(s) R2 : -3.39348698
10 day(s) Pearson r : 0.15683252
10 day(s) QLIKE : 3.30539949
20 day(s) MAE : 0.10228001
20 day(s) RMSE : 0.11627587
20 day(s) R2 : -3.41977375
20 day(s) Pearson r : 0.12395752
20 day(s) QLIKE : 3.27328180
full horizon MAE : 0.10228001
full horizon RMSE : 0.11627587
full horizon R2 : -3.41977375
full horizon Pearson r : 0.12395752
full horizon QLIKE : 3.27328180
--- Task 6 ---
1 day(s) MAE : 1.03753126
1 day(s) RMSE : 1.29762998
1 day(s) R2 : -0.91797259
1 day(s) Pearson r : -0.16224976
1 day(s) QLIKE : 0.07244159
3 day(s) MAE : 1.04247506
3 day(s) RMSE : 1.29896350
3 day(s) R2 : -0.92332515
3 day(s) Pearson r : -0.16677505
3 day(s) QLIKE : 0.07253204
5 day(s) MAE : 1.04645372
5 day(s) RMSE : 1.30064462
5 day(s) R2 : -0.92926773
5 day(s) Pearson r : -0.16132101
5 day(s) QLIKE : 0.07244349
10 day(s) MAE : 1.05279205
10 day(s) RMSE : 1.30687445
10 day(s) R2 : -0.95188108
10 day(s) Pearson r : -0.16648684
10 day(s) QLIKE : 0.07243477
20 day(s) MAE : 1.06404476
20 day(s) RMSE : 1.31115429
20 day(s) R2 : -0.97429683
20 day(s) Pearson r : -0.15729512
20 day(s) QLIKE : 0.07181331
full horizon MAE : 1.06404476
full horizon RMSE : 1.31115429
full horizon R2 : -0.97429683
full horizon Pearson r : -0.15729512
full horizon QLIKE : 0.07181331
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BTCUSDT/Custom_KAN_H20.pkl
Saved y_true min=0.505764, max=131.49
Saved y_pred min=2.77705, max=13.9634
=== BTCUSDT | H=20 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 2399
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (1727, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 27.572355514245807
Min value: -0.39529821705307827
Checking X_time_train_core:
Shape: (1727, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.8178490491226165
Checking y_train_core (log_mse scaled):
Shape: (1727, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.564043013270618
Min value: -20.83818392077683
Checking X_price_val:
Shape: (192, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.1802886452741648
Min value: -0.3973706893292718
Checking X_time_val:
Shape: (192, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_val (log_mse scaled):
Shape: (192, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3500532890780494
Min value: -4.25835726179007
Checking X_price_test:
Shape: (480, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.92735513679459
Min value: -0.3866725144992877
Checking X_time_test:
Shape: (480, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.510852258333365
Min value: -1.5280954648836782
Checking y_test (log_mse scaled):
Shape: (480, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5862887708961093
Min value: -109.4415927913587
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 164s 528ms/step - loss: 0.7461 - val_loss: 2.4542 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 20s 374ms/step - loss: 0.3970 - val_loss: 2.5640 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 25s 468ms/step - loss: 0.3517 - val_loss: 2.9173 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 26s 480ms/step - loss: 0.3344 - val_loss: 2.0586 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 26s 476ms/step - loss: 0.3190 - val_loss: 1.7564 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 27s 499ms/step - loss: 0.3091 - val_loss: 1.9950 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 26s 489ms/step - loss: 0.2977 - val_loss: 1.7651 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 27s 506ms/step - loss: 0.2847 - val_loss: 1.8764 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 27s 495ms/step - loss: 0.2747 - val_loss: 1.7102 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 27s 504ms/step - loss: 0.2742 - val_loss: 1.8558 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 27s 500ms/step - loss: 0.2597 - val_loss: 1.8159 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=12 tf_ratio=0.421 -> TF=ON
Epoch 12/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 26s 476ms/step - loss: 0.2576 - val_loss: 1.5302 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=13 tf_ratio=0.368 -> TF=ON
Epoch 13/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 26s 486ms/step - loss: 0.2515 - val_loss: 1.4402 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=14 tf_ratio=0.316 -> TF=ON
Epoch 14/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 27s 501ms/step - loss: 0.2464 - val_loss: 1.5442 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=15 tf_ratio=0.263 -> TF=ON
Epoch 15/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 26s 487ms/step - loss: 0.2463 - val_loss: 1.5399 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=16 tf_ratio=0.211 -> TF=ON
Epoch 16/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 29s 535ms/step - loss: 0.2418 - val_loss: 1.6704 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=17 tf_ratio=0.158 -> TF=ON
Epoch 17/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 26s 482ms/step - loss: 0.2331 - val_loss: 5.5578 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=18 tf_ratio=0.105 -> TF=ON
Epoch 18/20
54/54 ━━━━━━━━━━━━━━━━━━━━ 0s 507ms/step - loss: 0.2127
Epoch 18: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
54/54 ━━━━━━━━━━━━━━━━━━━━ 28s 521ms/step - loss: 0.2275 - val_loss: 2.3629 - learning_rate: 5.0000e-04
Epoch 18: early stopping
Restoring model weights from the end of the best epoch: 13.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 20
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 5.10259546
1 day(s) RMSE : 9.14681509
1 day(s) R2 : 0.00850214
1 day(s) Pearson r : 0.31565626
1 day(s) QLIKE : 0.37565575
3 day(s) MAE : 7.15673213
3 day(s) RMSE : 10.13450040
3 day(s) R2 : -0.21701657
3 day(s) Pearson r : 0.01720221
3 day(s) QLIKE : 0.40795743
5 day(s) MAE : 6.21777157
5 day(s) RMSE : 9.79472430
5 day(s) R2 : -0.13713597
5 day(s) Pearson r : 0.01155481
5 day(s) QLIKE : 0.42465603
10 day(s) MAE : 5.45924871
10 day(s) RMSE : 9.76158995
10 day(s) R2 : -0.12918361
10 day(s) Pearson r : 0.00698171
10 day(s) QLIKE : 0.45722184
20 day(s) MAE : 5.05987316
20 day(s) RMSE : 9.71482561
20 day(s) R2 : -0.11878556
20 day(s) Pearson r : 0.00459797
20 day(s) QLIKE : 0.42478437
full horizon MAE : 5.05987316
full horizon RMSE : 9.71482561
full horizon R2 : -0.11878556
full horizon Pearson r : 0.00459797
full horizon QLIKE : 0.42478437
--- Task 2 ---
1 day(s) MAE : 0.09371143
1 day(s) RMSE : 0.24754012
1 day(s) R2 : -0.15253448
1 day(s) Pearson r : 0.15218284
1 day(s) QLIKE : 3.38602540
3 day(s) MAE : 0.09713229
3 day(s) RMSE : 0.24952482
3 day(s) R2 : -0.17114703
3 day(s) Pearson r : 0.00157275
3 day(s) QLIKE : 5.97830659
5 day(s) MAE : 0.09883997
5 day(s) RMSE : 0.25046731
5 day(s) R2 : -0.18005832
5 day(s) Pearson r : 0.00087634
5 day(s) QLIKE : 11.94781212
10 day(s) MAE : 0.10014944
10 day(s) RMSE : 0.25117682
10 day(s) R2 : -0.18686349
10 day(s) Pearson r : 0.00045363
10 day(s) QLIKE : 10.33221574
20 day(s) MAE : 0.10087653
20 day(s) RMSE : 0.25154310
20 day(s) R2 : -0.19057336
20 day(s) Pearson r : 0.00017224
20 day(s) QLIKE : 6.92043695
full horizon MAE : 0.10087653
full horizon RMSE : 0.25154310
full horizon R2 : -0.19057336
full horizon Pearson r : 0.00017224
full horizon QLIKE : 6.92043695
--- Task 3 ---
1 day(s) MAE : 0.25659222
1 day(s) RMSE : 0.28225921
1 day(s) R2 : -0.44329320
1 day(s) Pearson r : -0.13825827
1 day(s) QLIKE : 0.13957354
3 day(s) MAE : 0.54629008
3 day(s) RMSE : 0.61961124
3 day(s) R2 : -5.95805697
3 day(s) Pearson r : 0.00059691
3 day(s) QLIKE : 2.53628716
5 day(s) MAE : 0.60400579
5 day(s) RMSE : 0.66110458
5 day(s) R2 : -6.92454705
5 day(s) Pearson r : 0.00082776
5 day(s) QLIKE : 2.31435183
10 day(s) MAE : 0.68004622
10 day(s) RMSE : 0.72805693
10 day(s) R2 : -8.62103207
10 day(s) Pearson r : 0.00172133
10 day(s) QLIKE : 1.66904781
20 day(s) MAE : 0.70459246
20 day(s) RMSE : 0.74457766
20 day(s) R2 : -9.08443192
20 day(s) Pearson r : 0.00218183
20 day(s) QLIKE : 1.23766548
full horizon MAE : 0.70459246
full horizon RMSE : 0.74457766
full horizon R2 : -9.08443192
full horizon Pearson r : 0.00218183
full horizon QLIKE : 1.23766548
--- Task 4 ---
1 day(s) MAE : 0.92325482
1 day(s) RMSE : 2.37794651
1 day(s) R2 : -0.13504716
1 day(s) Pearson r : 0.13099576
1 day(s) QLIKE : 1.21119054
3 day(s) MAE : 0.91763702
3 day(s) RMSE : 2.36656122
3 day(s) R2 : -0.12464050
3 day(s) Pearson r : 0.00514399
3 day(s) QLIKE : 1.21257725
5 day(s) MAE : 0.91242053
5 day(s) RMSE : 2.35348467
5 day(s) R2 : -0.11267814
5 day(s) Pearson r : 0.00149693
5 day(s) QLIKE : 1.22470247
10 day(s) MAE : 0.92082094
10 day(s) RMSE : 2.32661983
10 day(s) R2 : -0.08847386
10 day(s) Pearson r : 0.00083829
10 day(s) QLIKE : 1.25033675
20 day(s) MAE : 0.92537302
20 day(s) RMSE : 2.34845076
20 day(s) R2 : -0.11109027
20 day(s) Pearson r : -0.00565711
20 day(s) QLIKE : 1.24572087
full horizon MAE : 0.92537302
full horizon RMSE : 2.34845076
full horizon R2 : -0.11109027
full horizon Pearson r : -0.00565711
full horizon QLIKE : 1.24572087
--- Task 5 ---
1 day(s) MAE : 0.10188527
1 day(s) RMSE : 0.11602869
1 day(s) R2 : -3.36808357
1 day(s) Pearson r : -0.00359972
1 day(s) QLIKE : 0.33878841
3 day(s) MAE : 0.10163868
3 day(s) RMSE : 0.11580003
3 day(s) R2 : -3.35398329
3 day(s) Pearson r : -0.02029935
3 day(s) QLIKE : 2.74290528
5 day(s) MAE : 0.10090217
5 day(s) RMSE : 0.11513583
5 day(s) R2 : -3.30778866
5 day(s) Pearson r : -0.00121892
5 day(s) QLIKE : 4.00555613
10 day(s) MAE : 0.10097006
10 day(s) RMSE : 0.11516131
10 day(s) R2 : -3.31978149
10 day(s) Pearson r : 0.00774951
10 day(s) QLIKE : 4.07293606
20 day(s) MAE : 0.10153976
20 day(s) RMSE : 0.11561630
20 day(s) R2 : -3.36977441
20 day(s) Pearson r : 0.00741462
20 day(s) QLIKE : 3.46614341
full horizon MAE : 0.10153976
full horizon RMSE : 0.11561630
full horizon R2 : -3.36977441
full horizon Pearson r : 0.00741462
full horizon QLIKE : 3.46614341
--- Task 6 ---
1 day(s) MAE : 0.81624705
1 day(s) RMSE : 0.95341311
1 day(s) R2 : -0.03538745
1 day(s) Pearson r : -0.06734122
1 day(s) QLIKE : 0.06349578
3 day(s) MAE : 0.82266390
3 day(s) RMSE : 0.96995995
3 day(s) R2 : -0.07242375
3 day(s) Pearson r : -0.00007699
3 day(s) QLIKE : 0.06357716
5 day(s) MAE : 0.83209753
5 day(s) RMSE : 0.99073601
5 day(s) R2 : -0.11941493
5 day(s) Pearson r : -0.00126576
5 day(s) QLIKE : 0.06382186
10 day(s) MAE : 0.86018390
10 day(s) RMSE : 1.04142659
10 day(s) R2 : -0.23948978
10 day(s) Pearson r : -0.00550528
10 day(s) QLIKE : 0.06448723
20 day(s) MAE : 0.86802675
20 day(s) RMSE : 1.05116132
20 day(s) R2 : -0.26894731
20 day(s) Pearson r : -0.00567925
20 day(s) QLIKE : 0.06385976
full horizon MAE : 0.86802675
full horizon RMSE : 1.05116132
full horizon R2 : -0.26894731
full horizon Pearson r : -0.00567925
full horizon QLIKE : 0.06385976
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/BTCUSDT/Custom_KAN_LSTM_H20.pkl
Saved y_true min=0.505764, max=131.49
Saved y_pred min=3.79999, max=14.8047
=== EURUSD | H=1 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 3782
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2723, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 19.199765452525025
Min value: -0.7148707238463011
Checking X_time_train_core:
Shape: (2723, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.7683468970942753
Checking y_train_core (log_mse scaled):
Shape: (2723, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.747719247380912
Min value: -3.718602223017602
Checking X_price_val:
Shape: (302, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 7.408921165171427
Min value: -0.7103978753601863
Checking X_time_val:
Shape: (302, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_val (log_mse scaled):
Shape: (302, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.319059579642507
Min value: -2.9759282116206096
Checking X_price_test:
Shape: (757, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.131631151144182
Min value: -0.7173043382357188
Checking X_time_test:
Shape: (757, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_test (log_mse scaled):
Shape: (757, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3834322546316087
Min value: -5.3499699676080334
Epoch 001 | phase=1 | train_loss=4.9491 | val_main=1.189717
Epoch 002 | phase=1 | train_loss=4.5831 | val_main=1.188764
Epoch 003 | phase=1 | train_loss=4.4385 | val_main=1.188539
Epoch 004 | phase=1 | train_loss=4.2795 | val_main=1.188661
Epoch 005 | phase=1 | train_loss=4.1004 | val_main=1.188490
Epoch 006 | phase=1 | train_loss=3.9730 | val_main=1.188741
Epoch 007 | phase=1 | train_loss=3.8455 | val_main=1.189212
Epoch 008 | phase=1 | train_loss=3.7594 | val_main=1.189379
Epoch 009 | phase=1 | train_loss=3.5401 | val_main=1.189346
Epoch 010 | phase=1 | train_loss=3.4324 | val_main=1.189356
Epoch 011 | phase=1 | train_loss=3.3489 | val_main=1.189455
Epoch 012 | phase=1 | train_loss=3.2178 | val_main=1.189419
Epoch 013 | phase=1 | train_loss=3.0417 | val_main=1.189316
Epoch 014 | phase=1 | train_loss=2.9450 | val_main=1.189335
Epoch 015 | phase=1 | train_loss=2.8781 | val_main=1.189283
Epoch 016 | phase=0 | train_loss=3.6702 | val_main=0.826157
Epoch 017 | phase=0 | train_loss=3.3364 | val_main=0.575484
Epoch 018 | phase=0 | train_loss=2.9042 | val_main=0.427556
Epoch 019 | phase=0 | train_loss=2.6866 | val_main=0.419603
Epoch 020 | phase=0 | train_loss=2.5815 | val_main=0.409484
Epoch 021 | phase=0 | train_loss=2.4291 | val_main=0.412038
Epoch 022 | phase=0 | train_loss=2.3013 | val_main=0.376453
Epoch 023 | phase=0 | train_loss=2.2064 | val_main=0.372456
Epoch 024 | phase=0 | train_loss=2.0367 | val_main=0.380145
Epoch 025 | phase=0 | train_loss=1.8998 | val_main=0.357460
Epoch 026 | phase=0 | train_loss=1.8025 | val_main=0.359772
Epoch 027 | phase=0 | train_loss=1.7061 | val_main=0.355926
Epoch 028 | phase=0 | train_loss=1.6477 | val_main=0.349449
Epoch 029 | phase=0 | train_loss=1.5600 | val_main=0.350542
Epoch 030 | phase=0 | train_loss=1.4152 | val_main=0.344691
Epoch 031 | phase=2 | train_loss=0.1441 | val_main=0.362259
Epoch 032 | phase=2 | train_loss=0.1400 | val_main=0.343762
Epoch 033 | phase=2 | train_loss=0.1229 | val_main=0.359692
Epoch 034 | phase=2 | train_loss=0.1167 | val_main=0.369957
Epoch 035 | phase=2 | train_loss=0.1099 | val_main=0.378125
Epoch 036 | phase=2 | train_loss=0.1009 | val_main=0.393970
Epoch 037 | phase=2 | train_loss=0.0959 | val_main=0.383327
Epoch 038 | phase=2 | train_loss=0.0906 | val_main=0.406622
Epoch 039 | phase=2 | train_loss=0.0852 | val_main=0.402736
Epoch 040 | phase=2 | train_loss=0.0782 | val_main=0.422376
Epoch 041 | phase=2 | train_loss=0.0731 | val_main=0.424579
Epoch 042 | phase=2 | train_loss=0.0711 | val_main=0.429025
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 6
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.08815789
1 day(s) RMSE : 0.17309408
1 day(s) R2 : 0.34357247
1 day(s) Pearson r : 0.59980755
1 day(s) QLIKE : 0.42895226
full horizon MAE : 0.08815789
full horizon RMSE : 0.17309408
full horizon R2 : 0.34357247
full horizon Pearson r : 0.59980755
full horizon QLIKE : 0.42895226
--- Task 2 ---
1 day(s) MAE : 0.03443979
1 day(s) RMSE : 0.05443198
1 day(s) R2 : -0.50406138
1 day(s) Pearson r : -0.04452532
1 day(s) QLIKE : 19.22167861
full horizon MAE : 0.03443979
full horizon RMSE : 0.05443198
full horizon R2 : -0.50406138
full horizon Pearson r : -0.04452532
full horizon QLIKE : 19.22167861
--- Task 3 ---
1 day(s) MAE : 0.82175664
1 day(s) RMSE : 1.24762956
1 day(s) R2 : -10.52396334
1 day(s) Pearson r : -0.01867348
1 day(s) QLIKE : 23.54696320
full horizon MAE : 0.82175664
full horizon RMSE : 1.24762956
full horizon R2 : -10.52396334
full horizon Pearson r : -0.01867348
full horizon QLIKE : 23.54696320
--- Task 4 ---
1 day(s) MAE : 0.03953752
1 day(s) RMSE : 0.06383919
1 day(s) R2 : -0.45874167
1 day(s) Pearson r : -0.10693423
1 day(s) QLIKE : 14.12030956
full horizon MAE : 0.03953752
full horizon RMSE : 0.06383919
full horizon R2 : -0.45874167
full horizon Pearson r : -0.10693423
full horizon QLIKE : 14.12030956
--- Task 5 ---
1 day(s) MAE : 0.00880799
1 day(s) RMSE : 0.01389087
1 day(s) R2 : -4.56074994
1 day(s) Pearson r : 0.21069232
1 day(s) QLIKE : 17.38174351
full horizon MAE : 0.00880799
full horizon RMSE : 0.01389087
full horizon R2 : -4.56074994
full horizon Pearson r : 0.21069232
full horizon QLIKE : 17.38174351
--- Task 6 ---
1 day(s) MAE : 1.18041513
1 day(s) RMSE : 3.52821210
1 day(s) R2 : -7.14670575
1 day(s) Pearson r : 0.17864742
1 day(s) QLIKE : 0.03015396
full horizon MAE : 1.18041513
full horizon RMSE : 3.52821210
full horizon R2 : -7.14670575
full horizon Pearson r : 0.17864742
full horizon QLIKE : 0.03015396
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/EURUSD/Custom_KAN_H1.pkl
Saved y_true min=9.56189e-05, max=2.66457
Saved y_pred min=0.00224037, max=1.21855
=== EURUSD | H=1 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 3782
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2723, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 19.199765452525025
Min value: -0.7148707238463011
Checking X_time_train_core:
Shape: (2723, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.7683468970942753
Checking y_train_core (log_mse scaled):
Shape: (2723, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.747719247380912
Min value: -3.718602223017602
Checking X_price_val:
Shape: (302, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 7.408921165171427
Min value: -0.7103978753601863
Checking X_time_val:
Shape: (302, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_val (log_mse scaled):
Shape: (302, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.319059579642507
Min value: -2.9759282116206096
Checking X_price_test:
Shape: (757, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.131631151144182
Min value: -0.7173043382357188
Checking X_time_test:
Shape: (757, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_test (log_mse scaled):
Shape: (757, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3834322546316087
Min value: -5.3499699676080334
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 150s 294ms/step - loss: 0.9784 - val_loss: 1.0328 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 16s 184ms/step - loss: 0.9266 - val_loss: 0.8935 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 17s 197ms/step - loss: 0.9300 - val_loss: 0.8793 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 17s 194ms/step - loss: 0.9022 - val_loss: 0.8725 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 17s 203ms/step - loss: 0.8984 - val_loss: 0.8811 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 17s 204ms/step - loss: 0.8853 - val_loss: 0.8954 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 17s 199ms/step - loss: 0.8764 - val_loss: 0.8727 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 17s 201ms/step - loss: 0.8937 - val_loss: 1.0827 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 17s 201ms/step - loss: 0.9277 - val_loss: 0.8658 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 17s 200ms/step - loss: 0.9094 - val_loss: 0.8745 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 17s 202ms/step - loss: 0.9004 - val_loss: 0.8659 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=12 tf_ratio=0.421 -> TF=ON
Epoch 12/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 17s 202ms/step - loss: 0.8929 - val_loss: 0.8632 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=13 tf_ratio=0.368 -> TF=ON
Epoch 13/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 17s 201ms/step - loss: 0.8849 - val_loss: 0.8632 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=14 tf_ratio=0.316 -> TF=ON
Epoch 14/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 19s 225ms/step - loss: 0.8791 - val_loss: 0.8646 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=15 tf_ratio=0.263 -> TF=ON
Epoch 15/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 18s 212ms/step - loss: 0.8780 - val_loss: 0.8649 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=16 tf_ratio=0.211 -> TF=ON
Epoch 16/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 18s 213ms/step - loss: 0.8686 - val_loss: 0.8700 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=17 tf_ratio=0.158 -> TF=ON
Epoch 17/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 0s 206ms/step - loss: 0.8813
Epoch 17: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
86/86 ━━━━━━━━━━━━━━━━━━━━ 18s 212ms/step - loss: 0.8743 - val_loss: 1.0731 - learning_rate: 5.0000e-04
Epoch 17: early stopping
Restoring model weights from the end of the best epoch: 12.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 1
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.10766944
1 day(s) RMSE : 0.20219395
1 day(s) R2 : 0.10430812
1 day(s) Pearson r : 0.41305027
1 day(s) QLIKE : 0.48990247
full horizon MAE : 0.10766944
full horizon RMSE : 0.20219395
full horizon R2 : 0.10430812
full horizon Pearson r : 0.41305027
full horizon QLIKE : 0.48990247
--- Task 2 ---
1 day(s) MAE : 0.03446843
1 day(s) RMSE : 0.05584583
1 day(s) R2 : -0.58321144
1 day(s) Pearson r : -0.08214266
1 day(s) QLIKE : 13.67497443
full horizon MAE : 0.03446843
full horizon RMSE : 0.05584583
full horizon R2 : -0.58321144
full horizon Pearson r : -0.08214266
full horizon QLIKE : 13.67497443
--- Task 3 ---
1 day(s) MAE : 0.64213141
1 day(s) RMSE : 0.73495240
1 day(s) R2 : -2.99897774
1 day(s) Pearson r : 0.18658454
1 day(s) QLIKE : 3.65706645
full horizon MAE : 0.64213141
full horizon RMSE : 0.73495240
full horizon R2 : -2.99897774
full horizon Pearson r : 0.18658454
full horizon QLIKE : 3.65706645
--- Task 4 ---
1 day(s) MAE : 0.03256461
1 day(s) RMSE : 0.06170885
1 day(s) R2 : -0.36300846
1 day(s) Pearson r : -0.19075945
1 day(s) QLIKE : 6.23364200
full horizon MAE : 0.03256461
full horizon RMSE : 0.06170885
full horizon R2 : -0.36300846
full horizon Pearson r : -0.19075945
full horizon QLIKE : 6.23364200
--- Task 5 ---
1 day(s) MAE : 0.00512760
1 day(s) RMSE : 0.00780969
1 day(s) R2 : -0.75768994
1 day(s) Pearson r : 0.41595537
1 day(s) QLIKE : 13.59205659
full horizon MAE : 0.00512760
full horizon RMSE : 0.00780969
full horizon R2 : -0.75768994
full horizon Pearson r : 0.41595537
full horizon QLIKE : 13.59205659
--- Task 6 ---
1 day(s) MAE : 1.31008082
1 day(s) RMSE : 1.61721115
1 day(s) R2 : -0.71161511
1 day(s) Pearson r : -0.24099428
1 day(s) QLIKE : 0.04352173
full horizon MAE : 1.31008082
full horizon RMSE : 1.61721115
full horizon R2 : -0.71161511
full horizon Pearson r : -0.24099428
full horizon QLIKE : 0.04352173
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/EURUSD/Custom_KAN_LSTM_H1.pkl
Saved y_true min=9.56189e-05, max=2.66457
Saved y_pred min=0.0522551, max=0.272158
=== EURUSD | H=5 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 3782
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2723, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 19.199765452525025
Min value: -0.7148707238463011
Checking X_time_train_core:
Shape: (2723, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.7683468970942753
Checking y_train_core (log_mse scaled):
Shape: (2723, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.747698926836269
Min value: -3.7204515821714925
Checking X_price_val:
Shape: (302, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 7.408921165171427
Min value: -0.7103978753601863
Checking X_time_val:
Shape: (302, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_val (log_mse scaled):
Shape: (302, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3194271117573377
Min value: -2.977504877870329
Checking X_price_test:
Shape: (757, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.131631151144182
Min value: -0.7173043382357188
Checking X_time_test:
Shape: (757, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_test (log_mse scaled):
Shape: (757, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3838234229198108
Min value: -5.352418327671095
Epoch 001 | phase=1 | train_loss=4.9940 | val_main=1.203375
Epoch 002 | phase=1 | train_loss=4.6248 | val_main=1.202117
Epoch 003 | phase=1 | train_loss=4.4663 | val_main=1.202000
Epoch 004 | phase=1 | train_loss=4.3465 | val_main=1.202120
Epoch 005 | phase=1 | train_loss=4.2458 | val_main=1.201796
Epoch 006 | phase=1 | train_loss=4.1529 | val_main=1.201829
Epoch 007 | phase=1 | train_loss=3.9906 | val_main=1.201987
Epoch 008 | phase=1 | train_loss=3.9169 | val_main=1.201990
Epoch 009 | phase=1 | train_loss=3.7260 | val_main=1.201890
Epoch 010 | phase=1 | train_loss=3.6142 | val_main=1.202271
Epoch 011 | phase=1 | train_loss=3.4321 | val_main=1.202025
Epoch 012 | phase=1 | train_loss=3.3184 | val_main=1.202177
Epoch 013 | phase=1 | train_loss=3.0951 | val_main=1.202007
Epoch 014 | phase=1 | train_loss=2.9953 | val_main=1.201985
Epoch 015 | phase=1 | train_loss=2.8475 | val_main=1.202243
Epoch 016 | phase=0 | train_loss=3.6638 | val_main=0.965766
Epoch 017 | phase=0 | train_loss=3.4534 | val_main=0.886018
Epoch 018 | phase=0 | train_loss=3.2850 | val_main=0.820569
Epoch 019 | phase=0 | train_loss=3.1268 | val_main=0.714860
Epoch 020 | phase=0 | train_loss=2.9417 | val_main=0.598589
Epoch 021 | phase=0 | train_loss=2.7258 | val_main=0.529892
Epoch 022 | phase=0 | train_loss=2.6052 | val_main=0.433917
Epoch 023 | phase=0 | train_loss=2.4820 | val_main=0.389224
Epoch 024 | phase=0 | train_loss=2.3154 | val_main=0.382612
Epoch 025 | phase=0 | train_loss=2.1660 | val_main=0.361810
Epoch 026 | phase=0 | train_loss=2.0976 | val_main=0.359725
Epoch 027 | phase=0 | train_loss=2.0313 | val_main=0.353338
Epoch 028 | phase=0 | train_loss=1.9753 | val_main=0.342450
Epoch 029 | phase=0 | train_loss=1.9022 | val_main=0.348529
Epoch 030 | phase=0 | train_loss=1.8952 | val_main=0.345696
Epoch 031 | phase=2 | train_loss=0.2066 | val_main=0.346874
Epoch 032 | phase=2 | train_loss=0.2035 | val_main=0.345580
Epoch 033 | phase=2 | train_loss=0.1911 | val_main=0.347458
Epoch 034 | phase=2 | train_loss=0.1886 | val_main=0.346024
Epoch 035 | phase=2 | train_loss=0.1805 | val_main=0.349525
Epoch 036 | phase=2 | train_loss=0.1728 | val_main=0.356478
Epoch 037 | phase=2 | train_loss=0.1678 | val_main=0.349051
Epoch 038 | phase=2 | train_loss=0.1614 | val_main=0.354963
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 30
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.08836400
1 day(s) RMSE : 0.17753344
1 day(s) R2 : 0.30946981
1 day(s) Pearson r : 0.57055601
1 day(s) QLIKE : 0.36900157
3 day(s) MAE : 0.09096749
3 day(s) RMSE : 0.18251161
3 day(s) R2 : 0.26457066
3 day(s) Pearson r : 0.52865547
3 day(s) QLIKE : 0.39110105
5 day(s) MAE : 0.09204536
5 day(s) RMSE : 0.18405891
5 day(s) R2 : 0.24354871
5 day(s) Pearson r : 0.50923234
5 day(s) QLIKE : 0.40632532
full horizon MAE : 0.09204536
full horizon RMSE : 0.18405891
full horizon R2 : 0.24354871
full horizon Pearson r : 0.50923234
full horizon QLIKE : 0.40632532
--- Task 2 ---
1 day(s) MAE : 0.03469110
1 day(s) RMSE : 0.05518527
1 day(s) R2 : -0.54597916
1 day(s) Pearson r : -0.08075569
1 day(s) QLIKE : 19.25089995
3 day(s) MAE : 0.03455853
3 day(s) RMSE : 0.05499603
3 day(s) R2 : -0.53500736
3 day(s) Pearson r : -0.06903299
3 day(s) QLIKE : 19.21869650
5 day(s) MAE : 0.03453898
5 day(s) RMSE : 0.05503580
5 day(s) R2 : -0.53619438
5 day(s) Pearson r : -0.05674600
5 day(s) QLIKE : 19.18286984
full horizon MAE : 0.03453898
full horizon RMSE : 0.05503580
full horizon R2 : -0.53619438
full horizon Pearson r : -0.05674600
full horizon QLIKE : 19.18286984
--- Task 3 ---
1 day(s) MAE : 0.56990036
1 day(s) RMSE : 0.78482498
1 day(s) R2 : -3.56011921
1 day(s) Pearson r : -0.00184255
1 day(s) QLIKE : 12.09273454
3 day(s) MAE : 0.60823744
3 day(s) RMSE : 0.88553489
3 day(s) R2 : -4.80492185
3 day(s) Pearson r : -0.02649008
3 day(s) QLIKE : 12.89113484
5 day(s) MAE : 0.59794769
5 day(s) RMSE : 0.86918115
5 day(s) R2 : -4.59121664
5 day(s) Pearson r : -0.01115928
5 day(s) QLIKE : 12.82983336
full horizon MAE : 0.59794769
full horizon RMSE : 0.86918115
full horizon R2 : -4.59121664
full horizon Pearson r : -0.01115928
full horizon QLIKE : 12.82983336
--- Task 4 ---
1 day(s) MAE : 0.03445251
1 day(s) RMSE : 0.06055503
1 day(s) R2 : -0.31251417
1 day(s) Pearson r : -0.15387042
1 day(s) QLIKE : 10.83778798
3 day(s) MAE : 0.03502128
3 day(s) RMSE : 0.06066420
3 day(s) R2 : -0.31705271
3 day(s) Pearson r : -0.16834435
3 day(s) QLIKE : 10.89924912
5 day(s) MAE : 0.03525375
5 day(s) RMSE : 0.06068626
5 day(s) R2 : -0.31765357
5 day(s) Pearson r : -0.17277686
5 day(s) QLIKE : 10.87843544
full horizon MAE : 0.03525375
full horizon RMSE : 0.06068626
full horizon R2 : -0.31765357
full horizon Pearson r : -0.17277686
full horizon QLIKE : 10.87843544
--- Task 5 ---
1 day(s) MAE : 0.00680682
1 day(s) RMSE : 0.01016224
1 day(s) R2 : -1.97614131
1 day(s) Pearson r : 0.15917150
1 day(s) QLIKE : 16.50622822
3 day(s) MAE : 0.00677319
3 day(s) RMSE : 0.01001334
3 day(s) R2 : -1.88956359
3 day(s) Pearson r : 0.14623142
3 day(s) QLIKE : 16.59934776
5 day(s) MAE : 0.00675100
5 day(s) RMSE : 0.00995291
5 day(s) R2 : -1.85479179
5 day(s) Pearson r : 0.13780777
5 day(s) QLIKE : 16.62424435
full horizon MAE : 0.00675100
full horizon RMSE : 0.00995291
full horizon R2 : -1.85479179
full horizon Pearson r : 0.13780777
full horizon QLIKE : 16.62424435
--- Task 6 ---
1 day(s) MAE : 1.39770881
1 day(s) RMSE : 3.50928164
1 day(s) R2 : -7.05951874
1 day(s) Pearson r : 0.16049505
1 day(s) QLIKE : 0.04080721
3 day(s) MAE : 1.38560642
3 day(s) RMSE : 3.52456736
3 day(s) R2 : -7.12701464
3 day(s) Pearson r : 0.15534830
3 day(s) QLIKE : 0.04036534
5 day(s) MAE : 1.38399784
5 day(s) RMSE : 3.57450792
5 day(s) R2 : -7.35452717
5 day(s) Pearson r : 0.15575666
5 day(s) QLIKE : 0.04021022
full horizon MAE : 1.38399784
full horizon RMSE : 3.57450792
full horizon R2 : -7.35452717
full horizon Pearson r : 0.15575666
full horizon QLIKE : 0.04021022
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/EURUSD/Custom_KAN_H5.pkl
Saved y_true min=9.56189e-05, max=2.66457
Saved y_pred min=0.00295811, max=1.07163
=== EURUSD | H=5 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 3782
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2723, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 19.199765452525025
Min value: -0.7148707238463011
Checking X_time_train_core:
Shape: (2723, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.7683468970942753
Checking y_train_core (log_mse scaled):
Shape: (2723, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.747698926836269
Min value: -3.7204515821714925
Checking X_price_val:
Shape: (302, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 7.408921165171427
Min value: -0.7103978753601863
Checking X_time_val:
Shape: (302, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_val (log_mse scaled):
Shape: (302, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3194271117573377
Min value: -2.977504877870329
Checking X_price_test:
Shape: (757, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.131631151144182
Min value: -0.7173043382357188
Checking X_time_test:
Shape: (757, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_test (log_mse scaled):
Shape: (757, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3838234229198108
Min value: -5.352418327671095
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 153s 332ms/step - loss: 0.6811 - val_loss: 1.2708 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 21s 247ms/step - loss: 0.4777 - val_loss: 1.4143 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 22s 254ms/step - loss: 0.4549 - val_loss: 1.1574 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 21s 247ms/step - loss: 0.4427 - val_loss: 1.0217 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 24s 275ms/step - loss: 0.4386 - val_loss: 0.9786 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 23s 266ms/step - loss: 0.4343 - val_loss: 1.0142 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 22s 256ms/step - loss: 0.4306 - val_loss: 0.9575 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 29s 334ms/step - loss: 0.4311 - val_loss: 0.9709 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 27s 319ms/step - loss: 0.4260 - val_loss: 0.9669 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 29s 340ms/step - loss: 0.4238 - val_loss: 0.9894 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 26s 303ms/step - loss: 0.4220 - val_loss: 0.9850 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=12 tf_ratio=0.421 -> TF=ON
Epoch 12/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 0s 288ms/step - loss: 0.4234
Epoch 12: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
86/86 ━━━━━━━━━━━━━━━━━━━━ 25s 295ms/step - loss: 0.4218 - val_loss: 0.9781 - learning_rate: 5.0000e-04
Epoch 12: early stopping
Restoring model weights from the end of the best epoch: 7.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 5
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.11508104
1 day(s) RMSE : 0.21192773
1 day(s) R2 : 0.01599360
1 day(s) Pearson r : 0.42510042
1 day(s) QLIKE : 0.48691850
3 day(s) MAE : 0.11795942
3 day(s) RMSE : 0.21378389
3 day(s) R2 : -0.00904358
3 day(s) Pearson r : 0.38456948
3 day(s) QLIKE : 0.51411824
5 day(s) MAE : 0.11939818
5 day(s) RMSE : 0.21495711
5 day(s) R2 : -0.03174146
5 day(s) Pearson r : 0.36901550
5 day(s) QLIKE : 0.52225088
full horizon MAE : 0.11939818
full horizon RMSE : 0.21495711
full horizon R2 : -0.03174146
full horizon Pearson r : 0.36901550
full horizon QLIKE : 0.52225088
--- Task 2 ---
1 day(s) MAE : 0.03459759
1 day(s) RMSE : 0.05621061
1 day(s) R2 : -0.60396187
1 day(s) Pearson r : -0.14581363
1 day(s) QLIKE : 11.89216410
3 day(s) MAE : 0.03456889
3 day(s) RMSE : 0.05622003
3 day(s) R2 : -0.60409403
3 day(s) Pearson r : -0.13038951
3 day(s) QLIKE : 13.41048951
5 day(s) MAE : 0.03451723
5 day(s) RMSE : 0.05621673
5 day(s) R2 : -0.60282775
5 day(s) Pearson r : -0.10023806
5 day(s) QLIKE : 12.53446312
full horizon MAE : 0.03451723
full horizon RMSE : 0.05621673
full horizon R2 : -0.60282775
full horizon Pearson r : -0.10023806
full horizon QLIKE : 12.53446312
--- Task 3 ---
1 day(s) MAE : 0.71596395
1 day(s) RMSE : 0.79686857
1 day(s) R2 : -3.70114834
1 day(s) Pearson r : 0.17674577
1 day(s) QLIKE : 3.50647304
3 day(s) MAE : 0.74802032
3 day(s) RMSE : 0.83101568
3 day(s) R2 : -4.11214846
3 day(s) Pearson r : 0.09622442
3 day(s) QLIKE : 8.22359709
5 day(s) MAE : 0.75510457
5 day(s) RMSE : 0.83842450
5 day(s) R2 : -4.20251862
5 day(s) Pearson r : 0.07220476
5 day(s) QLIKE : 6.69851627
full horizon MAE : 0.75510457
full horizon RMSE : 0.83842450
full horizon R2 : -4.20251862
full horizon Pearson r : 0.07220476
full horizon QLIKE : 6.69851627
--- Task 4 ---
1 day(s) MAE : 0.03282058
1 day(s) RMSE : 0.06196982
1 day(s) R2 : -0.37456129
1 day(s) Pearson r : -0.18103498
1 day(s) QLIKE : 5.00405993
3 day(s) MAE : 0.03322944
3 day(s) RMSE : 0.06189551
3 day(s) R2 : -0.37106014
3 day(s) Pearson r : -0.15375524
3 day(s) QLIKE : 10.38550411
5 day(s) MAE : 0.03364608
5 day(s) RMSE : 0.06193931
5 day(s) R2 : -0.37262899
5 day(s) Pearson r : -0.16822734
5 day(s) QLIKE : 10.45188847
full horizon MAE : 0.03364608
full horizon RMSE : 0.06193931
full horizon R2 : -0.37262899
full horizon Pearson r : -0.16822734
full horizon QLIKE : 10.45188847
--- Task 5 ---
1 day(s) MAE : 0.00512700
1 day(s) RMSE : 0.00780876
1 day(s) R2 : -0.75727152
1 day(s) Pearson r : 0.19801251
1 day(s) QLIKE : 12.74594658
3 day(s) MAE : 0.00512743
3 day(s) RMSE : 0.00780943
3 day(s) R2 : -0.75757254
3 day(s) Pearson r : 0.09409271
3 day(s) QLIKE : 13.27851958
5 day(s) MAE : 0.00512752
5 day(s) RMSE : 0.00780957
5 day(s) R2 : -0.75763312
5 day(s) Pearson r : 0.07057009
5 day(s) QLIKE : 11.96404282
full horizon MAE : 0.00512752
full horizon RMSE : 0.00780957
full horizon R2 : -0.75763312
full horizon Pearson r : 0.07057009
full horizon QLIKE : 11.96404282
--- Task 6 ---
1 day(s) MAE : 1.44011873
1 day(s) RMSE : 1.75067457
1 day(s) R2 : -1.00578096
1 day(s) Pearson r : 0.15147746
1 day(s) QLIKE : 0.03812034
3 day(s) MAE : 1.47609494
3 day(s) RMSE : 1.79712102
3 day(s) R2 : -1.11287623
3 day(s) Pearson r : 0.17045998
3 day(s) QLIKE : 0.03791437
5 day(s) MAE : 1.49292318
5 day(s) RMSE : 1.81972234
5 day(s) R2 : -1.16520789
5 day(s) Pearson r : 0.20048136
5 day(s) QLIKE : 0.03667145
full horizon MAE : 1.49292318
full horizon RMSE : 1.81972234
full horizon R2 : -1.16520789
full horizon Pearson r : 0.20048136
full horizon QLIKE : 0.03667145
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/EURUSD/Custom_KAN_LSTM_H5.pkl
Saved y_true min=9.56189e-05, max=2.66457
Saved y_pred min=0.0458121, max=0.246393
=== EURUSD | H=10 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 3782
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2723, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 19.199765452525025
Min value: -0.7148707238463011
Checking X_time_train_core:
Shape: (2723, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.7683468970942753
Checking y_train_core (log_mse scaled):
Shape: (2723, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.7476503800701995
Min value: -3.719996871136402
Checking X_price_val:
Shape: (302, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 7.408921165171427
Min value: -0.7103978753601863
Checking X_time_val:
Shape: (302, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_val (log_mse scaled):
Shape: (302, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3197553444271937
Min value: -2.9770657245458123
Checking X_price_test:
Shape: (757, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.131631151144182
Min value: -0.7173043382357188
Checking X_time_test:
Shape: (757, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_test (log_mse scaled):
Shape: (757, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3841503070956698
Min value: -5.351929442359245
Epoch 001 | phase=1 | train_loss=4.9952 | val_main=1.209539
Epoch 002 | phase=1 | train_loss=4.6494 | val_main=1.211823
Epoch 003 | phase=1 | train_loss=4.4827 | val_main=1.212710
Epoch 004 | phase=1 | train_loss=4.3714 | val_main=1.212207
Epoch 005 | phase=1 | train_loss=4.2574 | val_main=1.212573
Epoch 006 | phase=1 | train_loss=4.1090 | val_main=1.212730
Epoch 007 | phase=1 | train_loss=3.9448 | val_main=1.213538
Epoch 008 | phase=1 | train_loss=3.8639 | val_main=1.213764
Epoch 009 | phase=1 | train_loss=3.6649 | val_main=1.214872
Epoch 010 | phase=1 | train_loss=3.5370 | val_main=1.214828
Epoch 011 | phase=1 | train_loss=3.4194 | val_main=1.214975
Epoch 012 | phase=1 | train_loss=3.2967 | val_main=1.214589
Epoch 013 | phase=1 | train_loss=3.1062 | val_main=1.214220
Epoch 014 | phase=1 | train_loss=3.0157 | val_main=1.213969
Epoch 015 | phase=1 | train_loss=2.9420 | val_main=1.214238
Epoch 016 | phase=0 | train_loss=3.7315 | val_main=0.999687
Epoch 017 | phase=0 | train_loss=3.5233 | val_main=0.912624
Epoch 018 | phase=0 | train_loss=3.3560 | val_main=0.839868
Epoch 019 | phase=0 | train_loss=3.2215 | val_main=0.740966
Epoch 020 | phase=0 | train_loss=3.0533 | val_main=0.689957
Epoch 021 | phase=0 | train_loss=2.8201 | val_main=0.602356
Epoch 022 | phase=0 | train_loss=2.6882 | val_main=0.522273
Epoch 023 | phase=0 | train_loss=2.5845 | val_main=0.446492
Epoch 024 | phase=0 | train_loss=2.3863 | val_main=0.423190
Epoch 025 | phase=0 | train_loss=2.2812 | val_main=0.406252
Epoch 026 | phase=0 | train_loss=2.2473 | val_main=0.367131
Epoch 027 | phase=0 | train_loss=2.1591 | val_main=0.356897
Epoch 028 | phase=0 | train_loss=2.1164 | val_main=0.334568
Epoch 029 | phase=0 | train_loss=2.0340 | val_main=0.352075
Epoch 030 | phase=0 | train_loss=1.9688 | val_main=0.346131
Epoch 031 | phase=2 | train_loss=0.2291 | val_main=0.341468
Epoch 032 | phase=2 | train_loss=0.2260 | val_main=0.334809
Epoch 033 | phase=2 | train_loss=0.2171 | val_main=0.332946
Epoch 034 | phase=2 | train_loss=0.2158 | val_main=0.329222
Epoch 035 | phase=2 | train_loss=0.2088 | val_main=0.328985
Epoch 036 | phase=2 | train_loss=0.2024 | val_main=0.340944
Epoch 037 | phase=2 | train_loss=0.1993 | val_main=0.333794
Epoch 038 | phase=2 | train_loss=0.1951 | val_main=0.332970
Epoch 039 | phase=2 | train_loss=0.1910 | val_main=0.346763
Epoch 040 | phase=2 | train_loss=0.1870 | val_main=0.352996
Epoch 041 | phase=2 | train_loss=0.1838 | val_main=0.352100
Epoch 042 | phase=2 | train_loss=0.1801 | val_main=0.349358
Epoch 043 | phase=2 | train_loss=0.1766 | val_main=0.349907
Epoch 044 | phase=2 | train_loss=0.1762 | val_main=0.346840
Epoch 045 | phase=2 | train_loss=0.1734 | val_main=0.364801
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 60
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.08955657
1 day(s) RMSE : 0.17383639
1 day(s) R2 : 0.33793029
1 day(s) Pearson r : 0.58635538
1 day(s) QLIKE : 0.37756615
3 day(s) MAE : 0.09360218
3 day(s) RMSE : 0.18035779
3 day(s) R2 : 0.28182583
3 day(s) Pearson r : 0.54199454
3 day(s) QLIKE : 0.39069723
5 day(s) MAE : 0.09585852
5 day(s) RMSE : 0.18485370
5 day(s) R2 : 0.23700173
5 day(s) Pearson r : 0.51125463
5 day(s) QLIKE : 0.41188194
10 day(s) MAE : 0.09639125
10 day(s) RMSE : 0.18854423
10 day(s) R2 : 0.19865468
10 day(s) Pearson r : 0.47733436
10 day(s) QLIKE : 0.42608910
full horizon MAE : 0.09639125
full horizon RMSE : 0.18854423
full horizon R2 : 0.19865468
full horizon Pearson r : 0.47733436
full horizon QLIKE : 0.42608910
--- Task 2 ---
1 day(s) MAE : 0.03394429
1 day(s) RMSE : 0.05454115
1 day(s) R2 : -0.51010099
1 day(s) Pearson r : -0.01460582
1 day(s) QLIKE : 19.30600768
3 day(s) MAE : 0.03411893
3 day(s) RMSE : 0.05484849
3 day(s) R2 : -0.52678218
3 day(s) Pearson r : -0.02656502
3 day(s) QLIKE : 19.36618016
5 day(s) MAE : 0.03444678
5 day(s) RMSE : 0.05529804
5 day(s) R2 : -0.55086923
5 day(s) Pearson r : -0.02905630
5 day(s) QLIKE : 19.44860174
10 day(s) MAE : 0.03393860
10 day(s) RMSE : 0.05506994
10 day(s) R2 : -0.53600957
10 day(s) Pearson r : -0.01521919
10 day(s) QLIKE : 19.55704515
full horizon MAE : 0.03393860
full horizon RMSE : 0.05506994
full horizon R2 : -0.53600957
full horizon Pearson r : -0.01521919
full horizon QLIKE : 19.55704515
--- Task 3 ---
1 day(s) MAE : 0.59723010
1 day(s) RMSE : 0.74930427
1 day(s) R2 : -3.15668363
1 day(s) Pearson r : -0.14242218
1 day(s) QLIKE : 9.45261607
3 day(s) MAE : 0.58995913
3 day(s) RMSE : 0.73675230
3 day(s) R2 : -3.01816740
3 day(s) Pearson r : -0.13230451
3 day(s) QLIKE : 9.74444909
5 day(s) MAE : 0.58182841
5 day(s) RMSE : 0.72783623
5 day(s) R2 : -2.92060482
5 day(s) Pearson r : -0.12797682
5 day(s) QLIKE : 9.97871097
10 day(s) MAE : 0.56780829
10 day(s) RMSE : 0.71329830
10 day(s) R2 : -2.76360800
10 day(s) Pearson r : -0.07154204
10 day(s) QLIKE : 10.28755824
full horizon MAE : 0.56780829
full horizon RMSE : 0.71329830
full horizon R2 : -2.76360800
full horizon Pearson r : -0.07154204
full horizon QLIKE : 10.28755824
--- Task 4 ---
1 day(s) MAE : 0.03629914
1 day(s) RMSE : 0.06115116
1 day(s) R2 : -0.33848322
1 day(s) Pearson r : -0.18496252
1 day(s) QLIKE : 10.92590783
3 day(s) MAE : 0.03660736
3 day(s) RMSE : 0.06117379
3 day(s) R2 : -0.33927246
3 day(s) Pearson r : -0.17890431
3 day(s) QLIKE : 11.22085450
5 day(s) MAE : 0.03694254
5 day(s) RMSE : 0.06121161
5 day(s) R2 : -0.34056563
5 day(s) Pearson r : -0.16811281
5 day(s) QLIKE : 11.50538090
10 day(s) MAE : 0.03688577
10 day(s) RMSE : 0.06097283
10 day(s) R2 : -0.32929283
10 day(s) Pearson r : -0.15331383
10 day(s) QLIKE : 11.72243816
full horizon MAE : 0.03688577
full horizon RMSE : 0.06097283
full horizon R2 : -0.32929283
full horizon Pearson r : -0.15331383
full horizon QLIKE : 11.72243816
--- Task 5 ---
1 day(s) MAE : 0.00861942
1 day(s) RMSE : 0.01378903
1 day(s) R2 : -4.47951323
1 day(s) Pearson r : 0.20326192
1 day(s) QLIKE : 16.52114714
3 day(s) MAE : 0.00869276
3 day(s) RMSE : 0.01386257
3 day(s) R2 : -4.53811347
3 day(s) Pearson r : 0.18441732
3 day(s) QLIKE : 16.54608379
5 day(s) MAE : 0.00877773
5 day(s) RMSE : 0.01397833
5 day(s) R2 : -4.63099008
5 day(s) Pearson r : 0.17448125
5 day(s) QLIKE : 16.55070608
10 day(s) MAE : 0.00857087
10 day(s) RMSE : 0.01358219
10 day(s) R2 : -4.31635368
10 day(s) Pearson r : 0.15326227
10 day(s) QLIKE : 16.57281044
full horizon MAE : 0.00857087
full horizon RMSE : 0.01358219
full horizon R2 : -4.31635368
full horizon Pearson r : 0.15326227
full horizon QLIKE : 16.57281044
--- Task 6 ---
1 day(s) MAE : 1.15106844
1 day(s) RMSE : 1.61606181
1 day(s) R2 : -0.70918313
1 day(s) Pearson r : 0.26678696
1 day(s) QLIKE : 0.03345758
3 day(s) MAE : 1.14379775
3 day(s) RMSE : 1.60544506
3 day(s) R2 : -0.68620475
3 day(s) Pearson r : 0.26502661
3 day(s) QLIKE : 0.03325400
5 day(s) MAE : 1.13771921
5 day(s) RMSE : 1.60151687
5 day(s) R2 : -0.67707462
5 day(s) Pearson r : 0.26830258
5 day(s) QLIKE : 0.03301023
10 day(s) MAE : 1.12100765
10 day(s) RMSE : 1.60640157
10 day(s) R2 : -0.68373863
10 day(s) Pearson r : 0.28260034
10 day(s) QLIKE : 0.03234772
full horizon MAE : 1.12100765
full horizon RMSE : 1.60640157
full horizon R2 : -0.68373863
full horizon Pearson r : 0.28260034
full horizon QLIKE : 0.03234772
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/EURUSD/Custom_KAN_H10.pkl
Saved y_true min=9.56189e-05, max=2.66457
Saved y_pred min=0.00297112, max=1.32878
=== EURUSD | H=10 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 3782
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2723, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 19.199765452525025
Min value: -0.7148707238463011
Checking X_time_train_core:
Shape: (2723, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.7683468970942753
Checking y_train_core (log_mse scaled):
Shape: (2723, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.7476503800701995
Min value: -3.719996871136402
Checking X_price_val:
Shape: (302, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 7.408921165171427
Min value: -0.7103978753601863
Checking X_time_val:
Shape: (302, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_val (log_mse scaled):
Shape: (302, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3197553444271937
Min value: -2.9770657245458123
Checking X_price_test:
Shape: (757, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.131631151144182
Min value: -0.7173043382357188
Checking X_time_test:
Shape: (757, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_test (log_mse scaled):
Shape: (757, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3841503070956698
Min value: -5.351929442359245
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 174s 395ms/step - loss: 0.6320 - val_loss: 1.2921 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 28s 322ms/step - loss: 0.4132 - val_loss: 1.4350 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 28s 325ms/step - loss: 0.3891 - val_loss: 1.3263 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 28s 321ms/step - loss: 0.3775 - val_loss: 1.0932 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 28s 327ms/step - loss: 0.3682 - val_loss: 1.2110 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 28s 326ms/step - loss: 0.3616 - val_loss: 1.0926 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 28s 324ms/step - loss: 0.3587 - val_loss: 0.9953 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 30s 345ms/step - loss: 0.3543 - val_loss: 1.0238 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 34s 395ms/step - loss: 0.3464 - val_loss: 1.0783 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 32s 370ms/step - loss: 0.3421 - val_loss: 1.0344 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 30s 346ms/step - loss: 0.3378 - val_loss: 1.0170 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=12 tf_ratio=0.421 -> TF=ON
Epoch 12/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 0s 370ms/step - loss: 0.3362
Epoch 12: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
86/86 ━━━━━━━━━━━━━━━━━━━━ 33s 385ms/step - loss: 0.3346 - val_loss: 1.1068 - learning_rate: 5.0000e-04
Epoch 12: early stopping
Restoring model weights from the end of the best epoch: 7.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 10
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.11025227
1 day(s) RMSE : 0.20677671
1 day(s) R2 : 0.06324592
1 day(s) Pearson r : 0.41293901
1 day(s) QLIKE : 0.48962977
3 day(s) MAE : 0.11721950
3 day(s) RMSE : 0.21552583
3 day(s) R2 : -0.02555420
3 day(s) Pearson r : 0.34708090
3 day(s) QLIKE : 0.51972154
5 day(s) MAE : 0.11756639
5 day(s) RMSE : 0.21659161
5 day(s) R2 : -0.04749152
5 day(s) Pearson r : 0.33023322
5 day(s) QLIKE : 0.51862278
10 day(s) MAE : 0.11649309
10 day(s) RMSE : 0.21569807
10 day(s) R2 : -0.04878320
10 day(s) Pearson r : 0.30813985
10 day(s) QLIKE : 0.51944667
full horizon MAE : 0.11649309
full horizon RMSE : 0.21569807
full horizon R2 : -0.04878320
full horizon Pearson r : 0.30813985
full horizon QLIKE : 0.51944667
--- Task 2 ---
1 day(s) MAE : 0.03449850
1 day(s) RMSE : 0.05606925
1 day(s) R2 : -0.59590433
1 day(s) Pearson r : -0.07019128
1 day(s) QLIKE : 13.17873974
3 day(s) MAE : 0.03453417
3 day(s) RMSE : 0.05611866
3 day(s) R2 : -0.59831461
3 day(s) Pearson r : -0.09682873
3 day(s) QLIKE : 15.72863829
5 day(s) MAE : 0.03449638
5 day(s) RMSE : 0.05615588
5 day(s) R2 : -0.59935974
5 day(s) Pearson r : -0.07249472
5 day(s) QLIKE : 14.41360917
10 day(s) MAE : 0.03441843
10 day(s) RMSE : 0.05616861
10 day(s) R2 : -0.59790943
10 day(s) Pearson r : -0.04962249
10 day(s) QLIKE : 12.74831435
full horizon MAE : 0.03441843
full horizon RMSE : 0.05616861
full horizon R2 : -0.59790943
full horizon Pearson r : -0.04962249
full horizon QLIKE : 12.74831435
--- Task 3 ---
1 day(s) MAE : 0.70544837
1 day(s) RMSE : 0.78466746
1 day(s) R2 : -3.55828888
1 day(s) Pearson r : 0.19093147
1 day(s) QLIKE : 3.49976836
3 day(s) MAE : 0.74327997
3 day(s) RMSE : 0.82591043
3 day(s) R2 : -4.04952966
3 day(s) Pearson r : 0.09534002
3 day(s) QLIKE : 7.55411821
5 day(s) MAE : 0.75226022
5 day(s) RMSE : 0.83539211
5 day(s) R2 : -4.16495408
5 day(s) Pearson r : 0.06977842
5 day(s) QLIKE : 7.02763711
10 day(s) MAE : 0.75911236
10 day(s) RMSE : 0.84256724
10 day(s) R2 : -4.25135214
10 day(s) Pearson r : 0.04736674
10 day(s) QLIKE : 5.41821106
full horizon MAE : 0.75911236
full horizon RMSE : 0.84256724
full horizon R2 : -4.25135214
full horizon Pearson r : 0.04736674
full horizon QLIKE : 5.41821106
--- Task 4 ---
1 day(s) MAE : 0.03257742
1 day(s) RMSE : 0.06169103
1 day(s) R2 : -0.36222126
1 day(s) Pearson r : -0.15440293
1 day(s) QLIKE : 4.50611378
3 day(s) MAE : 0.03234016
3 day(s) RMSE : 0.06115330
3 day(s) R2 : -0.33837581
3 day(s) Pearson r : -0.12468555
3 day(s) QLIKE : 8.08213454
5 day(s) MAE : 0.03297594
5 day(s) RMSE : 0.06057024
5 day(s) R2 : -0.31261983
5 day(s) Pearson r : -0.10899419
5 day(s) QLIKE : 11.05301469
10 day(s) MAE : 0.03508875
10 day(s) RMSE : 0.06006666
10 day(s) R2 : -0.29007477
10 day(s) Pearson r : -0.12380094
10 day(s) QLIKE : 11.52329300
full horizon MAE : 0.03508875
full horizon RMSE : 0.06006666
full horizon R2 : -0.29007477
full horizon Pearson r : -0.12380094
full horizon QLIKE : 11.52329300
--- Task 5 ---
1 day(s) MAE : 0.00512736
1 day(s) RMSE : 0.00780933
1 day(s) R2 : -0.75752512
1 day(s) Pearson r : 0.41337337
1 day(s) QLIKE : 12.75232346
3 day(s) MAE : 0.00512755
3 day(s) RMSE : 0.00780962
3 day(s) R2 : -0.75765564
3 day(s) Pearson r : 0.19518972
3 day(s) QLIKE : 12.68379260
5 day(s) MAE : 0.00512759
5 day(s) RMSE : 0.00780968
5 day(s) R2 : -0.75768297
5 day(s) Pearson r : 0.14538551
5 day(s) QLIKE : 11.29248877
10 day(s) MAE : 0.00512762
10 day(s) RMSE : 0.00780972
10 day(s) R2 : -0.75770347
10 day(s) Pearson r : 0.10001538
10 day(s) QLIKE : 10.24160523
full horizon MAE : 0.00512762
full horizon RMSE : 0.00780972
full horizon R2 : -0.75770347
full horizon Pearson r : 0.10001538
full horizon QLIKE : 10.24160523
--- Task 6 ---
1 day(s) MAE : 1.35201625
1 day(s) RMSE : 1.64100959
1 day(s) R2 : -0.76236110
1 day(s) Pearson r : -0.23349714
1 day(s) QLIKE : 0.04277081
3 day(s) MAE : 1.37090468
3 day(s) RMSE : 1.66662597
3 day(s) R2 : -0.81717061
3 day(s) Pearson r : -0.25213048
3 day(s) QLIKE : 0.04141192
5 day(s) MAE : 1.36601672
5 day(s) RMSE : 1.66207018
5 day(s) R2 : -0.80629243
5 day(s) Pearson r : -0.25393858
5 day(s) QLIKE : 0.03977294
10 day(s) MAE : 1.39220386
10 day(s) RMSE : 1.68251969
10 day(s) R2 : -0.84708442
10 day(s) Pearson r : -0.28364294
10 day(s) QLIKE : 0.03791710
full horizon MAE : 1.39220386
full horizon RMSE : 1.68251969
full horizon R2 : -0.84708442
full horizon Pearson r : -0.28364294
full horizon QLIKE : 0.03791710
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/EURUSD/Custom_KAN_LSTM_H10.pkl
Saved y_true min=9.56189e-05, max=2.66457
Saved y_pred min=0.0447775, max=0.226415
=== EURUSD | H=20 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 3782
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2723, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 19.199765452525025
Min value: -0.7148707238463011
Checking X_time_train_core:
Shape: (2723, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.7683468970942753
Checking y_train_core (log_mse scaled):
Shape: (2723, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.7474473273136155
Min value: -3.7200073694899167
Checking X_price_val:
Shape: (302, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 7.408921165171427
Min value: -0.7103978753601863
Checking X_time_val:
Shape: (302, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_val (log_mse scaled):
Shape: (302, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.321220211077452
Min value: -2.97689474283611
Checking X_price_test:
Shape: (757, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.131631151144182
Min value: -0.7173043382357188
Checking X_time_test:
Shape: (757, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_test (log_mse scaled):
Shape: (757, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3856309038737455
Min value: -5.35233858226595
Epoch 001 | phase=1 | train_loss=4.9931 | val_main=1.228354
Epoch 002 | phase=1 | train_loss=4.6899 | val_main=1.229844
Epoch 003 | phase=1 | train_loss=4.5086 | val_main=1.230018
Epoch 004 | phase=1 | train_loss=4.3678 | val_main=1.229933
Epoch 005 | phase=1 | train_loss=4.2063 | val_main=1.230155
Epoch 006 | phase=1 | train_loss=4.0066 | val_main=1.230126
Epoch 007 | phase=1 | train_loss=3.8909 | val_main=1.230043
Epoch 008 | phase=1 | train_loss=3.8188 | val_main=1.229935
Epoch 009 | phase=1 | train_loss=3.6176 | val_main=1.230166
Epoch 010 | phase=1 | train_loss=3.4724 | val_main=1.230071
Epoch 011 | phase=1 | train_loss=3.3855 | val_main=1.230101
Epoch 012 | phase=1 | train_loss=3.2812 | val_main=1.230185
Epoch 013 | phase=1 | train_loss=3.0935 | val_main=1.230290
Epoch 014 | phase=1 | train_loss=3.0006 | val_main=1.230316
Epoch 015 | phase=1 | train_loss=2.9403 | val_main=1.230466
Epoch 016 | phase=0 | train_loss=3.7907 | val_main=1.057823
Epoch 017 | phase=0 | train_loss=3.6006 | val_main=0.956255
Epoch 018 | phase=0 | train_loss=3.4884 | val_main=0.911961
Epoch 019 | phase=0 | train_loss=3.3676 | val_main=0.885423
Epoch 020 | phase=0 | train_loss=3.2470 | val_main=0.780974
Epoch 021 | phase=0 | train_loss=3.0758 | val_main=0.702361
Epoch 022 | phase=0 | train_loss=2.9281 | val_main=0.630526
Epoch 023 | phase=0 | train_loss=2.7464 | val_main=0.537428
Epoch 024 | phase=0 | train_loss=2.6079 | val_main=0.497743
Epoch 025 | phase=0 | train_loss=2.4630 | val_main=0.443168
Epoch 026 | phase=0 | train_loss=2.3670 | val_main=0.395190
Epoch 027 | phase=0 | train_loss=2.2428 | val_main=0.382617
Epoch 028 | phase=0 | train_loss=2.1780 | val_main=0.364068
Epoch 029 | phase=0 | train_loss=2.1193 | val_main=0.364457
Epoch 030 | phase=0 | train_loss=2.0573 | val_main=0.354162
Epoch 031 | phase=2 | train_loss=0.2527 | val_main=0.352728
Epoch 032 | phase=2 | train_loss=0.2467 | val_main=0.354946
Epoch 033 | phase=2 | train_loss=0.2404 | val_main=0.362079
Epoch 034 | phase=2 | train_loss=0.2416 | val_main=0.349136
Epoch 035 | phase=2 | train_loss=0.2310 | val_main=0.353860
Epoch 036 | phase=2 | train_loss=0.2262 | val_main=0.362826
Epoch 037 | phase=2 | train_loss=0.2233 | val_main=0.363486
Epoch 038 | phase=2 | train_loss=0.2187 | val_main=0.358472
Epoch 039 | phase=2 | train_loss=0.2169 | val_main=0.371263
Epoch 040 | phase=2 | train_loss=0.2124 | val_main=0.375643
Epoch 041 | phase=2 | train_loss=0.2090 | val_main=0.388353
Epoch 042 | phase=2 | train_loss=0.2075 | val_main=0.379832
Epoch 043 | phase=2 | train_loss=0.2035 | val_main=0.387429
Epoch 044 | phase=2 | train_loss=0.2036 | val_main=0.374385
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 120
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.09009713
1 day(s) RMSE : 0.17642242
1 day(s) R2 : 0.31808558
1 day(s) Pearson r : 0.57007083
1 day(s) QLIKE : 0.38172591
3 day(s) MAE : 0.09477819
3 day(s) RMSE : 0.18557990
3 day(s) R2 : 0.23963547
3 day(s) Pearson r : 0.50872398
3 day(s) QLIKE : 0.41914086
5 day(s) MAE : 0.09593604
5 day(s) RMSE : 0.18749946
5 day(s) R2 : 0.21500427
5 day(s) Pearson r : 0.48838586
5 day(s) QLIKE : 0.42797432
10 day(s) MAE : 0.09721305
10 day(s) RMSE : 0.19034777
10 day(s) R2 : 0.18325066
10 day(s) Pearson r : 0.46195015
10 day(s) QLIKE : 0.43972254
20 day(s) MAE : 0.09967460
20 day(s) RMSE : 0.19377007
20 day(s) R2 : 0.14363532
20 day(s) Pearson r : 0.42455563
20 day(s) QLIKE : 0.47449161
full horizon MAE : 0.09967460
full horizon RMSE : 0.19377007
full horizon R2 : 0.14363532
full horizon Pearson r : 0.42455563
full horizon QLIKE : 0.47449161
--- Task 2 ---
1 day(s) MAE : 0.03838463
1 day(s) RMSE : 0.06354503
1 day(s) R2 : -1.04984298
1 day(s) Pearson r : -0.01343853
1 day(s) QLIKE : 18.72059700
3 day(s) MAE : 0.03827613
3 day(s) RMSE : 0.06337762
3 day(s) R2 : -1.03854163
3 day(s) Pearson r : -0.00111704
3 day(s) QLIKE : 18.76380126
5 day(s) MAE : 0.03803094
5 day(s) RMSE : 0.06194521
5 day(s) R2 : -0.94612677
5 day(s) Pearson r : -0.00379936
5 day(s) QLIKE : 18.95615537
10 day(s) MAE : 0.03738371
10 day(s) RMSE : 0.05976137
10 day(s) R2 : -0.80886365
10 day(s) Pearson r : -0.03235211
10 day(s) QLIKE : 19.20117834
20 day(s) MAE : 0.03708690
20 day(s) RMSE : 0.05836105
20 day(s) R2 : -0.72391235
20 day(s) Pearson r : -0.04655799
20 day(s) QLIKE : 19.41749632
full horizon MAE : 0.03708690
full horizon RMSE : 0.05836105
full horizon R2 : -0.72391235
full horizon Pearson r : -0.04655799
full horizon QLIKE : 19.41749632
--- Task 3 ---
1 day(s) MAE : 0.60460476
1 day(s) RMSE : 0.73249674
1 day(s) R2 : -2.97229915
1 day(s) Pearson r : -0.10161839
1 day(s) QLIKE : 7.21530196
3 day(s) MAE : 0.60106441
3 day(s) RMSE : 0.72755429
3 day(s) R2 : -2.91846380
3 day(s) Pearson r : -0.10015558
3 day(s) QLIKE : 7.33966660
5 day(s) MAE : 0.59751681
5 day(s) RMSE : 0.72240256
5 day(s) R2 : -2.86228471
5 day(s) Pearson r : -0.09401293
5 day(s) QLIKE : 7.47335177
10 day(s) MAE : 0.59498650
10 day(s) RMSE : 0.72714359
10 day(s) R2 : -2.91113104
10 day(s) Pearson r : -0.08383971
10 day(s) QLIKE : 7.88469071
20 day(s) MAE : 0.57291118
20 day(s) RMSE : 0.70541741
20 day(s) R2 : -2.67942495
20 day(s) Pearson r : -0.02135542
20 day(s) QLIKE : 8.05336010
full horizon MAE : 0.57291118
full horizon RMSE : 0.70541741
full horizon R2 : -2.67942495
full horizon Pearson r : -0.02135542
full horizon QLIKE : 8.05336010
--- Task 4 ---
1 day(s) MAE : 0.03502900
1 day(s) RMSE : 0.05994632
1 day(s) R2 : -0.28625949
1 day(s) Pearson r : -0.10330504
1 day(s) QLIKE : 12.29250276
3 day(s) MAE : 0.03514593
3 day(s) RMSE : 0.06004382
3 day(s) R2 : -0.29025275
3 day(s) Pearson r : -0.10922068
3 day(s) QLIKE : 12.55805237
5 day(s) MAE : 0.03536073
5 day(s) RMSE : 0.06013261
5 day(s) R2 : -0.29372066
5 day(s) Pearson r : -0.11732994
5 day(s) QLIKE : 12.88158277
10 day(s) MAE : 0.03573982
10 day(s) RMSE : 0.06031226
10 day(s) R2 : -0.30064617
10 day(s) Pearson r : -0.12474296
10 day(s) QLIKE : 13.34603132
20 day(s) MAE : 0.03630392
20 day(s) RMSE : 0.06042370
20 day(s) R2 : -0.30465091
20 day(s) Pearson r : -0.11197950
20 day(s) QLIKE : 13.28275125
full horizon MAE : 0.03630392
full horizon RMSE : 0.06042370
full horizon R2 : -0.30465091
full horizon Pearson r : -0.11197950
full horizon QLIKE : 13.28275125
--- Task 5 ---
1 day(s) MAE : 0.00671275
1 day(s) RMSE : 0.01084310
1 day(s) R2 : -2.38829357
1 day(s) Pearson r : 0.20225983
1 day(s) QLIKE : 16.60175330
3 day(s) MAE : 0.00703007
3 day(s) RMSE : 0.01152159
3 day(s) R2 : -2.82559855
3 day(s) Pearson r : 0.18999992
3 day(s) QLIKE : 16.65648222
5 day(s) MAE : 0.00723832
5 day(s) RMSE : 0.01176297
5 day(s) R2 : -2.98757260
5 day(s) Pearson r : 0.18685414
5 day(s) QLIKE : 16.70222855
10 day(s) MAE : 0.00720208
10 day(s) RMSE : 0.01150401
10 day(s) R2 : -2.81393416
10 day(s) Pearson r : 0.17248243
10 day(s) QLIKE : 16.74344322
20 day(s) MAE : 0.00716413
20 day(s) RMSE : 0.01136058
20 day(s) R2 : -2.72382232
20 day(s) Pearson r : 0.13274517
20 day(s) QLIKE : 16.62703415
full horizon MAE : 0.00716413
full horizon RMSE : 0.01136058
full horizon R2 : -2.72382232
full horizon Pearson r : 0.13274517
full horizon QLIKE : 16.62703415
--- Task 6 ---
1 day(s) MAE : 1.23455848
1 day(s) RMSE : 2.19251113
1 day(s) R2 : -2.14598229
1 day(s) Pearson r : 0.19084434
1 day(s) QLIKE : 0.03734005
3 day(s) MAE : 1.23855078
3 day(s) RMSE : 2.20149234
3 day(s) R2 : -2.17068991
3 day(s) Pearson r : 0.18349393
3 day(s) QLIKE : 0.03796776
5 day(s) MAE : 1.22943624
5 day(s) RMSE : 2.18842316
5 day(s) R2 : -2.13149667
5 day(s) Pearson r : 0.19012883
5 day(s) QLIKE : 0.03746893
10 day(s) MAE : 1.22112602
10 day(s) RMSE : 2.19532118
10 day(s) R2 : -2.14457943
10 day(s) Pearson r : 0.22438347
10 day(s) QLIKE : 0.03681021
20 day(s) MAE : 1.20508909
20 day(s) RMSE : 2.27450790
20 day(s) R2 : -2.37516445
20 day(s) Pearson r : 0.30071316
20 day(s) QLIKE : 0.03498644
full horizon MAE : 1.20508909
full horizon RMSE : 2.27450790
full horizon R2 : -2.37516445
full horizon Pearson r : 0.30071316
full horizon QLIKE : 0.03498644
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/EURUSD/Custom_KAN_H20.pkl
Saved y_true min=9.56189e-05, max=2.66457
Saved y_pred min=0.00325541, max=1.00685
=== EURUSD | H=20 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 3782
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2723, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 19.199765452525025
Min value: -0.7148707238463011
Checking X_time_train_core:
Shape: (2723, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.7683468970942753
Checking y_train_core (log_mse scaled):
Shape: (2723, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.7474473273136155
Min value: -3.7200073694899167
Checking X_price_val:
Shape: (302, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 7.408921165171427
Min value: -0.7103978753601863
Checking X_time_val:
Shape: (302, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_val (log_mse scaled):
Shape: (302, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.321220211077452
Min value: -2.97689474283611
Checking X_price_test:
Shape: (757, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 8.131631151144182
Min value: -0.7173043382357188
Checking X_time_test:
Shape: (757, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.474527252926688
Min value: -1.4974014475059652
Checking y_test (log_mse scaled):
Shape: (757, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.3856309038737455
Min value: -5.35233858226595
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 175s 470ms/step - loss: 0.6134 - val_loss: 1.8066 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 41s 471ms/step - loss: 0.3827 - val_loss: 2.0618 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 44s 516ms/step - loss: 0.3584 - val_loss: 2.3161 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 49s 567ms/step - loss: 0.3443 - val_loss: 2.1891 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 43s 496ms/step - loss: 0.3323 - val_loss: 1.9408 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
86/86 ━━━━━━━━━━━━━━━━━━━━ 0s 462ms/step - loss: 0.3247
Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
86/86 ━━━━━━━━━━━━━━━━━━━━ 41s 477ms/step - loss: 0.3184 - val_loss: 2.0848 - learning_rate: 5.0000e-04
Epoch 6: early stopping
Restoring model weights from the end of the best epoch: 1.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 20
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.12621655
1 day(s) RMSE : 0.22828548
1 day(s) R2 : -0.14177078
1 day(s) Pearson r : -0.35684470
1 day(s) QLIKE : 0.58543800
3 day(s) MAE : 0.12528325
3 day(s) RMSE : 0.22673438
3 day(s) R2 : -0.13499704
3 day(s) Pearson r : 0.00180501
3 day(s) QLIKE : 0.58395910
5 day(s) MAE : 0.12892220
5 day(s) RMSE : 0.23054307
5 day(s) R2 : -0.18678324
5 day(s) Pearson r : 0.00912402
5 day(s) QLIKE : 0.59614609
10 day(s) MAE : 0.13639083
10 day(s) RMSE : 0.23796285
10 day(s) R2 : -0.27647269
10 day(s) Pearson r : 0.00560534
10 day(s) QLIKE : 0.61038971
20 day(s) MAE : 0.14013057
20 day(s) RMSE : 0.24118429
20 day(s) R2 : -0.32673251
20 day(s) Pearson r : 0.00533730
20 day(s) QLIKE : 0.59609619
full horizon MAE : 0.14013057
full horizon RMSE : 0.24118429
full horizon R2 : -0.32673251
full horizon Pearson r : 0.00533730
full horizon QLIKE : 0.59609619
--- Task 2 ---
1 day(s) MAE : 0.03453400
1 day(s) RMSE : 0.05623568
1 day(s) R2 : -0.60539263
1 day(s) Pearson r : 0.04542675
1 day(s) QLIKE : 9.83683956
3 day(s) MAE : 0.03452233
3 day(s) RMSE : 0.05623314
3 day(s) R2 : -0.60484227
3 day(s) Pearson r : 0.00169777
3 day(s) QLIKE : 11.34376983
5 day(s) MAE : 0.03448912
5 day(s) RMSE : 0.05622460
5 day(s) R2 : -0.60327662
5 day(s) Pearson r : 0.00159883
5 day(s) QLIKE : 13.60663065
10 day(s) MAE : 0.03441481
10 day(s) RMSE : 0.05620298
10 day(s) R2 : -0.59986521
10 day(s) Pearson r : 0.00171224
10 day(s) QLIKE : 12.18016071
20 day(s) MAE : 0.03437521
20 day(s) RMSE : 0.05619073
20 day(s) R2 : -0.59807948
20 day(s) Pearson r : 0.00140936
20 day(s) QLIKE : 11.31028098
full horizon MAE : 0.03437521
full horizon RMSE : 0.05619073
full horizon R2 : -0.59807948
full horizon Pearson r : 0.00140936
full horizon QLIKE : 11.31028098
--- Task 3 ---
1 day(s) MAE : 0.74835805
1 day(s) RMSE : 0.82834649
1 day(s) R2 : -4.07989375
1 day(s) Pearson r : 0.15503720
1 day(s) QLIKE : 3.33582382
3 day(s) MAE : 0.75943980
3 day(s) RMSE : 0.84184971
3 day(s) R2 : -4.24631253
3 day(s) Pearson r : 0.00191491
3 day(s) QLIKE : 6.04949685
5 day(s) MAE : 0.76195607
5 day(s) RMSE : 0.84488436
5 day(s) R2 : -4.28299586
5 day(s) Pearson r : 0.00121785
5 day(s) QLIKE : 7.04444171
10 day(s) MAE : 0.76396031
10 day(s) RMSE : 0.84728648
10 day(s) R2 : -4.31034288
10 day(s) Pearson r : 0.00063474
10 day(s) QLIKE : 6.39352012
20 day(s) MAE : 0.76500637
20 day(s) RMSE : 0.84853616
20 day(s) R2 : -4.32388057
20 day(s) Pearson r : 0.00034776
20 day(s) QLIKE : 5.22453997
full horizon MAE : 0.76500637
full horizon RMSE : 0.84853616
full horizon R2 : -4.32388057
full horizon Pearson r : 0.00034776
full horizon QLIKE : 5.22453997
--- Task 4 ---
1 day(s) MAE : 0.03287545
1 day(s) RMSE : 0.06210912
1 day(s) R2 : -0.38074757
1 day(s) Pearson r : -0.10938982
1 day(s) QLIKE : 2.92223937
3 day(s) MAE : 0.03303926
3 day(s) RMSE : 0.06226541
3 day(s) R2 : -0.38749646
3 day(s) Pearson r : -0.00585664
3 day(s) QLIKE : 3.03322321
5 day(s) MAE : 0.03316315
5 day(s) RMSE : 0.06236573
5 day(s) R2 : -0.39159352
5 day(s) Pearson r : -0.00274955
5 day(s) QLIKE : 5.78207657
10 day(s) MAE : 0.03323645
10 day(s) RMSE : 0.06244003
10 day(s) R2 : -0.39403637
10 day(s) Pearson r : -0.00121593
10 day(s) QLIKE : 6.71191457
20 day(s) MAE : 0.03325915
20 day(s) RMSE : 0.06247653
20 day(s) R2 : -0.39480471
20 day(s) Pearson r : -0.00057527
20 day(s) QLIKE : 5.24936592
full horizon MAE : 0.03325915
full horizon RMSE : 0.06247653
full horizon R2 : -0.39480471
full horizon Pearson r : -0.00057527
full horizon QLIKE : 5.24936592
--- Task 5 ---
1 day(s) MAE : 0.00512764
1 day(s) RMSE : 0.00780973
1 day(s) R2 : -0.75770525
1 day(s) Pearson r : 0.29094500
1 day(s) QLIKE : 8.96221489
3 day(s) MAE : 0.00512618
3 day(s) RMSE : 0.00780467
3 day(s) R2 : -0.75542818
3 day(s) Pearson r : 0.00326506
3 day(s) QLIKE : 10.92019593
5 day(s) MAE : 0.00510231
5 day(s) RMSE : 0.00771895
5 day(s) R2 : -0.71707943
5 day(s) Pearson r : 0.00067700
5 day(s) QLIKE : 12.56349337
10 day(s) MAE : 0.00498674
10 day(s) RMSE : 0.00705669
10 day(s) R2 : -0.43508172
10 day(s) Pearson r : 0.00033249
10 day(s) QLIKE : 14.66265984
20 day(s) MAE : 0.00525605
20 day(s) RMSE : 0.00674359
20 day(s) R2 : -0.31210955
20 day(s) Pearson r : 0.00165549
20 day(s) QLIKE : 15.79372853
full horizon MAE : 0.00525605
full horizon RMSE : 0.00674359
full horizon R2 : -0.31210955
full horizon Pearson r : 0.00165549
full horizon QLIKE : 15.79372853
--- Task 6 ---
1 day(s) MAE : 1.29332628
1 day(s) RMSE : 1.54448298
1 day(s) R2 : -0.56112944
1 day(s) Pearson r : 0.06653848
1 day(s) QLIKE : 0.02770916
3 day(s) MAE : 1.57746247
3 day(s) RMSE : 1.83645797
3 day(s) R2 : -1.20638551
3 day(s) Pearson r : 0.00553590
3 day(s) QLIKE : 0.02907965
5 day(s) MAE : 1.80778598
5 day(s) RMSE : 2.07595632
5 day(s) R2 : -1.81790126
5 day(s) Pearson r : 0.01127241
5 day(s) QLIKE : 0.03008544
10 day(s) MAE : 1.87738158
10 day(s) RMSE : 2.13972317
10 day(s) R2 : -1.98731906
10 day(s) Pearson r : 0.04485066
10 day(s) QLIKE : 0.02949967
20 day(s) MAE : 2.03368681
20 day(s) RMSE : 2.31306671
20 day(s) R2 : -2.49057002
20 day(s) Pearson r : 0.07847771
20 day(s) QLIKE : 0.03154199
full horizon MAE : 2.03368681
full horizon RMSE : 2.31306671
full horizon R2 : -2.49057002
full horizon Pearson r : 0.07847771
full horizon QLIKE : 0.03154199
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/EURUSD/Custom_KAN_LSTM_H20.pkl
Saved y_true min=9.56189e-05, max=2.66457
Saved y_pred min=0.0606639, max=0.117904
=== GOLD | H=1 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 5534
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (3984, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 17.63876776611196
Min value: -0.5790835075307484
Checking X_time_train_core:
Shape: (3984, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.678060596158252
Checking y_train_core (log_mse scaled):
Shape: (3984, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.253814902458322
Min value: -5.250298730157774
Checking X_price_val:
Shape: (443, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.324782722398346
Min value: -0.5755730380699846
Checking X_time_val:
Shape: (443, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_val (log_mse scaled):
Shape: (443, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.7902797110229687
Min value: -5.155379024632684
Checking X_price_test:
Shape: (1107, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.413509896698947
Min value: -0.5768892216495461
Checking X_time_test:
Shape: (1107, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_test (log_mse scaled):
Shape: (1107, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.5405778347636003
Min value: -5.155379024632684
Epoch 001 | phase=1 | train_loss=4.8095 | val_main=0.486445
Epoch 002 | phase=1 | train_loss=4.4237 | val_main=0.486358
Epoch 003 | phase=1 | train_loss=4.2331 | val_main=0.486345
Epoch 004 | phase=1 | train_loss=4.1420 | val_main=0.486436
Epoch 005 | phase=1 | train_loss=4.0132 | val_main=0.486476
Epoch 006 | phase=1 | train_loss=3.9100 | val_main=0.486434
Epoch 007 | phase=1 | train_loss=3.8297 | val_main=0.486468
Epoch 008 | phase=1 | train_loss=3.8065 | val_main=0.486537
Epoch 009 | phase=1 | train_loss=3.7791 | val_main=0.486505
Epoch 010 | phase=1 | train_loss=3.6620 | val_main=0.486419
Epoch 011 | phase=1 | train_loss=3.5839 | val_main=0.486099
Epoch 012 | phase=1 | train_loss=3.5572 | val_main=0.486245
Epoch 013 | phase=1 | train_loss=3.4315 | val_main=0.486175
Epoch 014 | phase=1 | train_loss=3.3788 | val_main=0.486212
Epoch 015 | phase=1 | train_loss=3.2905 | val_main=0.486131
Epoch 016 | phase=0 | train_loss=4.1266 | val_main=0.455466
Epoch 017 | phase=0 | train_loss=3.8001 | val_main=0.398078
Epoch 018 | phase=0 | train_loss=3.4527 | val_main=0.321515
Epoch 019 | phase=0 | train_loss=3.3010 | val_main=0.312507
Epoch 020 | phase=0 | train_loss=3.1889 | val_main=0.290006
Epoch 021 | phase=0 | train_loss=3.0173 | val_main=0.286137
Epoch 022 | phase=0 | train_loss=2.8938 | val_main=0.274189
Epoch 023 | phase=0 | train_loss=2.8323 | val_main=0.267696
Epoch 024 | phase=0 | train_loss=2.7501 | val_main=0.259894
Epoch 025 | phase=0 | train_loss=2.7323 | val_main=0.249145
Epoch 026 | phase=0 | train_loss=2.5545 | val_main=0.263837
Epoch 027 | phase=0 | train_loss=2.5070 | val_main=0.251098
Epoch 028 | phase=0 | train_loss=2.3893 | val_main=0.253022
Epoch 029 | phase=0 | train_loss=2.3626 | val_main=0.254703
Epoch 030 | phase=0 | train_loss=2.3573 | val_main=0.284536
Epoch 031 | phase=2 | train_loss=0.1552 | val_main=0.274398
Epoch 032 | phase=2 | train_loss=0.1314 | val_main=0.279163
Epoch 033 | phase=2 | train_loss=0.1260 | val_main=0.273155
Epoch 034 | phase=2 | train_loss=0.1120 | val_main=0.279139
Epoch 035 | phase=2 | train_loss=0.1098 | val_main=0.292047
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 6
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.30937749
1 day(s) RMSE : 0.59588361
1 day(s) R2 : 0.17315826
1 day(s) Pearson r : 0.44755197
1 day(s) QLIKE : 0.51382151
full horizon MAE : 0.30937749
full horizon RMSE : 0.59588361
full horizon R2 : 0.17315826
full horizon Pearson r : 0.44755197
full horizon QLIKE : 0.51382151
--- Task 2 ---
1 day(s) MAE : 0.03767039
1 day(s) RMSE : 0.05816697
1 day(s) R2 : -0.60826871
1 day(s) Pearson r : 0.21189069
1 day(s) QLIKE : 15.20939514
full horizon MAE : 0.03767039
full horizon RMSE : 0.05816697
full horizon R2 : -0.60826871
full horizon Pearson r : 0.21189069
full horizon QLIKE : 15.20939514
--- Task 3 ---
1 day(s) MAE : 0.31569860
1 day(s) RMSE : 0.47907707
1 day(s) R2 : -1.19345699
1 day(s) Pearson r : -0.23258487
1 day(s) QLIKE : 10.38697984
full horizon MAE : 0.31569860
full horizon RMSE : 0.47907707
full horizon R2 : -1.19345699
full horizon Pearson r : -0.23258487
full horizon QLIKE : 10.38697984
--- Task 4 ---
1 day(s) MAE : 0.12188845
1 day(s) RMSE : 0.25595767
1 day(s) R2 : -0.27888842
1 day(s) Pearson r : -0.16293585
1 day(s) QLIKE : 10.30833077
full horizon MAE : 0.12188845
full horizon RMSE : 0.25595767
full horizon R2 : -0.27888842
full horizon Pearson r : -0.16293585
full horizon QLIKE : 10.30833077
--- Task 5 ---
1 day(s) MAE : 0.04075025
1 day(s) RMSE : 0.05984030
1 day(s) R2 : -0.78453498
1 day(s) Pearson r : -0.01518305
1 day(s) QLIKE : 14.64244544
full horizon MAE : 0.04075025
full horizon RMSE : 0.05984030
full horizon R2 : -0.78453498
full horizon Pearson r : -0.01518305
full horizon QLIKE : 14.64244544
--- Task 6 ---
1 day(s) MAE : 0.97158885
1 day(s) RMSE : 1.92563735
1 day(s) R2 : -10.59983834
1 day(s) Pearson r : -0.06069739
1 day(s) QLIKE : 0.04418244
full horizon MAE : 0.97158885
full horizon RMSE : 1.92563735
full horizon R2 : -10.59983834
full horizon Pearson r : -0.06069739
full horizon QLIKE : 0.04418244
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GOLD/Custom_KAN_H1.pkl
Saved y_true min=0.00380168, max=7.43162
Saved y_pred min=0.00456254, max=2.08821
=== GOLD | H=1 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 5534
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (3984, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 17.63876776611196
Min value: -0.5790835075307484
Checking X_time_train_core:
Shape: (3984, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.678060596158252
Checking y_train_core (log_mse scaled):
Shape: (3984, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.253814902458322
Min value: -5.250298730157774
Checking X_price_val:
Shape: (443, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.324782722398346
Min value: -0.5755730380699846
Checking X_time_val:
Shape: (443, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_val (log_mse scaled):
Shape: (443, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.7902797110229687
Min value: -5.155379024632684
Checking X_price_test:
Shape: (1107, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.413509896698947
Min value: -0.5768892216495461
Checking X_time_test:
Shape: (1107, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_test (log_mse scaled):
Shape: (1107, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.5405778347636003
Min value: -5.155379024632684
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 170s 278ms/step - loss: 0.9819 - val_loss: 0.4949 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 24s 188ms/step - loss: 0.9328 - val_loss: 0.4904 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 24s 188ms/step - loss: 0.9376 - val_loss: 0.5537 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 24s 190ms/step - loss: 0.9440 - val_loss: 0.4549 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 24s 190ms/step - loss: 0.9471 - val_loss: 0.4961 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 23s 188ms/step - loss: 0.9538 - val_loss: 0.5113 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 26s 210ms/step - loss: 0.9184 - val_loss: 0.4754 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 30s 237ms/step - loss: 0.9252 - val_loss: 0.4880 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 220ms/step - loss: 0.9374
Epoch 9: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
125/125 ━━━━━━━━━━━━━━━━━━━━ 28s 226ms/step - loss: 0.9429 - val_loss: 0.4898 - learning_rate: 5.0000e-04
Epoch 9: early stopping
Restoring model weights from the end of the best epoch: 4.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 1
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.40801242
1 day(s) RMSE : 0.68180273
1 day(s) R2 : -0.08247278
1 day(s) Pearson r : 0.35730863
1 day(s) QLIKE : 0.49933048
full horizon MAE : 0.40801242
full horizon RMSE : 0.68180273
full horizon R2 : -0.08247278
full horizon Pearson r : 0.35730863
full horizon QLIKE : 0.49933048
--- Task 2 ---
1 day(s) MAE : 0.04010084
1 day(s) RMSE : 0.06087685
1 day(s) R2 : -0.76161096
1 day(s) Pearson r : 0.08691458
1 day(s) QLIKE : 10.86233446
full horizon MAE : 0.04010084
full horizon RMSE : 0.06087685
full horizon R2 : -0.76161096
full horizon Pearson r : 0.08691458
full horizon QLIKE : 10.86233446
--- Task 3 ---
1 day(s) MAE : 0.42627181
1 day(s) RMSE : 0.51775649
1 day(s) R2 : -1.56194297
1 day(s) Pearson r : 0.15360883
1 day(s) QLIKE : 3.66665696
full horizon MAE : 0.42627181
full horizon RMSE : 0.51775649
full horizon R2 : -1.56194297
full horizon Pearson r : 0.15360883
full horizon QLIKE : 3.66665696
--- Task 4 ---
1 day(s) MAE : 0.12105574
1 day(s) RMSE : 0.25655662
1 day(s) R2 : -0.28488070
1 day(s) Pearson r : -0.03855730
1 day(s) QLIKE : 2.17782323
full horizon MAE : 0.12105574
full horizon RMSE : 0.25655662
full horizon R2 : -0.28488070
full horizon Pearson r : -0.03855730
full horizon QLIKE : 2.17782323
--- Task 5 ---
1 day(s) MAE : 0.03992821
1 day(s) RMSE : 0.05996898
1 day(s) R2 : -0.79221839
1 day(s) Pearson r : 0.24440235
1 day(s) QLIKE : 5.85887460
full horizon MAE : 0.03992821
full horizon RMSE : 0.05996898
full horizon R2 : -0.79221839
full horizon Pearson r : 0.24440235
full horizon QLIKE : 5.85887460
--- Task 6 ---
1 day(s) MAE : 0.66812460
1 day(s) RMSE : 0.80557110
1 day(s) R2 : -1.03006846
1 day(s) Pearson r : 0.02896732
1 day(s) QLIKE : 0.02469974
full horizon MAE : 0.66812460
full horizon RMSE : 0.80557110
full horizon R2 : -1.03006846
full horizon Pearson r : 0.02896732
full horizon QLIKE : 0.02469974
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GOLD/Custom_KAN_LSTM_H1.pkl
Saved y_true min=0.00380168, max=7.43162
Saved y_pred min=0.273148, max=0.979725
=== GOLD | H=5 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 5534
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (3984, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 17.63876776611196
Min value: -0.5790835075307484
Checking X_time_train_core:
Shape: (3984, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.678060596158252
Checking y_train_core (log_mse scaled):
Shape: (3984, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.254005191594221
Min value: -5.250623802304656
Checking X_price_val:
Shape: (443, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.324782722398346
Min value: -0.5755730380699846
Checking X_time_val:
Shape: (443, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_val (log_mse scaled):
Shape: (443, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.790672308331636
Min value: -5.15537980838342
Checking X_price_test:
Shape: (1107, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.413509896698947
Min value: -0.5768892216495461
Checking X_time_test:
Shape: (1107, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_test (log_mse scaled):
Shape: (1107, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.5409449791338365
Min value: -5.15537980838342
Epoch 001 | phase=1 | train_loss=4.8453 | val_main=0.486215
Epoch 002 | phase=1 | train_loss=4.4619 | val_main=0.486261
Epoch 003 | phase=1 | train_loss=4.2445 | val_main=0.486245
Epoch 004 | phase=1 | train_loss=4.1387 | val_main=0.486258
Epoch 005 | phase=1 | train_loss=4.0148 | val_main=0.486243
Epoch 006 | phase=1 | train_loss=3.8990 | val_main=0.486245
Epoch 007 | phase=1 | train_loss=3.8274 | val_main=0.486262
Epoch 008 | phase=1 | train_loss=3.8064 | val_main=0.486269
Epoch 009 | phase=1 | train_loss=3.7854 | val_main=0.486322
Epoch 010 | phase=1 | train_loss=3.6809 | val_main=0.486266
Epoch 011 | phase=1 | train_loss=3.6880 | val_main=0.486267
Epoch 012 | phase=1 | train_loss=3.5869 | val_main=0.486270
Epoch 013 | phase=1 | train_loss=3.4723 | val_main=0.486287
Epoch 014 | phase=1 | train_loss=3.4259 | val_main=0.486319
Epoch 015 | phase=1 | train_loss=3.3217 | val_main=0.486316
Epoch 016 | phase=0 | train_loss=4.1412 | val_main=0.466364
Epoch 017 | phase=0 | train_loss=4.0098 | val_main=0.465184
Epoch 018 | phase=0 | train_loss=3.8002 | val_main=0.463814
Epoch 019 | phase=0 | train_loss=3.5507 | val_main=0.426784
Epoch 020 | phase=0 | train_loss=3.3307 | val_main=0.385905
Epoch 021 | phase=0 | train_loss=3.1569 | val_main=0.353550
Epoch 022 | phase=0 | train_loss=2.9818 | val_main=0.329787
Epoch 023 | phase=0 | train_loss=2.8774 | val_main=0.303995
Epoch 024 | phase=0 | train_loss=2.7591 | val_main=0.291238
Epoch 025 | phase=0 | train_loss=2.7347 | val_main=0.277308
Epoch 026 | phase=0 | train_loss=2.6591 | val_main=0.271639
Epoch 027 | phase=0 | train_loss=2.5987 | val_main=0.269702
Epoch 028 | phase=0 | train_loss=2.4902 | val_main=0.263617
Epoch 029 | phase=0 | train_loss=2.5356 | val_main=0.254756
Epoch 030 | phase=0 | train_loss=2.4211 | val_main=0.252715
Epoch 031 | phase=2 | train_loss=0.1827 | val_main=0.257835
Epoch 032 | phase=2 | train_loss=0.1729 | val_main=0.260639
Epoch 033 | phase=2 | train_loss=0.1664 | val_main=0.255945
Epoch 034 | phase=2 | train_loss=0.1606 | val_main=0.252781
Epoch 035 | phase=2 | train_loss=0.1523 | val_main=0.257684
Epoch 036 | phase=2 | train_loss=0.1459 | val_main=0.255870
Epoch 037 | phase=2 | train_loss=0.1426 | val_main=0.259780
Epoch 038 | phase=2 | train_loss=0.1376 | val_main=0.258463
Epoch 039 | phase=2 | train_loss=0.1320 | val_main=0.263569
Epoch 040 | phase=2 | train_loss=0.1293 | val_main=0.263713
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 30
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.30624593
1 day(s) RMSE : 0.57106105
1 day(s) R2 : 0.24061049
1 day(s) Pearson r : 0.50846614
1 day(s) QLIKE : 0.43266136
3 day(s) MAE : 0.30688224
3 day(s) RMSE : 0.58262028
3 day(s) R2 : 0.21021676
3 day(s) Pearson r : 0.49192331
3 day(s) QLIKE : 0.46900276
5 day(s) MAE : 0.31118324
5 day(s) RMSE : 0.58999464
5 day(s) R2 : 0.19240147
5 day(s) Pearson r : 0.47875836
5 day(s) QLIKE : 0.48800106
full horizon MAE : 0.31118324
full horizon RMSE : 0.58999464
full horizon R2 : 0.19240147
full horizon Pearson r : 0.47875836
full horizon QLIKE : 0.48800106
--- Task 2 ---
1 day(s) MAE : 0.03859776
1 day(s) RMSE : 0.05800627
1 day(s) R2 : -0.59939448
1 day(s) Pearson r : 0.03273520
1 day(s) QLIKE : 16.12666359
3 day(s) MAE : 0.03876399
3 day(s) RMSE : 0.05825532
3 day(s) R2 : -0.61418451
3 day(s) Pearson r : 0.00749837
3 day(s) QLIKE : 16.00127841
5 day(s) MAE : 0.03898686
5 day(s) RMSE : 0.05843149
5 day(s) R2 : -0.62498962
5 day(s) Pearson r : 0.01286188
5 day(s) QLIKE : 16.08296578
full horizon MAE : 0.03898686
full horizon RMSE : 0.05843149
full horizon R2 : -0.62498962
full horizon Pearson r : 0.01286188
full horizon QLIKE : 16.08296578
--- Task 3 ---
1 day(s) MAE : 0.32218820
1 day(s) RMSE : 0.47121069
1 day(s) R2 : -1.12201589
1 day(s) Pearson r : -0.28189649
1 day(s) QLIKE : 8.99346361
3 day(s) MAE : 0.31321132
3 day(s) RMSE : 0.46065969
3 day(s) R2 : -1.02846555
3 day(s) Pearson r : -0.26209868
3 day(s) QLIKE : 8.91067406
5 day(s) MAE : 0.31150593
5 day(s) RMSE : 0.46173986
5 day(s) R2 : -1.03828651
5 day(s) Pearson r : -0.26026291
5 day(s) QLIKE : 8.76303588
full horizon MAE : 0.31150593
full horizon RMSE : 0.46173986
full horizon R2 : -1.03828651
full horizon Pearson r : -0.26026291
full horizon QLIKE : 8.76303588
--- Task 4 ---
1 day(s) MAE : 0.12266798
1 day(s) RMSE : 0.25652902
1 day(s) R2 : -0.28460428
1 day(s) Pearson r : -0.14410528
1 day(s) QLIKE : 9.64751757
3 day(s) MAE : 0.12266684
3 day(s) RMSE : 0.25647125
3 day(s) R2 : -0.28417005
3 day(s) Pearson r : -0.13973070
3 day(s) QLIKE : 9.55576185
5 day(s) MAE : 0.12262754
5 day(s) RMSE : 0.25651424
5 day(s) R2 : -0.28473536
5 day(s) Pearson r : -0.13884136
5 day(s) QLIKE : 9.58383400
full horizon MAE : 0.12262754
full horizon RMSE : 0.25651424
full horizon R2 : -0.28473536
full horizon Pearson r : -0.13884136
full horizon QLIKE : 9.58383400
--- Task 5 ---
1 day(s) MAE : 0.03931979
1 day(s) RMSE : 0.05751320
1 day(s) R2 : -0.64843829
1 day(s) Pearson r : 0.09119713
1 day(s) QLIKE : 16.15975286
3 day(s) MAE : 0.03948505
3 day(s) RMSE : 0.05761201
3 day(s) R2 : -0.64709866
3 day(s) Pearson r : 0.08992802
3 day(s) QLIKE : 16.19944717
5 day(s) MAE : 0.03968304
5 day(s) RMSE : 0.05782702
5 day(s) R2 : -0.65194128
5 day(s) Pearson r : 0.08773869
5 day(s) QLIKE : 16.21979674
full horizon MAE : 0.03968304
full horizon RMSE : 0.05782702
full horizon R2 : -0.65194128
full horizon Pearson r : 0.08773869
full horizon QLIKE : 16.21979674
--- Task 6 ---
1 day(s) MAE : 0.94327033
1 day(s) RMSE : 1.92860666
1 day(s) R2 : -10.63563947
1 day(s) Pearson r : -0.06782363
1 day(s) QLIKE : 0.04258075
3 day(s) MAE : 0.94821628
3 day(s) RMSE : 1.94877123
3 day(s) R2 : -10.85781622
3 day(s) Pearson r : -0.07384000
3 day(s) QLIKE : 0.04297525
5 day(s) MAE : 0.94726449
5 day(s) RMSE : 1.95748450
5 day(s) R2 : -10.94429834
5 day(s) Pearson r : -0.07532901
5 day(s) QLIKE : 0.04290490
full horizon MAE : 0.94726449
full horizon RMSE : 1.95748450
full horizon R2 : -10.94429834
full horizon Pearson r : -0.07532901
full horizon QLIKE : 0.04290490
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GOLD/Custom_KAN_H5.pkl
Saved y_true min=0.00380168, max=7.43162
Saved y_pred min=0.00236784, max=2.65448
=== GOLD | H=5 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 5534
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (3984, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 17.63876776611196
Min value: -0.5790835075307484
Checking X_time_train_core:
Shape: (3984, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.678060596158252
Checking y_train_core (log_mse scaled):
Shape: (3984, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.254005191594221
Min value: -5.250623802304656
Checking X_price_val:
Shape: (443, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.324782722398346
Min value: -0.5755730380699846
Checking X_time_val:
Shape: (443, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_val (log_mse scaled):
Shape: (443, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.790672308331636
Min value: -5.15537980838342
Checking X_price_test:
Shape: (1107, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.413509896698947
Min value: -0.5768892216495461
Checking X_time_test:
Shape: (1107, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_test (log_mse scaled):
Shape: (1107, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.5409449791338365
Min value: -5.15537980838342
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 185s 339ms/step - loss: 0.6295 - val_loss: 0.4907 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 31s 248ms/step - loss: 0.4689 - val_loss: 0.5174 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 31s 246ms/step - loss: 0.4561 - val_loss: 0.4856 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 31s 246ms/step - loss: 0.4497 - val_loss: 0.6141 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 31s 248ms/step - loss: 0.4505 - val_loss: 0.5311 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 32s 253ms/step - loss: 0.4438 - val_loss: 0.5002 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 32s 256ms/step - loss: 0.4457 - val_loss: 0.5615 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 271ms/step - loss: 0.4325
Epoch 8: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
125/125 ━━━━━━━━━━━━━━━━━━━━ 35s 279ms/step - loss: 0.4444 - val_loss: 0.5347 - learning_rate: 5.0000e-04
Epoch 8: early stopping
Restoring model weights from the end of the best epoch: 3.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 5
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.43206667
1 day(s) RMSE : 0.71059443
1 day(s) R2 : -0.17582615
1 day(s) Pearson r : 0.40932228
1 day(s) QLIKE : 0.48399667
3 day(s) MAE : 0.44194800
3 day(s) RMSE : 0.71794698
3 day(s) R2 : -0.19928239
3 day(s) Pearson r : 0.33819012
3 day(s) QLIKE : 0.49724586
5 day(s) MAE : 0.45404049
5 day(s) RMSE : 0.73130276
5 day(s) R2 : -0.24077720
5 day(s) Pearson r : 0.31496787
5 day(s) QLIKE : 0.50507291
full horizon MAE : 0.45404049
full horizon RMSE : 0.73130276
full horizon R2 : -0.24077720
full horizon Pearson r : 0.31496787
full horizon QLIKE : 0.50507291
--- Task 2 ---
1 day(s) MAE : 0.04020814
1 day(s) RMSE : 0.06098747
1 day(s) R2 : -0.76801911
1 day(s) Pearson r : 0.12025653
1 day(s) QLIKE : 10.02086693
3 day(s) MAE : 0.04025540
3 day(s) RMSE : 0.06100942
3 day(s) R2 : -0.77041775
3 day(s) Pearson r : 0.08470533
3 day(s) QLIKE : 11.61569066
5 day(s) MAE : 0.04030857
5 day(s) RMSE : 0.06103612
5 day(s) R2 : -0.77308897
5 day(s) Pearson r : 0.06528350
5 day(s) QLIKE : 10.47297948
full horizon MAE : 0.04030857
full horizon RMSE : 0.06103612
full horizon R2 : -0.77308897
full horizon Pearson r : 0.06528350
full horizon QLIKE : 10.47297948
--- Task 3 ---
1 day(s) MAE : 0.40612354
1 day(s) RMSE : 0.46610950
1 day(s) R2 : -1.07631995
1 day(s) Pearson r : 0.10193709
1 day(s) QLIKE : 3.55901107
3 day(s) MAE : 0.53266278
3 day(s) RMSE : 0.64209813
3 day(s) R2 : -2.94103261
3 day(s) Pearson r : 0.09341926
3 day(s) QLIKE : 9.12673219
5 day(s) MAE : 0.58925515
5 day(s) RMSE : 0.69280139
5 day(s) R2 : -3.58868228
5 day(s) Pearson r : 0.08227731
5 day(s) QLIKE : 8.54226127
full horizon MAE : 0.58925515
full horizon RMSE : 0.69280139
full horizon R2 : -3.58868228
full horizon Pearson r : 0.08227731
full horizon QLIKE : 8.54226127
--- Task 4 ---
1 day(s) MAE : 0.12250138
1 day(s) RMSE : 0.25716060
1 day(s) R2 : -0.29093752
1 day(s) Pearson r : 0.01238657
1 day(s) QLIKE : 2.04339468
3 day(s) MAE : 0.12312037
3 day(s) RMSE : 0.25721707
3 day(s) R2 : -0.29164964
3 day(s) Pearson r : -0.00261888
3 day(s) QLIKE : 2.24709272
5 day(s) MAE : 0.12348446
5 day(s) RMSE : 0.25713565
5 day(s) R2 : -0.29096746
5 day(s) Pearson r : -0.00733922
5 day(s) QLIKE : 2.27108617
full horizon MAE : 0.12348446
full horizon RMSE : 0.25713565
full horizon R2 : -0.29096746
full horizon Pearson r : -0.00733922
full horizon QLIKE : 2.27108617
--- Task 5 ---
1 day(s) MAE : 0.03995632
1 day(s) RMSE : 0.06001259
1 day(s) R2 : -0.79482590
1 day(s) Pearson r : 0.22361798
1 day(s) QLIKE : 6.10222592
3 day(s) MAE : 0.04005451
3 day(s) RMSE : 0.06015792
3 day(s) R2 : -0.79588768
3 day(s) Pearson r : 0.12179638
3 day(s) QLIKE : 12.63240604
5 day(s) MAE : 0.04014805
5 day(s) RMSE : 0.06029767
5 day(s) R2 : -0.79611487
5 day(s) Pearson r : 0.09282771
5 day(s) QLIKE : 10.08221868
full horizon MAE : 0.04014805
full horizon RMSE : 0.06029767
full horizon R2 : -0.79611487
full horizon Pearson r : 0.09282771
full horizon QLIKE : 10.08221868
--- Task 6 ---
1 day(s) MAE : 0.68837781
1 day(s) RMSE : 0.83233554
1 day(s) R2 : -1.16720408
1 day(s) Pearson r : -0.08158636
1 day(s) QLIKE : 0.02485084
3 day(s) MAE : 0.71045682
3 day(s) RMSE : 0.85609521
3 day(s) R2 : -1.28837467
3 day(s) Pearson r : -0.07304605
3 day(s) QLIKE : 0.02518731
5 day(s) MAE : 0.69918520
5 day(s) RMSE : 0.84706948
5 day(s) R2 : -1.23667070
5 day(s) Pearson r : -0.06897188
5 day(s) QLIKE : 0.02440826
full horizon MAE : 0.69918520
full horizon RMSE : 0.84706948
full horizon R2 : -1.23667070
full horizon Pearson r : -0.06897188
full horizon QLIKE : 0.02440826
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GOLD/Custom_KAN_LSTM_H5.pkl
Saved y_true min=0.00380168, max=7.43162
Saved y_pred min=0.186091, max=0.642617
=== GOLD | H=10 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 5534
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (3984, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 17.63876776611196
Min value: -0.5790835075307484
Checking X_time_train_core:
Shape: (3984, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.678060596158252
Checking y_train_core (log_mse scaled):
Shape: (3984, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.254200938794579
Min value: -5.254244390683287
Checking X_price_val:
Shape: (443, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.324782722398346
Min value: -0.5755730380699846
Checking X_time_val:
Shape: (443, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_val (log_mse scaled):
Shape: (443, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.7918153902171274
Min value: -5.155380789277184
Checking X_price_test:
Shape: (1107, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.413509896698947
Min value: -0.5768892216495461
Checking X_time_test:
Shape: (1107, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_test (log_mse scaled):
Shape: (1107, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.5419191122017313
Min value: -5.155380789277184
Epoch 001 | phase=1 | train_loss=4.8597 | val_main=0.487606
Epoch 002 | phase=1 | train_loss=4.4647 | val_main=0.487826
Epoch 003 | phase=1 | train_loss=4.2466 | val_main=0.487803
Epoch 004 | phase=1 | train_loss=4.1432 | val_main=0.487805
Epoch 005 | phase=1 | train_loss=4.0099 | val_main=0.487810
Epoch 006 | phase=1 | train_loss=3.8979 | val_main=0.487801
Epoch 007 | phase=1 | train_loss=3.8308 | val_main=0.487822
Epoch 008 | phase=1 | train_loss=3.7954 | val_main=0.487743
Epoch 009 | phase=1 | train_loss=3.8122 | val_main=0.487778
Epoch 010 | phase=1 | train_loss=3.6835 | val_main=0.487863
Epoch 011 | phase=1 | train_loss=3.6653 | val_main=0.487775
Epoch 012 | phase=1 | train_loss=3.6700 | val_main=0.487901
Epoch 013 | phase=1 | train_loss=3.5210 | val_main=0.488104
Epoch 014 | phase=1 | train_loss=3.4407 | val_main=0.487977
Epoch 015 | phase=1 | train_loss=3.4414 | val_main=0.488074
Epoch 016 | phase=0 | train_loss=4.2478 | val_main=0.467706
Epoch 017 | phase=0 | train_loss=4.1510 | val_main=0.462619
Epoch 018 | phase=0 | train_loss=4.0065 | val_main=0.467026
Epoch 019 | phase=0 | train_loss=3.8065 | val_main=0.443451
Epoch 020 | phase=0 | train_loss=3.6277 | val_main=0.399249
Epoch 021 | phase=0 | train_loss=3.4504 | val_main=0.382608
Epoch 022 | phase=0 | train_loss=3.3354 | val_main=0.360200
Epoch 023 | phase=0 | train_loss=3.1891 | val_main=0.339906
Epoch 024 | phase=0 | train_loss=3.0212 | val_main=0.322760
Epoch 025 | phase=0 | train_loss=3.0170 | val_main=0.309964
Epoch 026 | phase=0 | train_loss=2.8825 | val_main=0.291942
Epoch 027 | phase=0 | train_loss=2.8326 | val_main=0.280712
Epoch 028 | phase=0 | train_loss=2.7493 | val_main=0.274371
Epoch 029 | phase=0 | train_loss=2.6625 | val_main=0.263521
Epoch 030 | phase=0 | train_loss=2.6681 | val_main=0.262418
Epoch 031 | phase=2 | train_loss=0.2141 | val_main=0.260010
Epoch 032 | phase=2 | train_loss=0.1979 | val_main=0.259481
Epoch 033 | phase=2 | train_loss=0.1910 | val_main=0.259308
Epoch 034 | phase=2 | train_loss=0.1811 | val_main=0.268092
Epoch 035 | phase=2 | train_loss=0.1761 | val_main=0.258075
Epoch 036 | phase=2 | train_loss=0.1694 | val_main=0.264835
Epoch 037 | phase=2 | train_loss=0.1649 | val_main=0.263034
Epoch 038 | phase=2 | train_loss=0.1605 | val_main=0.259285
Epoch 039 | phase=2 | train_loss=0.1553 | val_main=0.263563
Epoch 040 | phase=2 | train_loss=0.1517 | val_main=0.263600
Epoch 041 | phase=2 | train_loss=0.1493 | val_main=0.268854
Epoch 042 | phase=2 | train_loss=0.1483 | val_main=0.268007
Epoch 043 | phase=2 | train_loss=0.1446 | val_main=0.264932
Epoch 044 | phase=2 | train_loss=0.1437 | val_main=0.276412
Epoch 045 | phase=2 | train_loss=0.1391 | val_main=0.272636
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 60
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.29360863
1 day(s) RMSE : 0.54910075
1 day(s) R2 : 0.29789255
1 day(s) Pearson r : 0.56564221
1 day(s) QLIKE : 0.46071299
3 day(s) MAE : 0.29934640
3 day(s) RMSE : 0.56427540
3 day(s) R2 : 0.25916932
3 day(s) Pearson r : 0.53168179
3 day(s) QLIKE : 0.46458633
5 day(s) MAE : 0.30527375
5 day(s) RMSE : 0.57499768
5 day(s) R2 : 0.23293606
5 day(s) Pearson r : 0.50964844
5 day(s) QLIKE : 0.47451782
10 day(s) MAE : 0.31790361
10 day(s) RMSE : 0.58755326
10 day(s) R2 : 0.20800525
10 day(s) Pearson r : 0.48846445
10 day(s) QLIKE : 0.50967996
full horizon MAE : 0.31790361
full horizon RMSE : 0.58755326
full horizon R2 : 0.20800525
full horizon Pearson r : 0.48846445
full horizon QLIKE : 0.50967996
--- Task 2 ---
1 day(s) MAE : 0.03845333
1 day(s) RMSE : 0.05787924
1 day(s) R2 : -0.59239671
1 day(s) Pearson r : 0.06653659
1 day(s) QLIKE : 17.07209622
3 day(s) MAE : 0.03870939
3 day(s) RMSE : 0.05832744
3 day(s) R2 : -0.61818348
3 day(s) Pearson r : 0.03789019
3 day(s) QLIKE : 17.07478202
5 day(s) MAE : 0.03875479
5 day(s) RMSE : 0.05838072
5 day(s) R2 : -0.62216714
5 day(s) Pearson r : 0.03804032
5 day(s) QLIKE : 17.12412113
10 day(s) MAE : 0.03897190
10 day(s) RMSE : 0.05949218
10 day(s) R2 : -0.68808215
10 day(s) Pearson r : 0.05820633
10 day(s) QLIKE : 17.06791371
full horizon MAE : 0.03897190
full horizon RMSE : 0.05949218
full horizon R2 : -0.68808215
full horizon Pearson r : 0.05820633
full horizon QLIKE : 17.06791371
--- Task 3 ---
1 day(s) MAE : 0.32123822
1 day(s) RMSE : 0.47028574
1 day(s) R2 : -1.11369336
1 day(s) Pearson r : -0.20389476
1 day(s) QLIKE : 8.33558087
3 day(s) MAE : 0.31618882
3 day(s) RMSE : 0.46758947
3 day(s) R2 : -1.08995366
3 day(s) Pearson r : -0.20020862
3 day(s) QLIKE : 8.27873845
5 day(s) MAE : 0.31730235
5 day(s) RMSE : 0.46995817
5 day(s) R2 : -1.11148937
5 day(s) Pearson r : -0.20227173
5 day(s) QLIKE : 8.18226782
10 day(s) MAE : 0.31971297
10 day(s) RMSE : 0.46889605
10 day(s) R2 : -1.11376202
10 day(s) Pearson r : -0.20462423
10 day(s) QLIKE : 8.13434322
full horizon MAE : 0.31971297
full horizon RMSE : 0.46889605
full horizon R2 : -1.11376202
full horizon Pearson r : -0.20462423
full horizon QLIKE : 8.13434322
--- Task 4 ---
1 day(s) MAE : 0.12365697
1 day(s) RMSE : 0.25683762
1 day(s) R2 : -0.28769687
1 day(s) Pearson r : -0.10019129
1 day(s) QLIKE : 9.00004823
3 day(s) MAE : 0.12375450
3 day(s) RMSE : 0.25674392
3 day(s) R2 : -0.28690208
3 day(s) Pearson r : -0.09109618
3 day(s) QLIKE : 9.00581733
5 day(s) MAE : 0.12364490
5 day(s) RMSE : 0.25668043
5 day(s) R2 : -0.28640058
5 day(s) Pearson r : -0.08332395
5 day(s) QLIKE : 9.04784459
10 day(s) MAE : 0.12299866
10 day(s) RMSE : 0.25576006
10 day(s) R2 : -0.28630420
10 day(s) Pearson r : -0.08520947
10 day(s) QLIKE : 9.00207557
full horizon MAE : 0.12299866
full horizon RMSE : 0.25576006
full horizon R2 : -0.28630420
full horizon Pearson r : -0.08520947
full horizon QLIKE : 9.00207557
--- Task 5 ---
1 day(s) MAE : 0.04082043
1 day(s) RMSE : 0.05998545
1 day(s) R2 : -0.79320311
1 day(s) Pearson r : 0.00461281
1 day(s) QLIKE : 16.01003543
3 day(s) MAE : 0.04068012
3 day(s) RMSE : 0.05978835
3 day(s) R2 : -0.77389015
3 day(s) Pearson r : 0.01317244
3 day(s) QLIKE : 16.13536875
5 day(s) MAE : 0.04077408
5 day(s) RMSE : 0.05992991
5 day(s) R2 : -0.77427256
5 day(s) Pearson r : 0.01662860
5 day(s) QLIKE : 16.16940756
10 day(s) MAE : 0.04080745
10 day(s) RMSE : 0.06009523
10 day(s) R2 : -0.76390668
10 day(s) Pearson r : 0.01847634
10 day(s) QLIKE : 16.22218721
full horizon MAE : 0.04080745
full horizon RMSE : 0.06009523
full horizon R2 : -0.76390668
full horizon Pearson r : 0.01847634
full horizon QLIKE : 16.22218721
--- Task 6 ---
1 day(s) MAE : 1.03990013
1 day(s) RMSE : 2.10210271
1 day(s) R2 : -12.82327021
1 day(s) Pearson r : -0.06105258
1 day(s) QLIKE : 0.04843702
3 day(s) MAE : 1.04135044
3 day(s) RMSE : 2.10568548
3 day(s) R2 : -12.84426799
3 day(s) Pearson r : -0.06879452
3 day(s) QLIKE : 0.04869126
5 day(s) MAE : 1.04461871
5 day(s) RMSE : 2.12246456
5 day(s) R2 : -13.04251420
5 day(s) Pearson r : -0.07317854
5 day(s) QLIKE : 0.04896127
10 day(s) MAE : 1.04698649
10 day(s) RMSE : 2.15755178
10 day(s) R2 : -13.41787317
10 day(s) Pearson r : -0.07981985
10 day(s) QLIKE : 0.04918642
full horizon MAE : 1.04698649
full horizon RMSE : 2.15755178
full horizon R2 : -13.41787317
full horizon Pearson r : -0.07981985
full horizon QLIKE : 0.04918642
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GOLD/Custom_KAN_H10.pkl
Saved y_true min=0.00380168, max=7.43162
Saved y_pred min=0.00299108, max=3.8361
=== GOLD | H=10 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 5534
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (3984, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 17.63876776611196
Min value: -0.5790835075307484
Checking X_time_train_core:
Shape: (3984, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.678060596158252
Checking y_train_core (log_mse scaled):
Shape: (3984, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.254200938794579
Min value: -5.254244390683287
Checking X_price_val:
Shape: (443, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.324782722398346
Min value: -0.5755730380699846
Checking X_time_val:
Shape: (443, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_val (log_mse scaled):
Shape: (443, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.7918153902171274
Min value: -5.155380789277184
Checking X_price_test:
Shape: (1107, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.413509896698947
Min value: -0.5768892216495461
Checking X_time_test:
Shape: (1107, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_test (log_mse scaled):
Shape: (1107, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.5419191122017313
Min value: -5.155380789277184
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 190s 364ms/step - loss: 0.5710 - val_loss: 0.5149 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 39s 315ms/step - loss: 0.4048 - val_loss: 0.6005 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 39s 313ms/step - loss: 0.3923 - val_loss: 0.5700 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 39s 313ms/step - loss: 0.3842 - val_loss: 0.6132 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 39s 312ms/step - loss: 0.3787 - val_loss: 0.6285 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 305ms/step - loss: 0.3689
Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
125/125 ━━━━━━━━━━━━━━━━━━━━ 39s 314ms/step - loss: 0.3781 - val_loss: 0.6857 - learning_rate: 5.0000e-04
Epoch 6: early stopping
Restoring model weights from the end of the best epoch: 1.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 10
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.42404226
1 day(s) RMSE : 0.71976519
1 day(s) R2 : -0.20637186
1 day(s) Pearson r : 0.45772640
1 day(s) QLIKE : 0.52656784
3 day(s) MAE : 0.43106096
3 day(s) RMSE : 0.72779656
3 day(s) R2 : -0.23241425
3 day(s) Pearson r : 0.06627836
3 day(s) QLIKE : 0.53470351
5 day(s) MAE : 0.44396251
5 day(s) RMSE : 0.74036189
5 day(s) R2 : -0.27170826
5 day(s) Pearson r : 0.04071285
5 day(s) QLIKE : 0.53896608
10 day(s) MAE : 0.45015701
10 day(s) RMSE : 0.74803716
10 day(s) R2 : -0.28373141
10 day(s) Pearson r : 0.06546412
10 day(s) QLIKE : 0.53658728
full horizon MAE : 0.45015701
full horizon RMSE : 0.74803716
full horizon R2 : -0.28373141
full horizon Pearson r : 0.06546412
full horizon QLIKE : 0.53658728
--- Task 2 ---
1 day(s) MAE : 0.04022236
1 day(s) RMSE : 0.06100458
1 day(s) R2 : -0.76901104
1 day(s) Pearson r : 0.09617776
1 day(s) QLIKE : 7.88909217
3 day(s) MAE : 0.04026947
3 day(s) RMSE : 0.06102485
3 day(s) R2 : -0.77131361
3 day(s) Pearson r : 0.02318208
3 day(s) QLIKE : 12.77419191
5 day(s) MAE : 0.04031703
5 day(s) RMSE : 0.06104540
5 day(s) R2 : -0.77362771
5 day(s) Pearson r : 0.01597958
5 day(s) QLIKE : 11.38443393
10 day(s) MAE : 0.04035356
10 day(s) RMSE : 0.06103325
10 day(s) R2 : -0.77667028
10 day(s) Pearson r : 0.01043649
10 day(s) QLIKE : 9.70225226
full horizon MAE : 0.04035356
full horizon RMSE : 0.06103325
full horizon R2 : -0.77667028
full horizon Pearson r : 0.01043649
full horizon QLIKE : 9.70225226
--- Task 3 ---
1 day(s) MAE : 0.60274216
1 day(s) RMSE : 0.62241384
1 day(s) R2 : -2.70234460
1 day(s) Pearson r : 0.02240481
1 day(s) QLIKE : 3.46570356
3 day(s) MAE : 0.73301975
3 day(s) RMSE : 0.78952214
3 day(s) R2 : -4.95848485
3 day(s) Pearson r : 0.00034463
3 day(s) QLIKE : 6.37132781
5 day(s) MAE : 0.75930379
5 day(s) RMSE : 0.81916049
5 day(s) R2 : -5.41517465
5 day(s) Pearson r : 0.00027992
5 day(s) QLIKE : 6.16621363
10 day(s) MAE : 0.77985219
10 day(s) RMSE : 0.84115812
10 day(s) R2 : -5.80233819
10 day(s) Pearson r : -0.00066100
10 day(s) QLIKE : 5.43409639
full horizon MAE : 0.77985219
full horizon RMSE : 0.84115812
full horizon R2 : -5.80233819
full horizon Pearson r : -0.00066100
full horizon QLIKE : 5.43409639
--- Task 4 ---
1 day(s) MAE : 0.12328881
1 day(s) RMSE : 0.25750286
1 day(s) R2 : -0.29437605
1 day(s) Pearson r : 0.03309716
1 day(s) QLIKE : 1.87773506
3 day(s) MAE : 0.12409543
3 day(s) RMSE : 0.25795268
3 day(s) R2 : -0.29904813
3 day(s) Pearson r : 0.00440123
3 day(s) QLIKE : 1.91877380
5 day(s) MAE : 0.12427902
5 day(s) RMSE : 0.25805564
5 day(s) R2 : -0.30022181
5 day(s) Pearson r : -0.00417565
5 day(s) QLIKE : 1.91038814
10 day(s) MAE : 0.12453425
10 day(s) RMSE : 0.25753020
10 day(s) R2 : -0.30417104
10 day(s) Pearson r : 0.00116767
10 day(s) QLIKE : 2.52776731
full horizon MAE : 0.12453425
full horizon RMSE : 0.25753020
full horizon R2 : -0.30417104
full horizon Pearson r : 0.00116767
full horizon QLIKE : 2.52776731
--- Task 5 ---
1 day(s) MAE : 0.03993922
1 day(s) RMSE : 0.05999693
1 day(s) R2 : -0.79388908
1 day(s) Pearson r : 0.19143643
1 day(s) QLIKE : 5.82919191
3 day(s) MAE : 0.03997815
3 day(s) RMSE : 0.06006432
3 day(s) R2 : -0.79030362
3 day(s) Pearson r : 0.00013201
3 day(s) QLIKE : 6.24061190
5 day(s) MAE : 0.04010202
5 day(s) RMSE : 0.06024137
5 day(s) R2 : -0.79276223
5 day(s) Pearson r : -0.00134503
5 day(s) QLIKE : 14.73205392
10 day(s) MAE : 0.04035035
10 day(s) RMSE : 0.06061240
10 day(s) R2 : -0.79439726
10 day(s) Pearson r : -0.00294715
10 day(s) QLIKE : 12.46632148
full horizon MAE : 0.04035035
full horizon RMSE : 0.06061240
full horizon R2 : -0.79439726
full horizon Pearson r : -0.00294715
full horizon QLIKE : 12.46632148
--- Task 6 ---
1 day(s) MAE : 0.44225776
1 day(s) RMSE : 0.58547586
1 day(s) R2 : -0.07231140
1 day(s) Pearson r : -0.12081817
1 day(s) QLIKE : 0.01176597
3 day(s) MAE : 0.44974378
3 day(s) RMSE : 0.60362830
3 day(s) R2 : -0.13768538
3 day(s) Pearson r : -0.08419066
3 day(s) QLIKE : 0.01178449
5 day(s) MAE : 0.45056106
5 day(s) RMSE : 0.60224851
5 day(s) R2 : -0.13061682
5 day(s) Pearson r : -0.10136143
5 day(s) QLIKE : 0.01196118
10 day(s) MAE : 0.47998267
10 day(s) RMSE : 0.61731050
10 day(s) R2 : -0.18028278
10 day(s) Pearson r : -0.06516413
10 day(s) QLIKE : 0.01337909
full horizon MAE : 0.47998267
full horizon RMSE : 0.61731050
full horizon R2 : -0.18028278
full horizon Pearson r : -0.06516413
full horizon QLIKE : 0.01337909
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GOLD/Custom_KAN_LSTM_H10.pkl
Saved y_true min=0.00380168, max=7.43162
Saved y_pred min=0.272388, max=0.396835
=== GOLD | H=20 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 5534
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (3984, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 17.63876776611196
Min value: -0.5790835075307484
Checking X_time_train_core:
Shape: (3984, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.678060596158252
Checking y_train_core (log_mse scaled):
Shape: (3984, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.254162049886622
Min value: -5.257736685381158
Checking X_price_val:
Shape: (443, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.324782722398346
Min value: -0.5755730380699846
Checking X_time_val:
Shape: (443, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_val (log_mse scaled):
Shape: (443, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.7933331106383543
Min value: -5.155382782210931
Checking X_price_test:
Shape: (1107, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.413509896698947
Min value: -0.5768892216495461
Checking X_time_test:
Shape: (1107, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_test (log_mse scaled):
Shape: (1107, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.5432591469134986
Min value: -5.155382782210931
Epoch 001 | phase=1 | train_loss=4.8936 | val_main=0.489308
Epoch 002 | phase=1 | train_loss=4.5083 | val_main=0.489570
Epoch 003 | phase=1 | train_loss=4.2700 | val_main=0.489478
Epoch 004 | phase=1 | train_loss=4.1634 | val_main=0.489465
Epoch 005 | phase=1 | train_loss=4.0329 | val_main=0.489449
Epoch 006 | phase=1 | train_loss=3.9218 | val_main=0.489454
Epoch 007 | phase=1 | train_loss=3.8505 | val_main=0.489470
Epoch 008 | phase=1 | train_loss=3.8028 | val_main=0.489452
Epoch 009 | phase=1 | train_loss=3.8042 | val_main=0.489441
Epoch 010 | phase=1 | train_loss=3.7046 | val_main=0.489443
Epoch 011 | phase=1 | train_loss=3.7317 | val_main=0.489412
Epoch 012 | phase=1 | train_loss=3.6564 | val_main=0.489369
Epoch 013 | phase=1 | train_loss=3.5545 | val_main=0.489291
Epoch 014 | phase=1 | train_loss=3.5000 | val_main=0.489295
Epoch 015 | phase=1 | train_loss=3.4899 | val_main=0.489278
Epoch 016 | phase=0 | train_loss=4.3174 | val_main=0.478563
Epoch 017 | phase=0 | train_loss=4.2483 | val_main=0.472755
Epoch 018 | phase=0 | train_loss=4.1841 | val_main=0.487989
Epoch 019 | phase=0 | train_loss=3.9586 | val_main=0.495664
Epoch 020 | phase=0 | train_loss=3.8126 | val_main=0.456354
Epoch 021 | phase=0 | train_loss=3.6385 | val_main=0.446043
Epoch 022 | phase=0 | train_loss=3.4627 | val_main=0.426894
Epoch 023 | phase=0 | train_loss=3.3176 | val_main=0.405713
Epoch 024 | phase=0 | train_loss=3.1557 | val_main=0.388327
Epoch 025 | phase=0 | train_loss=3.3220 | val_main=0.380061
Epoch 026 | phase=0 | train_loss=3.0749 | val_main=0.359082
Epoch 027 | phase=0 | train_loss=3.0026 | val_main=0.346406
Epoch 028 | phase=0 | train_loss=2.9814 | val_main=0.331655
Epoch 029 | phase=0 | train_loss=2.8759 | val_main=0.325270
Epoch 030 | phase=0 | train_loss=2.8120 | val_main=0.310593
Epoch 031 | phase=2 | train_loss=0.2524 | val_main=0.304199
Epoch 032 | phase=2 | train_loss=0.2378 | val_main=0.297359
Epoch 033 | phase=2 | train_loss=0.2276 | val_main=0.293016
Epoch 034 | phase=2 | train_loss=0.2200 | val_main=0.287833
Epoch 035 | phase=2 | train_loss=0.2115 | val_main=0.292914
Epoch 036 | phase=2 | train_loss=0.2042 | val_main=0.285422
Epoch 037 | phase=2 | train_loss=0.1992 | val_main=0.293194
Epoch 038 | phase=2 | train_loss=0.1949 | val_main=0.286565
Epoch 039 | phase=2 | train_loss=0.1882 | val_main=0.285057
Epoch 040 | phase=2 | train_loss=0.1847 | val_main=0.292776
Epoch 041 | phase=2 | train_loss=0.1796 | val_main=0.290013
Epoch 042 | phase=2 | train_loss=0.1772 | val_main=0.291189
Epoch 043 | phase=2 | train_loss=0.1750 | val_main=0.298379
Epoch 044 | phase=2 | train_loss=0.1733 | val_main=0.294032
Epoch 045 | phase=2 | train_loss=0.1673 | val_main=0.295401
Epoch 046 | phase=2 | train_loss=0.1653 | val_main=0.299802
Epoch 047 | phase=2 | train_loss=0.1614 | val_main=0.301543
Epoch 048 | phase=2 | train_loss=0.1605 | val_main=0.299931
Epoch 049 | phase=2 | train_loss=0.1590 | val_main=0.302226
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 120
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.29729663
1 day(s) RMSE : 0.56791720
1 day(s) R2 : 0.24894876
1 day(s) Pearson r : 0.52812074
1 day(s) QLIKE : 0.42387564
3 day(s) MAE : 0.30936414
3 day(s) RMSE : 0.58992890
3 day(s) R2 : 0.19027778
3 day(s) Pearson r : 0.48381351
3 day(s) QLIKE : 0.41941654
5 day(s) MAE : 0.31299261
5 day(s) RMSE : 0.59163713
5 day(s) R2 : 0.18789865
5 day(s) Pearson r : 0.47603819
5 day(s) QLIKE : 0.44993289
10 day(s) MAE : 0.32408643
10 day(s) RMSE : 0.59401634
10 day(s) R2 : 0.19048556
10 day(s) Pearson r : 0.47967996
10 day(s) QLIKE : 0.48696600
20 day(s) MAE : 0.34681460
20 day(s) RMSE : 0.63178049
20 day(s) R2 : 0.09780080
20 day(s) Pearson r : 0.40129759
20 day(s) QLIKE : 0.56078898
full horizon MAE : 0.34681460
full horizon RMSE : 0.63178049
full horizon R2 : 0.09780080
full horizon Pearson r : 0.40129759
full horizon QLIKE : 0.56078898
--- Task 2 ---
1 day(s) MAE : 0.03788411
1 day(s) RMSE : 0.05709707
1 day(s) R2 : -0.54964898
1 day(s) Pearson r : 0.08087111
1 day(s) QLIKE : 16.96825879
3 day(s) MAE : 0.03807888
3 day(s) RMSE : 0.05730907
3 day(s) R2 : -0.56217118
3 day(s) Pearson r : 0.06273781
3 day(s) QLIKE : 16.97195979
5 day(s) MAE : 0.03802897
5 day(s) RMSE : 0.05733861
5 day(s) R2 : -0.56477136
5 day(s) Pearson r : 0.05918947
5 day(s) QLIKE : 17.03609578
10 day(s) MAE : 0.03801863
10 day(s) RMSE : 0.05739204
10 day(s) R2 : -0.57100353
10 day(s) Pearson r : 0.05423128
10 day(s) QLIKE : 17.06890578
20 day(s) MAE : 0.03811015
20 day(s) RMSE : 0.05771618
20 day(s) R2 : -0.59738993
20 day(s) Pearson r : 0.04204152
20 day(s) QLIKE : 16.94839515
full horizon MAE : 0.03811015
full horizon RMSE : 0.05771618
full horizon R2 : -0.59738993
full horizon Pearson r : 0.04204152
full horizon QLIKE : 16.94839515
--- Task 3 ---
1 day(s) MAE : 0.31759312
1 day(s) RMSE : 0.46096026
1 day(s) R2 : -1.03069792
1 day(s) Pearson r : -0.25387275
1 day(s) QLIKE : 8.40762181
3 day(s) MAE : 0.31070750
3 day(s) RMSE : 0.45751041
3 day(s) R2 : -1.00082529
3 day(s) Pearson r : -0.24431160
3 day(s) QLIKE : 8.42222126
5 day(s) MAE : 0.30876127
5 day(s) RMSE : 0.45739665
5 day(s) R2 : -1.00012178
5 day(s) Pearson r : -0.23973133
5 day(s) QLIKE : 8.30785568
10 day(s) MAE : 0.30860599
10 day(s) RMSE : 0.45411696
10 day(s) R2 : -0.98261497
10 day(s) Pearson r : -0.23547205
10 day(s) QLIKE : 8.21486230
20 day(s) MAE : 0.30142216
20 day(s) RMSE : 0.45121772
20 day(s) R2 : -1.00151273
20 day(s) Pearson r : -0.22723764
20 day(s) QLIKE : 8.16816411
full horizon MAE : 0.30142216
full horizon RMSE : 0.45121772
full horizon R2 : -1.00151273
full horizon Pearson r : -0.22723764
full horizon QLIKE : 8.16816411
--- Task 4 ---
1 day(s) MAE : 0.12370900
1 day(s) RMSE : 0.25701673
1 day(s) R2 : -0.28949345
1 day(s) Pearson r : -0.12937874
1 day(s) QLIKE : 8.39590494
3 day(s) MAE : 0.12376284
3 day(s) RMSE : 0.25699978
3 day(s) R2 : -0.28946828
3 day(s) Pearson r : -0.12936346
3 day(s) QLIKE : 8.24286418
5 day(s) MAE : 0.12365344
5 day(s) RMSE : 0.25694019
5 day(s) R2 : -0.28900560
5 day(s) Pearson r : -0.12813769
5 day(s) QLIKE : 8.21962200
10 day(s) MAE : 0.12289289
10 day(s) RMSE : 0.25592028
10 day(s) R2 : -0.28791632
10 day(s) Pearson r : -0.12774310
10 day(s) QLIKE : 8.10228091
20 day(s) MAE : 0.12028802
20 day(s) RMSE : 0.25198707
20 day(s) R2 : -0.28604155
20 day(s) Pearson r : -0.13674599
20 day(s) QLIKE : 7.75816848
full horizon MAE : 0.12028802
full horizon RMSE : 0.25198707
full horizon R2 : -0.28604155
full horizon Pearson r : -0.13674599
full horizon QLIKE : 7.75816848
--- Task 5 ---
1 day(s) MAE : 0.03977641
1 day(s) RMSE : 0.05893167
1 day(s) R2 : -0.73075295
1 day(s) Pearson r : 0.05609695
1 day(s) QLIKE : 16.68690647
3 day(s) MAE : 0.04024153
3 day(s) RMSE : 0.05987984
3 day(s) R2 : -0.77932313
3 day(s) Pearson r : 0.02929184
3 day(s) QLIKE : 16.80632118
5 day(s) MAE : 0.04035758
5 day(s) RMSE : 0.06015892
5 day(s) R2 : -0.78785839
5 day(s) Pearson r : 0.03481701
5 day(s) QLIKE : 16.86478300
10 day(s) MAE : 0.04042123
10 day(s) RMSE : 0.06028893
10 day(s) R2 : -0.77529609
10 day(s) Pearson r : 0.05814383
10 day(s) QLIKE : 16.97168783
20 day(s) MAE : 0.04067649
20 day(s) RMSE : 0.06072623
20 day(s) R2 : -0.76205558
20 day(s) Pearson r : 0.05985624
20 day(s) QLIKE : 17.09257053
full horizon MAE : 0.04067649
full horizon RMSE : 0.06072623
full horizon R2 : -0.76205558
full horizon Pearson r : 0.05985624
full horizon QLIKE : 17.09257053
--- Task 6 ---
1 day(s) MAE : 0.92604995
1 day(s) RMSE : 1.95676490
1 day(s) R2 : -10.97788754
1 day(s) Pearson r : -0.06112892
1 day(s) QLIKE : 0.04170543
3 day(s) MAE : 0.92204898
3 day(s) RMSE : 1.96903186
3 day(s) R2 : -11.10566028
3 day(s) Pearson r : -0.06467843
3 day(s) QLIKE : 0.04147765
5 day(s) MAE : 0.92420110
5 day(s) RMSE : 1.99093458
5 day(s) R2 : -11.35600167
5 day(s) Pearson r : -0.06746098
5 day(s) QLIKE : 0.04164399
10 day(s) MAE : 0.92912206
10 day(s) RMSE : 2.05030717
10 day(s) R2 : -12.02016855
10 day(s) Pearson r : -0.06805644
10 day(s) QLIKE : 0.04198280
20 day(s) MAE : 0.93656564
20 day(s) RMSE : 2.14938283
20 day(s) R2 : -12.91877110
20 day(s) Pearson r : -0.06953127
20 day(s) QLIKE : 0.04257040
full horizon MAE : 0.93656564
full horizon RMSE : 2.14938283
full horizon R2 : -12.91877110
full horizon Pearson r : -0.06953127
full horizon QLIKE : 0.04257040
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GOLD/Custom_KAN_H20.pkl
Saved y_true min=0.00380168, max=7.43162
Saved y_pred min=0.0122381, max=5.42892
=== GOLD | H=20 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 5534
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (3984, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 17.63876776611196
Min value: -0.5790835075307484
Checking X_time_train_core:
Shape: (3984, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.678060596158252
Checking y_train_core (log_mse scaled):
Shape: (3984, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.254162049886622
Min value: -5.257736685381158
Checking X_price_val:
Shape: (443, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 11.324782722398346
Min value: -0.5755730380699846
Checking X_time_val:
Shape: (443, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_val (log_mse scaled):
Shape: (443, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.7933331106383543
Min value: -5.155382782210931
Checking X_price_test:
Shape: (1107, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 6.413509896698947
Min value: -0.5768892216495461
Checking X_time_test:
Shape: (1107, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.869694456765018
Min value: -1.4237179058716
Checking y_test (log_mse scaled):
Shape: (1107, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 1.5432591469134986
Min value: -5.155382782210931
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 195s 458ms/step - loss: 0.5464 - val_loss: 0.5462 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 63s 503ms/step - loss: 0.3717 - val_loss: 0.5982 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 62s 499ms/step - loss: 0.3572 - val_loss: 0.8529 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 59s 470ms/step - loss: 0.3443 - val_loss: 0.5765 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 60s 477ms/step - loss: 0.3287 - val_loss: 0.5163 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 63s 504ms/step - loss: 0.3148 - val_loss: 0.5989 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 64s 512ms/step - loss: 0.3023 - val_loss: 0.5001 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 61s 488ms/step - loss: 0.2947 - val_loss: 0.5861 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 61s 491ms/step - loss: 0.2837 - val_loss: 0.5024 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 66s 524ms/step - loss: 0.2707 - val_loss: 1.0611 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=11 tf_ratio=0.474 -> TF=ON
Epoch 11/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 61s 489ms/step - loss: 0.2646 - val_loss: 0.5425 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=12 tf_ratio=0.421 -> TF=ON
Epoch 12/20
125/125 ━━━━━━━━━━━━━━━━━━━━ 0s 472ms/step - loss: 0.2530
Epoch 12: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
125/125 ━━━━━━━━━━━━━━━━━━━━ 61s 486ms/step - loss: 0.2575 - val_loss: 0.5422 - learning_rate: 5.0000e-04
Epoch 12: early stopping
Restoring model weights from the end of the best epoch: 7.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 20
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.40302157
1 day(s) RMSE : 0.69898184
1 day(s) R2 : -0.13770927
1 day(s) Pearson r : 0.43282981
1 day(s) QLIKE : 0.53211307
3 day(s) MAE : 0.40656036
3 day(s) RMSE : 0.70161632
3 day(s) R2 : -0.14534434
3 day(s) Pearson r : 0.07533105
3 day(s) QLIKE : 0.53690361
5 day(s) MAE : 0.40477713
5 day(s) RMSE : 0.70034480
5 day(s) R2 : -0.13795005
5 day(s) Pearson r : 0.06461583
5 day(s) QLIKE : 0.53718241
10 day(s) MAE : 0.41553954
10 day(s) RMSE : 0.69186777
10 day(s) R2 : -0.09818125
10 day(s) Pearson r : 0.00143268
10 day(s) QLIKE : 0.57002434
20 day(s) MAE : 0.42617477
20 day(s) RMSE : 0.69087198
20 day(s) R2 : -0.07886021
20 day(s) Pearson r : 0.00828061
20 day(s) QLIKE : 0.58142114
full horizon MAE : 0.42617477
full horizon RMSE : 0.69087198
full horizon R2 : -0.07886021
full horizon Pearson r : 0.00828061
full horizon QLIKE : 0.58142114
--- Task 2 ---
1 day(s) MAE : 0.04022105
1 day(s) RMSE : 0.06100197
1 day(s) R2 : -0.76885995
1 day(s) Pearson r : 0.10445735
1 day(s) QLIKE : 7.86752618
3 day(s) MAE : 0.04026904
3 day(s) RMSE : 0.06102399
3 day(s) R2 : -0.77126350
3 day(s) Pearson r : 0.01935441
3 day(s) QLIKE : 13.01171333
5 day(s) MAE : 0.04031677
5 day(s) RMSE : 0.06104488
5 day(s) R2 : -0.77359762
5 day(s) Pearson r : 0.01332185
5 day(s) QLIKE : 10.96544809
10 day(s) MAE : 0.04035343
10 day(s) RMSE : 0.06103299
10 day(s) R2 : -0.77665520
10 day(s) Pearson r : 0.00868213
10 day(s) QLIKE : 9.48994061
20 day(s) MAE : 0.04026935
20 day(s) RMSE : 0.06088501
20 day(s) R2 : -0.77760988
20 day(s) Pearson r : 0.00641044
20 day(s) QLIKE : 8.69438089
full horizon MAE : 0.04026935
full horizon RMSE : 0.06088501
full horizon R2 : -0.77760988
full horizon Pearson r : 0.00641044
full horizon QLIKE : 8.69438089
--- Task 3 ---
1 day(s) MAE : 0.56906231
1 day(s) RMSE : 0.58352451
1 day(s) R2 : -2.25414247
1 day(s) Pearson r : -0.02915053
1 day(s) QLIKE : 3.46604667
3 day(s) MAE : 0.72214881
3 day(s) RMSE : 0.78005114
3 day(s) R2 : -4.81638789
3 day(s) Pearson r : -0.00023292
3 day(s) QLIKE : 9.36959796
5 day(s) MAE : 0.75278398
5 day(s) RMSE : 0.81370180
5 day(s) R2 : -5.32996108
5 day(s) Pearson r : -0.00012834
5 day(s) QLIKE : 8.14960635
10 day(s) MAE : 0.77656840
10 day(s) RMSE : 0.83847383
10 day(s) R2 : -5.75899250
10 day(s) Pearson r : -0.00093015
10 day(s) QLIKE : 7.65167515
20 day(s) MAE : 0.79133054
20 day(s) RMSE : 0.85212259
20 day(s) R2 : -6.13822471
20 day(s) Pearson r : -0.00299377
20 day(s) QLIKE : 6.76924796
full horizon MAE : 0.79133054
full horizon RMSE : 0.85212259
full horizon R2 : -6.13822471
full horizon Pearson r : -0.00299377
full horizon QLIKE : 6.76924796
--- Task 4 ---
1 day(s) MAE : 0.12226108
1 day(s) RMSE : 0.25688480
1 day(s) R2 : -0.28816994
1 day(s) Pearson r : -0.03961900
1 day(s) QLIKE : 1.87859748
3 day(s) MAE : 0.12345032
3 day(s) RMSE : 0.25757257
3 day(s) R2 : -0.29522254
3 day(s) Pearson r : -0.00003581
3 day(s) QLIKE : 1.93394145
5 day(s) MAE : 0.12404239
5 day(s) RMSE : 0.25790370
5 day(s) R2 : -0.29869109
5 day(s) Pearson r : -0.00133074
5 day(s) QLIKE : 1.95527426
10 day(s) MAE : 0.12410952
10 day(s) RMSE : 0.25729946
10 day(s) R2 : -0.30183511
10 day(s) Pearson r : -0.00591607
10 day(s) QLIKE : 2.00974190
20 day(s) MAE : 0.12166277
20 day(s) RMSE : 0.25325290
20 day(s) R2 : -0.29899460
20 day(s) Pearson r : -0.01046430
20 day(s) QLIKE : 1.96447427
full horizon MAE : 0.12166277
full horizon RMSE : 0.25325290
full horizon R2 : -0.29899460
full horizon Pearson r : -0.01046430
full horizon QLIKE : 1.96447427
--- Task 5 ---
1 day(s) MAE : 0.03985587
1 day(s) RMSE : 0.05989335
1 day(s) R2 : -0.78770086
1 day(s) Pearson r : 0.19599300
1 day(s) QLIKE : 5.83384583
3 day(s) MAE : 0.04001949
3 day(s) RMSE : 0.06011638
3 day(s) R2 : -0.79340873
3 day(s) Pearson r : 0.00144920
3 day(s) QLIKE : 9.42073126
5 day(s) MAE : 0.04012704
5 day(s) RMSE : 0.06027281
5 day(s) R2 : -0.79463420
5 day(s) Pearson r : -0.00005896
5 day(s) QLIKE : 13.97265199
10 day(s) MAE : 0.04036285
10 day(s) RMSE : 0.06062803
10 day(s) R2 : -0.79532266
10 day(s) Pearson r : -0.00176169
10 day(s) QLIKE : 10.47031878
20 day(s) MAE : 0.04081148
20 day(s) RMSE : 0.06130255
20 day(s) R2 : -0.79566000
20 day(s) Pearson r : -0.00350911
20 day(s) QLIKE : 8.45564084
full horizon MAE : 0.04081148
full horizon RMSE : 0.06130255
full horizon R2 : -0.79566000
full horizon Pearson r : -0.00350911
full horizon QLIKE : 8.45564084
--- Task 6 ---
1 day(s) MAE : 0.47788595
1 day(s) RMSE : 0.57921693
1 day(s) R2 : -0.04950723
1 day(s) Pearson r : -0.11242209
1 day(s) QLIKE : 0.01132132
3 day(s) MAE : 0.47485629
3 day(s) RMSE : 0.57824143
3 day(s) R2 : -0.04400219
3 day(s) Pearson r : -0.04973741
3 day(s) QLIKE : 0.01137736
5 day(s) MAE : 0.49140690
5 day(s) RMSE : 0.58987402
5 day(s) R2 : -0.08463227
5 day(s) Pearson r : -0.00326826
5 day(s) QLIKE : 0.01142511
10 day(s) MAE : 0.61359305
10 day(s) RMSE : 0.72670430
10 day(s) R2 : -0.63566442
10 day(s) Pearson r : 0.01150166
10 day(s) QLIKE : 0.01319750
20 day(s) MAE : 0.69386184
20 day(s) RMSE : 0.80717239
20 day(s) R2 : -0.96293506
20 day(s) Pearson r : 0.01112487
20 day(s) QLIKE : 0.01331264
full horizon MAE : 0.69386184
full horizon RMSE : 0.80717239
full horizon R2 : -0.96293506
full horizon Pearson r : 0.01112487
full horizon QLIKE : 0.01331264
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/GOLD/Custom_KAN_LSTM_H20.pkl
Saved y_true min=0.00380168, max=7.43162
Saved y_pred min=0.246585, max=0.993158
=== SP500 | H=1 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 3466
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2495, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.304044071790344
Min value: -0.29186160079947243
Checking X_time_train_core:
Shape: (2495, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -2.0363820510485593
Checking y_train_core (log_mse scaled):
Shape: (2495, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9996639755036183
Min value: -28.079833393572628
Checking X_price_val:
Shape: (277, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.189039765537116
Min value: -0.288808876061582
Checking X_time_val:
Shape: (277, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_val (log_mse scaled):
Shape: (277, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.544997163695437
Min value: -28.079833393572628
Checking X_price_test:
Shape: (694, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.65876861006644
Min value: -0.2909517549064528
Checking X_time_test:
Shape: (694, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_test (log_mse scaled):
Shape: (694, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.3795163284544945
Min value: -28.079833393572628
Epoch 001 | phase=1 | train_loss=4.9654 | val_main=1.238538
Epoch 002 | phase=1 | train_loss=4.8117 | val_main=1.237960
Epoch 003 | phase=1 | train_loss=4.7495 | val_main=1.237837
Epoch 004 | phase=1 | train_loss=4.7174 | val_main=1.237854
Epoch 005 | phase=1 | train_loss=4.6766 | val_main=1.237792
Epoch 006 | phase=1 | train_loss=4.6357 | val_main=1.237554
Epoch 007 | phase=1 | train_loss=4.6698 | val_main=1.237261
Epoch 008 | phase=1 | train_loss=4.6188 | val_main=1.237086
Epoch 009 | phase=1 | train_loss=4.5474 | val_main=1.236509
Epoch 010 | phase=1 | train_loss=4.5369 | val_main=1.236712
Epoch 011 | phase=1 | train_loss=4.3913 | val_main=1.237028
Epoch 012 | phase=1 | train_loss=4.2929 | val_main=1.237298
Epoch 013 | phase=1 | train_loss=4.1470 | val_main=1.237204
Epoch 014 | phase=1 | train_loss=4.0238 | val_main=1.236947
Epoch 015 | phase=1 | train_loss=3.8078 | val_main=1.236921
Epoch 016 | phase=0 | train_loss=4.7641 | val_main=1.050058
Epoch 017 | phase=0 | train_loss=4.3869 | val_main=0.757068
Epoch 018 | phase=0 | train_loss=4.3681 | val_main=0.654863
Epoch 019 | phase=0 | train_loss=3.9578 | val_main=0.553593
Epoch 020 | phase=0 | train_loss=3.6922 | val_main=0.499560
Epoch 021 | phase=0 | train_loss=3.6527 | val_main=0.489510
Epoch 022 | phase=0 | train_loss=3.6307 | val_main=0.489011
Epoch 023 | phase=0 | train_loss=3.5573 | val_main=0.484937
Epoch 024 | phase=0 | train_loss=3.4973 | val_main=0.499002
Epoch 025 | phase=0 | train_loss=3.4025 | val_main=0.502311
Epoch 026 | phase=0 | train_loss=3.3033 | val_main=0.513120
Epoch 027 | phase=0 | train_loss=3.2074 | val_main=0.502723
Epoch 028 | phase=0 | train_loss=3.2644 | val_main=0.501087
Epoch 029 | phase=0 | train_loss=3.2862 | val_main=0.508420
Epoch 030 | phase=0 | train_loss=3.1255 | val_main=0.525873
Epoch 031 | phase=2 | train_loss=0.3966 | val_main=0.519985
Epoch 032 | phase=2 | train_loss=0.3883 | val_main=0.528568
Epoch 033 | phase=2 | train_loss=0.3845 | val_main=0.523267
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 6
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.48477420
1 day(s) RMSE : 2.24711640
1 day(s) R2 : 0.37025505
1 day(s) Pearson r : 0.65268579
1 day(s) QLIKE : 0.44364619
full horizon MAE : 0.48477420
full horizon RMSE : 2.24711640
full horizon R2 : 0.37025505
full horizon Pearson r : 0.65268579
full horizon QLIKE : 0.44364619
--- Task 2 ---
1 day(s) MAE : 0.07727403
1 day(s) RMSE : 0.08826825
1 day(s) R2 : -1.63901666
1 day(s) Pearson r : 0.23614900
1 day(s) QLIKE : 7.25172692
full horizon MAE : 0.07727403
full horizon RMSE : 0.08826825
full horizon R2 : -1.63901666
full horizon Pearson r : 0.23614900
full horizon QLIKE : 7.25172692
--- Task 3 ---
1 day(s) MAE : 0.31877575
1 day(s) RMSE : 0.43687147
1 day(s) R2 : -46.46592831
1 day(s) Pearson r : -0.01832788
1 day(s) QLIKE : 0.77568128
full horizon MAE : 0.31877575
full horizon RMSE : 0.43687147
full horizon R2 : -46.46592831
full horizon Pearson r : -0.01832788
full horizon QLIKE : 0.77568128
--- Task 4 ---
1 day(s) MAE : 0.03229071
1 day(s) RMSE : 0.03899003
1 day(s) R2 : -1.90176847
1 day(s) Pearson r : -0.12596547
1 day(s) QLIKE : 1.19703047
full horizon MAE : 0.03229071
full horizon RMSE : 0.03899003
full horizon R2 : -1.90176847
full horizon Pearson r : -0.12596547
full horizon QLIKE : 1.19703047
--- Task 5 ---
1 day(s) MAE : 0.02640609
1 day(s) RMSE : 0.03439154
1 day(s) R2 : -0.22000324
1 day(s) Pearson r : 0.18096763
1 day(s) QLIKE : 0.42612345
full horizon MAE : 0.02640609
full horizon RMSE : 0.03439154
full horizon R2 : -0.22000324
full horizon Pearson r : 0.18096763
full horizon QLIKE : 0.42612345
--- Task 6 ---
1 day(s) MAE : 2.67525934
1 day(s) RMSE : 3.29786144
1 day(s) R2 : -1.54937039
1 day(s) Pearson r : 0.06064200
1 day(s) QLIKE : 0.08644298
full horizon MAE : 2.67525934
full horizon RMSE : 3.29786144
full horizon R2 : -1.54937039
full horizon Pearson r : 0.06064200
full horizon QLIKE : 0.08644298
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/SP500/Custom_KAN_H1.pkl
Saved y_true min=0.00323949, max=53.2315
Saved y_pred min=0.0294916, max=15.0328
=== SP500 | H=1 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 3466
Time steps for y: 1
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2495, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.304044071790344
Min value: -0.29186160079947243
Checking X_time_train_core:
Shape: (2495, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -2.0363820510485593
Checking y_train_core (log_mse scaled):
Shape: (2495, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.9996639755036183
Min value: -28.079833393572628
Checking X_price_val:
Shape: (277, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.189039765537116
Min value: -0.288808876061582
Checking X_time_val:
Shape: (277, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_val (log_mse scaled):
Shape: (277, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.544997163695437
Min value: -28.079833393572628
Checking X_price_test:
Shape: (694, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.65876861006644
Min value: -0.2909517549064528
Checking X_time_test:
Shape: (694, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_test (log_mse scaled):
Shape: (694, 1, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.3795163284544945
Min value: -28.079833393572628
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 176s 338ms/step - loss: 1.0036 - val_loss: 1.1506 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 15s 189ms/step - loss: 0.9641 - val_loss: 0.8365 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 16s 205ms/step - loss: 0.9574 - val_loss: 0.8473 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 15s 194ms/step - loss: 0.9445 - val_loss: 0.8151 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 15s 192ms/step - loss: 0.9466 - val_loss: 0.7790 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 15s 195ms/step - loss: 0.9428 - val_loss: 0.8176 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 16s 201ms/step - loss: 0.9382 - val_loss: 0.8526 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 17s 212ms/step - loss: 0.9381 - val_loss: 0.8040 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 18s 226ms/step - loss: 0.9324 - val_loss: 0.8013 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 0s 225ms/step - loss: 0.9979
Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
78/78 ━━━━━━━━━━━━━━━━━━━━ 18s 232ms/step - loss: 0.9331 - val_loss: 0.8049 - learning_rate: 5.0000e-04
Epoch 10: early stopping
Restoring model weights from the end of the best epoch: 5.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 1
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.58192507
1 day(s) RMSE : 2.71371725
1 day(s) R2 : 0.08157701
1 day(s) Pearson r : 0.47143322
1 day(s) QLIKE : 0.61688294
full horizon MAE : 0.58192507
full horizon RMSE : 2.71371725
full horizon R2 : 0.08157701
full horizon Pearson r : 0.47143322
full horizon QLIKE : 0.61688294
--- Task 2 ---
1 day(s) MAE : 0.03507331
1 day(s) RMSE : 0.04995514
1 day(s) R2 : 0.15473384
1 day(s) Pearson r : 0.54353732
1 day(s) QLIKE : 2.53169985
full horizon MAE : 0.03507331
full horizon RMSE : 0.04995514
full horizon R2 : 0.15473384
full horizon Pearson r : 0.54353732
full horizon QLIKE : 2.53169985
--- Task 3 ---
1 day(s) MAE : 0.38799865
1 day(s) RMSE : 0.40280362
1 day(s) R2 : -39.35165332
1 day(s) Pearson r : -0.36921924
1 day(s) QLIKE : 0.01117270
full horizon MAE : 0.38799865
full horizon RMSE : 0.40280362
full horizon R2 : -39.35165332
full horizon Pearson r : -0.36921924
full horizon QLIKE : 0.01117270
--- Task 4 ---
1 day(s) MAE : 0.02304827
1 day(s) RMSE : 0.02633547
1 day(s) R2 : -0.32384720
1 day(s) Pearson r : -0.38899153
1 day(s) QLIKE : 1.13479390
full horizon MAE : 0.02304827
full horizon RMSE : 0.02633547
full horizon R2 : -0.32384720
full horizon Pearson r : -0.38899153
full horizon QLIKE : 1.13479390
--- Task 5 ---
1 day(s) MAE : 0.02530964
1 day(s) RMSE : 0.03152965
1 day(s) R2 : -0.02540674
1 day(s) Pearson r : -0.10536220
1 day(s) QLIKE : 0.43159889
full horizon MAE : 0.02530964
full horizon RMSE : 0.03152965
full horizon R2 : -0.02540674
full horizon Pearson r : -0.10536220
full horizon QLIKE : 0.43159889
--- Task 6 ---
1 day(s) MAE : 2.21325842
1 day(s) RMSE : 2.73766994
1 day(s) R2 : -0.75683229
1 day(s) Pearson r : -0.34709459
1 day(s) QLIKE : 0.06320649
full horizon MAE : 2.21325842
full horizon RMSE : 2.73766994
full horizon R2 : -0.75683229
full horizon Pearson r : -0.34709459
full horizon QLIKE : 0.06320649
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/SP500/Custom_KAN_LSTM_H1.pkl
Saved y_true min=0.00323949, max=53.2315
Saved y_pred min=0.197013, max=2.03264
=== SP500 | H=5 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 3466
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2495, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.304044071790344
Min value: -0.29186160079947243
Checking X_time_train_core:
Shape: (2495, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -2.0363820510485593
Checking y_train_core (log_mse scaled):
Shape: (2495, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.008465261938893
Min value: -28.084553036741752
Checking X_price_val:
Shape: (277, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.189039765537116
Min value: -0.288808876061582
Checking X_time_val:
Shape: (277, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_val (log_mse scaled):
Shape: (277, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5447500237882554
Min value: -28.084553036741752
Checking X_price_test:
Shape: (694, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.65876861006644
Min value: -0.2909517549064528
Checking X_time_test:
Shape: (694, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_test (log_mse scaled):
Shape: (694, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.3784015875360596
Min value: -28.084553036741752
Epoch 001 | phase=1 | train_loss=5.0084 | val_main=1.233486
Epoch 002 | phase=1 | train_loss=4.8260 | val_main=1.232634
Epoch 003 | phase=1 | train_loss=4.7092 | val_main=1.232522
Epoch 004 | phase=1 | train_loss=4.6919 | val_main=1.232620
Epoch 005 | phase=1 | train_loss=4.6401 | val_main=1.232290
Epoch 006 | phase=1 | train_loss=4.6303 | val_main=1.232328
Epoch 007 | phase=1 | train_loss=4.7869 | val_main=1.232030
Epoch 008 | phase=1 | train_loss=4.5370 | val_main=1.232185
Epoch 009 | phase=1 | train_loss=4.5850 | val_main=1.232516
Epoch 010 | phase=1 | train_loss=4.4779 | val_main=1.232668
Epoch 011 | phase=1 | train_loss=4.4559 | val_main=1.232618
Epoch 012 | phase=1 | train_loss=4.2938 | val_main=1.232922
Epoch 013 | phase=1 | train_loss=4.1667 | val_main=1.232609
Epoch 014 | phase=1 | train_loss=4.0803 | val_main=1.232663
Epoch 015 | phase=1 | train_loss=3.9149 | val_main=1.232737
Epoch 016 | phase=0 | train_loss=4.7678 | val_main=1.150310
Epoch 017 | phase=0 | train_loss=4.4972 | val_main=0.811819
Epoch 018 | phase=0 | train_loss=4.4235 | val_main=0.792201
Epoch 019 | phase=0 | train_loss=4.2295 | val_main=0.757253
Epoch 020 | phase=0 | train_loss=4.2049 | val_main=0.756392
Epoch 021 | phase=0 | train_loss=4.0580 | val_main=0.773481
Epoch 022 | phase=0 | train_loss=4.0221 | val_main=0.759606
Epoch 023 | phase=0 | train_loss=4.0701 | val_main=0.747288
Epoch 024 | phase=0 | train_loss=3.9950 | val_main=0.754625
Epoch 025 | phase=0 | train_loss=3.8772 | val_main=0.751282
Epoch 026 | phase=0 | train_loss=3.8398 | val_main=0.767459
Epoch 027 | phase=0 | train_loss=3.6902 | val_main=0.770717
Epoch 028 | phase=0 | train_loss=3.7262 | val_main=0.773292
Epoch 029 | phase=0 | train_loss=3.8376 | val_main=0.773843
Epoch 030 | phase=0 | train_loss=3.8188 | val_main=0.767685
Epoch 031 | phase=2 | train_loss=0.6243 | val_main=0.778505
Epoch 032 | phase=2 | train_loss=0.6441 | val_main=0.780467
Epoch 033 | phase=2 | train_loss=0.6194 | val_main=0.793580
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 30
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.55973660
1 day(s) RMSE : 2.31164393
1 day(s) R2 : 0.33356863
1 day(s) Pearson r : 0.58162315
1 day(s) QLIKE : 0.58671698
3 day(s) MAE : 0.60524988
3 day(s) RMSE : 2.58292200
3 day(s) R2 : 0.16801567
3 day(s) Pearson r : 0.46160456
3 day(s) QLIKE : 0.61852362
5 day(s) MAE : 0.65075375
5 day(s) RMSE : 2.80371312
5 day(s) R2 : 0.01975456
5 day(s) Pearson r : 0.34426963
5 day(s) QLIKE : 0.66975733
full horizon MAE : 0.65075375
full horizon RMSE : 2.80371312
full horizon R2 : 0.01975456
full horizon Pearson r : 0.34426963
full horizon QLIKE : 0.66975733
--- Task 2 ---
1 day(s) MAE : 0.07309016
1 day(s) RMSE : 0.09658281
1 day(s) R2 : -2.15960504
1 day(s) Pearson r : 0.43712608
1 day(s) QLIKE : 7.34765287
3 day(s) MAE : 0.06737480
3 day(s) RMSE : 0.08394057
3 day(s) R2 : -1.35449774
3 day(s) Pearson r : 0.39061380
3 day(s) QLIKE : 7.32992279
5 day(s) MAE : 0.06812769
5 day(s) RMSE : 0.08220082
5 day(s) R2 : -1.22671924
5 day(s) Pearson r : 0.37134927
5 day(s) QLIKE : 7.40468125
full horizon MAE : 0.06812769
full horizon RMSE : 0.08220082
full horizon R2 : -1.22671924
full horizon Pearson r : 0.37134927
full horizon QLIKE : 7.40468125
--- Task 3 ---
1 day(s) MAE : 0.30920290
1 day(s) RMSE : 0.42250405
1 day(s) R2 : -43.39523618
1 day(s) Pearson r : -0.03193439
1 day(s) QLIKE : 0.65939620
3 day(s) MAE : 0.31146694
3 day(s) RMSE : 0.42118327
3 day(s) R2 : -42.82677786
3 day(s) Pearson r : -0.04145575
3 day(s) QLIKE : 0.63096659
5 day(s) MAE : 0.30963711
5 day(s) RMSE : 0.42039191
5 day(s) R2 : -42.40595594
5 day(s) Pearson r : -0.03879923
5 day(s) QLIKE : 0.61863716
full horizon MAE : 0.30963711
full horizon RMSE : 0.42039191
full horizon R2 : -42.40595594
full horizon Pearson r : -0.03879923
full horizon QLIKE : 0.61863716
--- Task 4 ---
1 day(s) MAE : 0.03296584
1 day(s) RMSE : 0.04022714
1 day(s) R2 : -2.08883055
1 day(s) Pearson r : -0.13481319
1 day(s) QLIKE : 1.19691988
3 day(s) MAE : 0.03286721
3 day(s) RMSE : 0.04013032
3 day(s) R2 : -2.06133311
3 day(s) Pearson r : -0.12727319
3 day(s) QLIKE : 1.19564929
5 day(s) MAE : 0.03276573
5 day(s) RMSE : 0.03987454
5 day(s) R2 : -2.01012181
5 day(s) Pearson r : -0.12340933
5 day(s) QLIKE : 1.19530503
full horizon MAE : 0.03276573
full horizon RMSE : 0.03987454
full horizon R2 : -2.01012181
full horizon Pearson r : -0.12340933
full horizon QLIKE : 1.19530503
--- Task 5 ---
1 day(s) MAE : 0.02690290
1 day(s) RMSE : 0.03447128
1 day(s) R2 : -0.22566703
1 day(s) Pearson r : 0.12024437
1 day(s) QLIKE : 0.43668273
3 day(s) MAE : 0.02675343
3 day(s) RMSE : 0.03434439
3 day(s) R2 : -0.22311717
3 day(s) Pearson r : 0.12410513
3 day(s) QLIKE : 0.43436728
5 day(s) MAE : 0.02651796
5 day(s) RMSE : 0.03401249
5 day(s) R2 : -0.20631745
5 day(s) Pearson r : 0.13754611
5 day(s) QLIKE : 0.42616766
full horizon MAE : 0.02651796
full horizon RMSE : 0.03401249
full horizon R2 : -0.20631745
full horizon Pearson r : 0.13754611
full horizon QLIKE : 0.42616766
--- Task 6 ---
1 day(s) MAE : 2.68266943
1 day(s) RMSE : 3.32710080
1 day(s) R2 : -1.59477703
1 day(s) Pearson r : 0.04528192
1 day(s) QLIKE : 0.08761525
3 day(s) MAE : 2.67464825
3 day(s) RMSE : 3.31876202
3 day(s) R2 : -1.57502267
3 day(s) Pearson r : 0.05084459
3 day(s) QLIKE : 0.08708968
5 day(s) MAE : 2.66384731
5 day(s) RMSE : 3.30359605
5 day(s) R2 : -1.54398642
5 day(s) Pearson r : 0.05614479
5 day(s) QLIKE : 0.08647860
full horizon MAE : 2.66384731
full horizon RMSE : 3.30359605
full horizon R2 : -1.54398642
full horizon Pearson r : 0.05614479
full horizon QLIKE : 0.08647860
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/SP500/Custom_KAN_H5.pkl
Saved y_true min=0.00323949, max=53.2315
Saved y_pred min=0.143876, max=30.4168
=== SP500 | H=5 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 3466
Time steps for y: 5
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2495, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.304044071790344
Min value: -0.29186160079947243
Checking X_time_train_core:
Shape: (2495, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -2.0363820510485593
Checking y_train_core (log_mse scaled):
Shape: (2495, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.008465261938893
Min value: -28.084553036741752
Checking X_price_val:
Shape: (277, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.189039765537116
Min value: -0.288808876061582
Checking X_time_val:
Shape: (277, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_val (log_mse scaled):
Shape: (277, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5447500237882554
Min value: -28.084553036741752
Checking X_price_test:
Shape: (694, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.65876861006644
Min value: -0.2909517549064528
Checking X_time_test:
Shape: (694, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_test (log_mse scaled):
Shape: (694, 5, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.3784015875360596
Min value: -28.084553036741752
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 172s 366ms/step - loss: 0.8092 - val_loss: 1.1127 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 19s 240ms/step - loss: 0.6023 - val_loss: 1.3889 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 20s 250ms/step - loss: 0.5600 - val_loss: 0.8720 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 20s 252ms/step - loss: 0.5377 - val_loss: 1.1287 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 19s 245ms/step - loss: 0.5257 - val_loss: 1.2308 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 19s 248ms/step - loss: 0.5191 - val_loss: 1.2710 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 21s 264ms/step - loss: 0.5094 - val_loss: 1.1616 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 0s 297ms/step - loss: 0.5826
Epoch 8: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
78/78 ━━━━━━━━━━━━━━━━━━━━ 24s 306ms/step - loss: 0.5027 - val_loss: 1.0760 - learning_rate: 5.0000e-04
Epoch 8: early stopping
Restoring model weights from the end of the best epoch: 3.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 5
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.61838397
1 day(s) RMSE : 2.80015597
1 day(s) R2 : 0.02213703
1 day(s) Pearson r : 0.34830508
1 day(s) QLIKE : 0.69574540
3 day(s) MAE : 0.63451976
3 day(s) RMSE : 2.77650504
3 day(s) R2 : 0.03863237
3 day(s) Pearson r : 0.29721195
3 day(s) QLIKE : 0.71631177
5 day(s) MAE : 0.64236976
5 day(s) RMSE : 2.80491641
5 day(s) R2 : 0.01891298
5 day(s) Pearson r : 0.23538775
5 day(s) QLIKE : 0.79835126
full horizon MAE : 0.64236976
full horizon RMSE : 2.80491641
full horizon R2 : 0.01891298
full horizon Pearson r : 0.23538775
full horizon QLIKE : 0.79835126
--- Task 2 ---
1 day(s) MAE : 0.03462909
1 day(s) RMSE : 0.05217154
1 day(s) R2 : 0.07806490
1 day(s) Pearson r : 0.47988074
1 day(s) QLIKE : 2.52537106
3 day(s) MAE : 0.04271142
3 day(s) RMSE : 0.06083719
3 day(s) R2 : -0.23678069
3 day(s) Pearson r : 0.40501661
3 day(s) QLIKE : 13.01419698
5 day(s) MAE : 0.04660316
5 day(s) RMSE : 0.06645819
5 day(s) R2 : -0.45549311
5 day(s) Pearson r : 0.34224219
5 day(s) QLIKE : 13.59849269
full horizon MAE : 0.04660316
full horizon RMSE : 0.06645819
full horizon R2 : -0.45549311
full horizon Pearson r : 0.34224219
full horizon QLIKE : 13.59849269
--- Task 3 ---
1 day(s) MAE : 0.33265610
1 day(s) RMSE : 0.33800832
1 day(s) R2 : -27.41380525
1 day(s) Pearson r : 0.39645476
1 day(s) QLIKE : 0.00218268
3 day(s) MAE : 0.24035282
3 day(s) RMSE : 0.25940490
3 day(s) R2 : -15.62469461
3 day(s) Pearson r : 0.04353049
3 day(s) QLIKE : 0.00916784
5 day(s) MAE : 0.20006462
5 day(s) RMSE : 0.22404465
5 day(s) R2 : -11.32848696
5 day(s) Pearson r : 0.01437848
5 day(s) QLIKE : 0.00995198
full horizon MAE : 0.20006462
full horizon RMSE : 0.22404465
full horizon R2 : -11.32848696
full horizon Pearson r : 0.01437848
full horizon QLIKE : 0.00995198
--- Task 4 ---
1 day(s) MAE : 0.02397391
1 day(s) RMSE : 0.02722318
1 day(s) R2 : -0.41459960
1 day(s) Pearson r : -0.35253496
1 day(s) QLIKE : 1.12740169
3 day(s) MAE : 0.02451031
3 day(s) RMSE : 0.02779148
3 day(s) R2 : -0.46821137
3 day(s) Pearson r : -0.32528868
3 day(s) QLIKE : 1.13022937
5 day(s) MAE : 0.02475864
5 day(s) RMSE : 0.02808444
5 day(s) R2 : -0.49322126
5 day(s) Pearson r : -0.32459345
5 day(s) QLIKE : 1.13483404
full horizon MAE : 0.02475864
full horizon RMSE : 0.02808444
full horizon R2 : -0.49322126
full horizon Pearson r : -0.32459345
full horizon QLIKE : 1.13483404
--- Task 5 ---
1 day(s) MAE : 0.02495631
1 day(s) RMSE : 0.03139459
1 day(s) R2 : -0.01664071
1 day(s) Pearson r : -0.04930959
1 day(s) QLIKE : 0.43046855
3 day(s) MAE : 0.02462624
3 day(s) RMSE : 0.03227479
3 day(s) R2 : -0.08014818
3 day(s) Pearson r : -0.00681117
3 day(s) QLIKE : 0.43080153
5 day(s) MAE : 0.02475737
5 day(s) RMSE : 0.03260740
5 day(s) R2 : -0.10870783
5 day(s) Pearson r : 0.01607667
5 day(s) QLIKE : 0.42497477
full horizon MAE : 0.02475737
full horizon RMSE : 0.03260740
full horizon R2 : -0.10870783
full horizon Pearson r : 0.01607667
full horizon QLIKE : 0.42497477
--- Task 6 ---
1 day(s) MAE : 2.26736001
1 day(s) RMSE : 2.80601853
1 day(s) R2 : -0.84564937
1 day(s) Pearson r : -0.35878713
1 day(s) QLIKE : 0.06662895
3 day(s) MAE : 2.27352679
3 day(s) RMSE : 2.81497600
3 day(s) R2 : -0.85258535
3 day(s) Pearson r : -0.36183429
3 day(s) QLIKE : 0.06849299
5 day(s) MAE : 2.22348084
5 day(s) RMSE : 2.75212596
5 day(s) R2 : -0.76553986
5 day(s) Pearson r : -0.31884216
5 day(s) QLIKE : 0.06999864
full horizon MAE : 2.22348084
full horizon RMSE : 2.75212596
full horizon R2 : -0.76553986
full horizon Pearson r : -0.31884216
full horizon QLIKE : 0.06999864
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/SP500/Custom_KAN_LSTM_H5.pkl
Saved y_true min=0.00323949, max=53.2315
Saved y_pred min=0.206683, max=1.96678
=== SP500 | H=10 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 3466
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2495, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.304044071790344
Min value: -0.29186160079947243
Checking X_time_train_core:
Shape: (2495, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -2.0363820510485593
Checking y_train_core (log_mse scaled):
Shape: (2495, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.0210836022185426
Min value: -28.086926568760678
Checking X_price_val:
Shape: (277, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.189039765537116
Min value: -0.288808876061582
Checking X_time_val:
Shape: (277, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_val (log_mse scaled):
Shape: (277, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5448233279041013
Min value: -28.086926568760678
Checking X_price_test:
Shape: (694, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.65876861006644
Min value: -0.2909517549064528
Checking X_time_test:
Shape: (694, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_test (log_mse scaled):
Shape: (694, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.378408171115352
Min value: -28.086926568760678
Epoch 001 | phase=1 | train_loss=4.9876 | val_main=1.237347
Epoch 002 | phase=1 | train_loss=4.8772 | val_main=1.240204
Epoch 003 | phase=1 | train_loss=4.6951 | val_main=1.240515
Epoch 004 | phase=1 | train_loss=4.6930 | val_main=1.239956
Epoch 005 | phase=1 | train_loss=4.6363 | val_main=1.240195
Epoch 006 | phase=1 | train_loss=4.6144 | val_main=1.239709
Epoch 007 | phase=1 | train_loss=4.6767 | val_main=1.239914
Epoch 008 | phase=1 | train_loss=4.5785 | val_main=1.239517
Epoch 009 | phase=1 | train_loss=4.5858 | val_main=1.239067
Epoch 010 | phase=1 | train_loss=4.5431 | val_main=1.239375
Epoch 011 | phase=1 | train_loss=4.4298 | val_main=1.238829
Epoch 012 | phase=1 | train_loss=4.3143 | val_main=1.239291
Epoch 013 | phase=1 | train_loss=4.2033 | val_main=1.239293
Epoch 014 | phase=1 | train_loss=4.1124 | val_main=1.239239
Epoch 015 | phase=1 | train_loss=3.9859 | val_main=1.239225
Epoch 016 | phase=0 | train_loss=4.8497 | val_main=1.177064
Epoch 017 | phase=0 | train_loss=4.6467 | val_main=0.846582
Epoch 018 | phase=0 | train_loss=4.5237 | val_main=0.813672
Epoch 019 | phase=0 | train_loss=4.3079 | val_main=0.792672
Epoch 020 | phase=0 | train_loss=4.2476 | val_main=0.813801
Epoch 021 | phase=0 | train_loss=4.2010 | val_main=0.820133
Epoch 022 | phase=0 | train_loss=4.1302 | val_main=0.794638
Epoch 023 | phase=0 | train_loss=4.1710 | val_main=0.781230
Epoch 024 | phase=0 | train_loss=4.2295 | val_main=0.791165
Epoch 025 | phase=0 | train_loss=3.9827 | val_main=0.790353
Epoch 026 | phase=0 | train_loss=3.9379 | val_main=0.812462
Epoch 027 | phase=0 | train_loss=3.8927 | val_main=0.796465
Epoch 028 | phase=0 | train_loss=3.8676 | val_main=0.816236
Epoch 029 | phase=0 | train_loss=3.8813 | val_main=0.794464
Epoch 030 | phase=0 | train_loss=3.8625 | val_main=0.787984
Epoch 031 | phase=2 | train_loss=0.6758 | val_main=0.806552
Epoch 032 | phase=2 | train_loss=0.6829 | val_main=0.803852
Epoch 033 | phase=2 | train_loss=0.6704 | val_main=0.823174
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 60
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.60514302
1 day(s) RMSE : 2.53691621
1 day(s) R2 : 0.19735075
1 day(s) Pearson r : 0.60337156
1 day(s) QLIKE : 0.59027515
3 day(s) MAE : 0.64942608
3 day(s) RMSE : 2.78386890
3 day(s) R2 : 0.03352612
3 day(s) Pearson r : 0.48035070
3 day(s) QLIKE : 0.62673358
5 day(s) MAE : 0.69461693
5 day(s) RMSE : 3.01937531
5 day(s) R2 : -0.13684664
5 day(s) Pearson r : 0.35734688
5 day(s) QLIKE : 0.68329959
10 day(s) MAE : 0.72564175
10 day(s) RMSE : 3.10490138
10 day(s) R2 : -0.20200008
10 day(s) Pearson r : 0.23661691
10 day(s) QLIKE : 0.79233418
full horizon MAE : 0.72564175
full horizon RMSE : 3.10490138
full horizon R2 : -0.20200008
full horizon Pearson r : 0.23661691
full horizon QLIKE : 0.79233418
--- Task 2 ---
1 day(s) MAE : 0.06179473
1 day(s) RMSE : 0.09604238
1 day(s) R2 : -2.12434467
1 day(s) Pearson r : 0.42827435
1 day(s) QLIKE : 7.51829087
3 day(s) MAE : 0.05987612
3 day(s) RMSE : 0.07996760
3 day(s) R2 : -1.13689181
3 day(s) Pearson r : 0.39444335
3 day(s) QLIKE : 7.32785355
5 day(s) MAE : 0.06137555
5 day(s) RMSE : 0.08368315
5 day(s) R2 : -1.30775185
5 day(s) Pearson r : 0.40933917
5 day(s) QLIKE : 7.41225689
10 day(s) MAE : 0.06214279
10 day(s) RMSE : 0.08728484
10 day(s) R2 : -1.43066109
10 day(s) Pearson r : 0.39490579
10 day(s) QLIKE : 7.44993911
full horizon MAE : 0.06214279
full horizon RMSE : 0.08728484
full horizon R2 : -1.43066109
full horizon Pearson r : 0.39490579
full horizon QLIKE : 7.44993911
--- Task 3 ---
1 day(s) MAE : 0.32330032
1 day(s) RMSE : 0.43268948
1 day(s) R2 : -45.56153394
1 day(s) Pearson r : -0.02739129
1 day(s) QLIKE : 0.60854556
3 day(s) MAE : 0.32162142
3 day(s) RMSE : 0.43112690
3 day(s) R2 : -44.92060048
3 day(s) Pearson r : -0.02633203
3 day(s) QLIKE : 0.58763693
5 day(s) MAE : 0.31748260
5 day(s) RMSE : 0.42950320
5 day(s) R2 : -44.30784753
5 day(s) Pearson r : -0.02124632
5 day(s) QLIKE : 0.61592280
10 day(s) MAE : 0.32094808
10 day(s) RMSE : 0.43153178
10 day(s) R2 : -44.06502947
10 day(s) Pearson r : -0.02717462
10 day(s) QLIKE : 0.58260606
full horizon MAE : 0.32094808
full horizon RMSE : 0.43153178
full horizon R2 : -44.06502947
full horizon Pearson r : -0.02717462
full horizon QLIKE : 0.58260606
--- Task 4 ---
1 day(s) MAE : 0.03441974
1 day(s) RMSE : 0.04187620
1 day(s) R2 : -2.34726588
1 day(s) Pearson r : -0.14887234
1 day(s) QLIKE : 1.21700612
3 day(s) MAE : 0.03411475
3 day(s) RMSE : 0.04137530
3 day(s) R2 : -2.25422644
3 day(s) Pearson r : -0.14350647
3 day(s) QLIKE : 1.21457837
5 day(s) MAE : 0.03403407
5 day(s) RMSE : 0.04122295
5 day(s) R2 : -2.21714587
5 day(s) Pearson r : -0.13890462
5 day(s) QLIKE : 1.21527753
10 day(s) MAE : 0.03352133
10 day(s) RMSE : 0.04024982
10 day(s) R2 : -2.04296997
10 day(s) Pearson r : -0.13838299
10 day(s) QLIKE : 1.21476666
full horizon MAE : 0.03352133
full horizon RMSE : 0.04024982
full horizon R2 : -2.04296997
full horizon Pearson r : -0.13838299
full horizon QLIKE : 1.21476666
--- Task 5 ---
1 day(s) MAE : 0.02846146
1 day(s) RMSE : 0.03645775
1 day(s) R2 : -0.37100003
1 day(s) Pearson r : 0.03687558
1 day(s) QLIKE : 0.44595998
3 day(s) MAE : 0.02822321
3 day(s) RMSE : 0.03618645
3 day(s) R2 : -0.35783915
3 day(s) Pearson r : 0.04784905
3 day(s) QLIKE : 0.44280734
5 day(s) MAE : 0.02809971
5 day(s) RMSE : 0.03601158
5 day(s) R2 : -0.35228763
5 day(s) Pearson r : 0.05879996
5 day(s) QLIKE : 0.43555041
10 day(s) MAE : 0.02749074
10 day(s) RMSE : 0.03507871
10 day(s) R2 : -0.30245764
10 day(s) Pearson r : 0.10021518
10 day(s) QLIKE : 0.41013737
full horizon MAE : 0.02749074
full horizon RMSE : 0.03507871
full horizon R2 : -0.30245764
full horizon Pearson r : 0.10021518
full horizon QLIKE : 0.41013737
--- Task 6 ---
1 day(s) MAE : 2.72223086
1 day(s) RMSE : 3.34542469
1 day(s) R2 : -1.62343703
1 day(s) Pearson r : 0.04451103
1 day(s) QLIKE : 0.08650612
3 day(s) MAE : 2.71703984
3 day(s) RMSE : 3.34414439
3 day(s) R2 : -1.61456157
3 day(s) Pearson r : 0.04646716
3 day(s) QLIKE : 0.08622608
5 day(s) MAE : 2.71302989
5 day(s) RMSE : 3.34082510
5 day(s) R2 : -1.60164713
5 day(s) Pearson r : 0.04906290
5 day(s) QLIKE : 0.08597071
10 day(s) MAE : 2.69763205
10 day(s) RMSE : 3.32550123
10 day(s) R2 : -1.55938825
10 day(s) Pearson r : 0.05595325
10 day(s) QLIKE : 0.08585372
full horizon MAE : 2.69763205
full horizon RMSE : 3.32550123
full horizon R2 : -1.55938825
full horizon Pearson r : 0.05595325
full horizon QLIKE : 0.08585372
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/SP500/Custom_KAN_H10.pkl
Saved y_true min=0.00323949, max=53.2315
Saved y_pred min=0.143027, max=43.9985
=== SP500 | H=10 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 3466
Time steps for y: 10
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2495, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.304044071790344
Min value: -0.29186160079947243
Checking X_time_train_core:
Shape: (2495, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -2.0363820510485593
Checking y_train_core (log_mse scaled):
Shape: (2495, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.0210836022185426
Min value: -28.086926568760678
Checking X_price_val:
Shape: (277, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.189039765537116
Min value: -0.288808876061582
Checking X_time_val:
Shape: (277, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_val (log_mse scaled):
Shape: (277, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.5448233279041013
Min value: -28.086926568760678
Checking X_price_test:
Shape: (694, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.65876861006644
Min value: -0.2909517549064528
Checking X_time_test:
Shape: (694, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_test (log_mse scaled):
Shape: (694, 10, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.378408171115352
Min value: -28.086926568760678
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 432s 471ms/step - loss: 0.7710 - val_loss: 1.2044 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 24s 305ms/step - loss: 0.5429 - val_loss: 1.5592 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 25s 319ms/step - loss: 0.4981 - val_loss: 1.6962 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 25s 319ms/step - loss: 0.4771 - val_loss: 1.6385 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 25s 325ms/step - loss: 0.4611 - val_loss: 0.9110 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 25s 319ms/step - loss: 0.4464 - val_loss: 1.1856 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=7 tf_ratio=0.684 -> TF=ON
Epoch 7/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 25s 317ms/step - loss: 0.4310 - val_loss: 1.4433 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=8 tf_ratio=0.632 -> TF=ON
Epoch 8/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 25s 322ms/step - loss: 0.4215 - val_loss: 1.4807 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=9 tf_ratio=0.579 -> TF=ON
Epoch 9/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 25s 326ms/step - loss: 0.4120 - val_loss: 1.6645 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=10 tf_ratio=0.526 -> TF=ON
Epoch 10/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 0s 317ms/step - loss: 0.4511
Epoch 10: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
78/78 ━━━━━━━━━━━━━━━━━━━━ 25s 326ms/step - loss: 0.4045 - val_loss: 1.6772 - learning_rate: 5.0000e-04
Epoch 10: early stopping
Restoring model weights from the end of the best epoch: 5.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 10
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.60700916
1 day(s) RMSE : 2.79798834
1 day(s) R2 : 0.02365039
1 day(s) Pearson r : 0.37939576
1 day(s) QLIKE : 0.71580521
3 day(s) MAE : 0.62331469
3 day(s) RMSE : 2.77255844
3 day(s) R2 : 0.04136346
3 day(s) Pearson r : 0.31061098
3 day(s) QLIKE : 0.69649454
5 day(s) MAE : 0.63509430
5 day(s) RMSE : 2.79528126
5 day(s) R2 : 0.02564165
5 day(s) Pearson r : 0.24254665
5 day(s) QLIKE : 0.79024379
10 day(s) MAE : 0.65852162
10 day(s) RMSE : 2.81815803
10 day(s) R2 : 0.00976207
10 day(s) Pearson r : 0.17296666
10 day(s) QLIKE : 0.90315859
full horizon MAE : 0.65852162
full horizon RMSE : 2.81815803
full horizon R2 : 0.00976207
full horizon Pearson r : 0.17296666
full horizon QLIKE : 0.90315859
--- Task 2 ---
1 day(s) MAE : 0.05699826
1 day(s) RMSE : 0.06628169
1 day(s) R2 : -0.48805936
1 day(s) Pearson r : 0.51870784
1 day(s) QLIKE : 2.53048542
3 day(s) MAE : 0.11230046
3 day(s) RMSE : 0.17005702
3 day(s) R2 : -8.66370278
3 day(s) Pearson r : 0.45199457
3 day(s) QLIKE : 2.66981245
5 day(s) MAE : 0.24504568
5 day(s) RMSE : 0.40559282
5 day(s) R2 : -53.21177575
5 day(s) Pearson r : 0.34498303
5 day(s) QLIKE : 19.30819638
10 day(s) MAE : 0.53629146
10 day(s) RMSE : 0.73390532
10 day(s) R2 : -170.84103215
10 day(s) Pearson r : 0.27743703
10 day(s) QLIKE : 22.80976568
full horizon MAE : 0.53629146
full horizon RMSE : 0.73390532
full horizon R2 : -170.84103215
full horizon Pearson r : 0.27743703
full horizon QLIKE : 22.80976568
--- Task 3 ---
1 day(s) MAE : 0.42271674
1 day(s) RMSE : 0.42801818
1 day(s) R2 : -44.56160832
1 day(s) Pearson r : -0.24347634
1 day(s) QLIKE : 0.00324553
3 day(s) MAE : 0.58627944
3 day(s) RMSE : 0.61153002
3 day(s) R2 : -91.39168217
3 day(s) Pearson r : -0.09950380
3 day(s) QLIKE : 0.34016505
5 day(s) MAE : 0.70331807
5 day(s) RMSE : 0.73294372
5 day(s) R2 : -130.94163914
5 day(s) Pearson r : -0.07290945
5 day(s) QLIKE : 4.41346445
10 day(s) MAE : 0.80162733
10 day(s) RMSE : 0.82200562
10 day(s) R2 : -162.51728084
10 day(s) Pearson r : -0.03690136
10 day(s) QLIKE : 2.88104607
full horizon MAE : 0.80162733
full horizon RMSE : 0.82200562
full horizon R2 : -162.51728084
full horizon Pearson r : -0.03690136
full horizon QLIKE : 2.88104607
--- Task 4 ---
1 day(s) MAE : 0.02475781
1 day(s) RMSE : 0.02806276
1 day(s) R2 : -0.50319937
1 day(s) Pearson r : -0.42297115
1 day(s) QLIKE : 1.11836805
3 day(s) MAE : 0.02704340
3 day(s) RMSE : 0.03102487
3 day(s) R2 : -0.82972284
3 day(s) Pearson r : -0.33722005
3 day(s) QLIKE : 1.14472680
5 day(s) MAE : 0.02622032
5 day(s) RMSE : 0.03007121
5 day(s) R2 : -0.71196384
5 day(s) Pearson r : -0.31834248
5 day(s) QLIKE : 1.15182674
10 day(s) MAE : 0.02607916
10 day(s) RMSE : 0.03000280
10 day(s) R2 : -0.69080389
10 day(s) Pearson r : -0.32839189
10 day(s) QLIKE : 1.18569723
full horizon MAE : 0.02607916
full horizon RMSE : 0.03000280
full horizon R2 : -0.69080389
full horizon Pearson r : -0.32839189
full horizon QLIKE : 1.18569723
--- Task 5 ---
1 day(s) MAE : 0.02484768
1 day(s) RMSE : 0.03131470
1 day(s) R2 : -0.01147316
1 day(s) Pearson r : -0.01023164
1 day(s) QLIKE : 0.42988841
3 day(s) MAE : 0.02534233
3 day(s) RMSE : 0.03127813
3 day(s) R2 : -0.01446685
3 day(s) Pearson r : -0.00493144
3 day(s) QLIKE : 0.42810785
5 day(s) MAE : 0.02549079
5 day(s) RMSE : 0.03124765
5 day(s) R2 : -0.01816780
5 day(s) Pearson r : -0.00588288
5 day(s) QLIKE : 0.42212264
10 day(s) MAE : 0.02535411
10 day(s) RMSE : 0.03101775
10 day(s) R2 : -0.01834996
10 day(s) Pearson r : -0.01215673
10 day(s) QLIKE : 0.40040041
full horizon MAE : 0.02535411
full horizon RMSE : 0.03101775
full horizon R2 : -0.01834996
full horizon Pearson r : -0.01215673
full horizon QLIKE : 0.40040041
--- Task 6 ---
1 day(s) MAE : 2.26237262
1 day(s) RMSE : 2.79642159
1 day(s) R2 : -0.83304624
1 day(s) Pearson r : -0.37024257
1 day(s) QLIKE : 0.06212047
3 day(s) MAE : 2.29409446
3 day(s) RMSE : 2.83541904
3 day(s) R2 : -0.87959091
3 day(s) Pearson r : -0.35067730
3 day(s) QLIKE : 0.06371745
5 day(s) MAE : 2.28876307
5 day(s) RMSE : 2.82880988
5 day(s) R2 : -0.86529889
5 day(s) Pearson r : -0.31015622
5 day(s) QLIKE : 0.06501273
10 day(s) MAE : 2.28574774
10 day(s) RMSE : 2.82103970
10 day(s) R2 : -0.84179099
10 day(s) Pearson r : -0.27869712
10 day(s) QLIKE : 0.06794023
full horizon MAE : 2.28574774
full horizon RMSE : 2.82103970
full horizon R2 : -0.84179099
full horizon Pearson r : -0.27869712
full horizon QLIKE : 0.06794023
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/SP500/Custom_KAN_LSTM_H10.pkl
Saved y_true min=0.00323949, max=53.2315
Saved y_pred min=0.102263, max=2.4581
=== SP500 | H=20 | Custom_KAN (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using device: mps
Batch size for y: 3466
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2495, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.304044071790344
Min value: -0.29186160079947243
Checking X_time_train_core:
Shape: (2495, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -2.0363820510485593
Checking y_train_core (log_mse scaled):
Shape: (2495, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.040427392407918
Min value: -28.09802133319718
Checking X_price_val:
Shape: (277, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.189039765537116
Min value: -0.288808876061582
Checking X_time_val:
Shape: (277, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_val (log_mse scaled):
Shape: (277, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.546217024276721
Min value: -28.09802133319718
Checking X_price_test:
Shape: (694, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.65876861006644
Min value: -0.2909517549064528
Checking X_time_test:
Shape: (694, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_test (log_mse scaled):
Shape: (694, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.3777376348304156
Min value: -28.09802133319718
Epoch 001 | phase=1 | train_loss=4.9737 | val_main=1.239120
Epoch 002 | phase=1 | train_loss=4.8934 | val_main=1.242069
Epoch 003 | phase=1 | train_loss=4.7079 | val_main=1.242712
Epoch 004 | phase=1 | train_loss=4.7168 | val_main=1.242313
Epoch 005 | phase=1 | train_loss=4.6247 | val_main=1.241971
Epoch 006 | phase=1 | train_loss=4.6334 | val_main=1.241779
Epoch 007 | phase=1 | train_loss=4.6410 | val_main=1.242062
Epoch 008 | phase=1 | train_loss=4.5637 | val_main=1.241922
Epoch 009 | phase=1 | train_loss=4.5324 | val_main=1.241764
Epoch 010 | phase=1 | train_loss=4.5368 | val_main=1.241874
Epoch 011 | phase=1 | train_loss=4.4415 | val_main=1.241556
Epoch 012 | phase=1 | train_loss=4.3472 | val_main=1.241532
Epoch 013 | phase=1 | train_loss=4.3360 | val_main=1.241778
Epoch 014 | phase=1 | train_loss=4.2677 | val_main=1.241775
Epoch 015 | phase=1 | train_loss=4.1499 | val_main=1.241645
Epoch 016 | phase=0 | train_loss=5.0284 | val_main=1.179077
Epoch 017 | phase=0 | train_loss=4.8452 | val_main=0.874938
Epoch 018 | phase=0 | train_loss=4.7214 | val_main=0.877519
Epoch 019 | phase=0 | train_loss=4.6207 | val_main=0.838269
Epoch 020 | phase=0 | train_loss=4.6058 | val_main=0.867170
Epoch 021 | phase=0 | train_loss=4.5353 | val_main=0.857799
Epoch 022 | phase=0 | train_loss=4.3673 | val_main=0.839675
Epoch 023 | phase=0 | train_loss=4.3281 | val_main=0.833295
Epoch 024 | phase=0 | train_loss=4.3958 | val_main=0.851652
Epoch 025 | phase=0 | train_loss=4.2973 | val_main=0.879776
Epoch 026 | phase=0 | train_loss=4.2225 | val_main=0.897138
Epoch 027 | phase=0 | train_loss=4.1611 | val_main=0.859533
Epoch 028 | phase=0 | train_loss=4.0841 | val_main=0.876749
Epoch 029 | phase=0 | train_loss=4.2711 | val_main=0.856718
Epoch 030 | phase=0 | train_loss=4.1243 | val_main=0.911371
Epoch 031 | phase=2 | train_loss=0.7296 | val_main=0.925956
Epoch 032 | phase=2 | train_loss=0.7278 | val_main=0.904187
Epoch 033 | phase=2 | train_loss=0.7221 | val_main=0.959852
Early stopping triggered.
Parameters used in the single-fit model:
input_dim: 60
output_dim: 120
no_tasks: 6
knots: 8
spline_power: 3
hidden_dim: 128
hidden_layers: 3
dropout: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 50
min_epochs: 30
batch_size: 128
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
patience: 10
min_delta: 0.00010000
warmup_aux_epochs: 15
joint_epochs: 15
device: mps
verbose: True
checkpoint_path: None
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.59265912
1 day(s) RMSE : 2.25889898
1 day(s) R2 : 0.36363370
1 day(s) Pearson r : 0.62893084
1 day(s) QLIKE : 0.59107949
3 day(s) MAE : 0.63788351
3 day(s) RMSE : 2.60422716
3 day(s) R2 : 0.15423387
3 day(s) Pearson r : 0.48532110
3 day(s) QLIKE : 0.64034046
5 day(s) MAE : 0.69019368
5 day(s) RMSE : 2.89454720
5 day(s) R2 : -0.04478988
5 day(s) Pearson r : 0.35316023
5 day(s) QLIKE : 0.69370123
10 day(s) MAE : 0.72677359
10 day(s) RMSE : 3.01979056
10 day(s) R2 : -0.13700538
10 day(s) Pearson r : 0.23184946
10 day(s) QLIKE : 0.80403601
20 day(s) MAE : 0.72558347
20 day(s) RMSE : 2.97970693
20 day(s) R2 : -0.10688997
20 day(s) Pearson r : 0.16625964
20 day(s) QLIKE : 0.84332589
full horizon MAE : 0.72558347
full horizon RMSE : 2.97970693
full horizon R2 : -0.10688997
full horizon Pearson r : 0.16625964
full horizon QLIKE : 0.84332589
--- Task 2 ---
1 day(s) MAE : 0.06827997
1 day(s) RMSE : 0.07896653
1 day(s) R2 : -1.11212308
1 day(s) Pearson r : 0.33606127
1 day(s) QLIKE : 7.77364661
3 day(s) MAE : 0.06958906
3 day(s) RMSE : 0.07962734
3 day(s) R2 : -1.11874594
3 day(s) Pearson r : 0.32472623
3 day(s) QLIKE : 7.74911230
5 day(s) MAE : 0.07116127
5 day(s) RMSE : 0.08141705
5 day(s) R2 : -1.18445852
5 day(s) Pearson r : 0.31553888
5 day(s) QLIKE : 7.83587729
10 day(s) MAE : 0.07046533
10 day(s) RMSE : 0.08051037
10 day(s) R2 : -1.06799976
10 day(s) Pearson r : 0.31389389
10 day(s) QLIKE : 7.76093613
20 day(s) MAE : 0.06675511
20 day(s) RMSE : 0.07713142
20 day(s) R2 : -0.78764336
20 day(s) Pearson r : 0.31051068
20 day(s) QLIKE : 7.46997763
full horizon MAE : 0.06675511
full horizon RMSE : 0.07713142
full horizon R2 : -0.78764336
full horizon Pearson r : 0.31051068
full horizon QLIKE : 7.46997763
--- Task 3 ---
1 day(s) MAE : 0.36931482
1 day(s) RMSE : 0.46141793
1 day(s) R2 : -51.94970547
1 day(s) Pearson r : 0.03819064
1 day(s) QLIKE : 0.50332604
3 day(s) MAE : 0.36469382
3 day(s) RMSE : 0.45754593
3 day(s) R2 : -50.72097542
3 day(s) Pearson r : 0.04333010
3 day(s) QLIKE : 0.49241375
5 day(s) MAE : 0.36167734
5 day(s) RMSE : 0.45653595
5 day(s) R2 : -50.19064140
5 day(s) Pearson r : 0.03885968
5 day(s) QLIKE : 0.49848850
10 day(s) MAE : 0.36516796
10 day(s) RMSE : 0.45767247
10 day(s) R2 : -49.69016042
10 day(s) Pearson r : 0.02217279
10 day(s) QLIKE : 0.43881711
20 day(s) MAE : 0.36868618
20 day(s) RMSE : 0.45710436
20 day(s) R2 : -48.03911860
20 day(s) Pearson r : 0.00585912
20 day(s) QLIKE : 0.38045110
full horizon MAE : 0.36868618
full horizon RMSE : 0.45710436
full horizon R2 : -48.03911860
full horizon Pearson r : 0.00585912
full horizon QLIKE : 0.38045110
--- Task 4 ---
1 day(s) MAE : 0.03359643
1 day(s) RMSE : 0.04057323
1 day(s) R2 : -2.14220828
1 day(s) Pearson r : -0.14427692
1 day(s) QLIKE : 1.19686086
3 day(s) MAE : 0.03337784
3 day(s) RMSE : 0.04029279
3 day(s) R2 : -2.08617199
3 day(s) Pearson r : -0.14260967
3 day(s) QLIKE : 1.19917403
5 day(s) MAE : 0.03334293
5 day(s) RMSE : 0.04017122
5 day(s) R2 : -2.05508077
5 day(s) Pearson r : -0.14092023
5 day(s) QLIKE : 1.20096284
10 day(s) MAE : 0.03278610
10 day(s) RMSE : 0.03926086
10 day(s) R2 : -1.89527158
10 day(s) Pearson r : -0.13920179
10 day(s) QLIKE : 1.20054683
20 day(s) MAE : 0.03215742
20 day(s) RMSE : 0.03821614
20 day(s) R2 : -1.71848624
20 day(s) Pearson r : -0.15477417
20 day(s) QLIKE : 1.20293783
full horizon MAE : 0.03215742
full horizon RMSE : 0.03821614
full horizon R2 : -1.71848624
full horizon Pearson r : -0.15477417
full horizon QLIKE : 1.20293783
--- Task 5 ---
1 day(s) MAE : 0.02826577
1 day(s) RMSE : 0.03593542
1 day(s) R2 : -0.33199726
1 day(s) Pearson r : 0.02985382
1 day(s) QLIKE : 0.43866440
3 day(s) MAE : 0.02788894
3 day(s) RMSE : 0.03555566
3 day(s) R2 : -0.31091323
3 day(s) Pearson r : 0.04036136
3 day(s) QLIKE : 0.43407258
5 day(s) MAE : 0.02770049
5 day(s) RMSE : 0.03526943
5 day(s) R2 : -0.29712471
5 day(s) Pearson r : 0.05997424
5 day(s) QLIKE : 0.42581193
10 day(s) MAE : 0.02712309
10 day(s) RMSE : 0.03451402
10 day(s) R2 : -0.26086194
10 day(s) Pearson r : 0.09309808
10 day(s) QLIKE : 0.40185822
20 day(s) MAE : 0.02622822
20 day(s) RMSE : 0.03327206
20 day(s) R2 : -0.21242980
20 day(s) Pearson r : 0.13757761
20 day(s) QLIKE : 0.32614722
full horizon MAE : 0.02622822
full horizon RMSE : 0.03327206
full horizon R2 : -0.21242980
full horizon Pearson r : 0.13757761
full horizon QLIKE : 0.32614722
--- Task 6 ---
1 day(s) MAE : 2.74568902
1 day(s) RMSE : 3.38405508
1 day(s) R2 : -1.68437370
1 day(s) Pearson r : 0.01165561
1 day(s) QLIKE : 0.08719635
3 day(s) MAE : 2.73249850
3 day(s) RMSE : 3.37550674
3 day(s) R2 : -1.66383175
3 day(s) Pearson r : 0.01426538
3 day(s) QLIKE : 0.08657021
5 day(s) MAE : 2.72807683
5 day(s) RMSE : 3.37177647
5 day(s) R2 : -1.65007682
5 day(s) Pearson r : 0.01655395
5 day(s) QLIKE : 0.08642802
10 day(s) MAE : 2.71910150
10 day(s) RMSE : 3.36625976
10 day(s) R2 : -1.62251028
10 day(s) Pearson r : 0.02065933
10 day(s) QLIKE : 0.08650766
20 day(s) MAE : 2.69855944
20 day(s) RMSE : 3.35249890
20 day(s) R2 : -1.56608468
20 day(s) Pearson r : 0.02356740
20 day(s) QLIKE : 0.08696706
full horizon MAE : 2.69855944
full horizon RMSE : 3.35249890
full horizon R2 : -1.56608468
full horizon Pearson r : 0.02356740
full horizon QLIKE : 0.08696706
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/SP500/Custom_KAN_H20.pkl
Saved y_true min=0.00323949, max=53.2315
Saved y_pred min=0.137403, max=34.3349
=== SP500 | H=20 | Custom_KAN_LSTM (simple fit) | no_tasks=6 ===
[mode=log_mse] loss_type=mse, target_is_logvar=True, normalize_y=True
Using TensorFlow device: CPU
Batch size for y: 3466
Time steps for y: 20
Features for y: 6
Starting training without CV:
Checking X_price_train_core:
Shape: (2495, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 20.304044071790344
Min value: -0.29186160079947243
Checking X_time_train_core:
Shape: (2495, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -2.0363820510485593
Checking y_train_core (log_mse scaled):
Shape: (2495, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 4.040427392407918
Min value: -28.09802133319718
Checking X_price_val:
Shape: (277, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.189039765537116
Min value: -0.288808876061582
Checking X_time_val:
Shape: (277, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_val (log_mse scaled):
Shape: (277, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 2.546217024276721
Min value: -28.09802133319718
Checking X_price_test:
Shape: (694, 60, 1)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 14.65876861006644
Min value: -0.2909517549064528
Checking X_time_test:
Shape: (694, 60, 14)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 9.067132736068572
Min value: -1.7123832950667475
Checking y_test (log_mse scaled):
Shape: (694, 20, 6)
Contains NaN values: False
Contains +inf values: False
Contains -inf values: False
Contains any infinities: False
Max value: 3.3777376348304156
Min value: -28.09802133319718
[TF Scheduler] epoch=1 tf_ratio=1.000 -> TF=ON
Epoch 1/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 209s 515ms/step - loss: 0.7671 - val_loss: 1.2792 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=2 tf_ratio=0.947 -> TF=ON
Epoch 2/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 35s 442ms/step - loss: 0.5125 - val_loss: 1.5834 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=3 tf_ratio=0.895 -> TF=ON
Epoch 3/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 36s 466ms/step - loss: 0.4673 - val_loss: 1.4550 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=4 tf_ratio=0.842 -> TF=ON
Epoch 4/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 36s 460ms/step - loss: 0.4497 - val_loss: 1.5625 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=5 tf_ratio=0.789 -> TF=ON
Epoch 5/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 36s 462ms/step - loss: 0.4318 - val_loss: 1.6495 - learning_rate: 5.0000e-04
[TF Scheduler] epoch=6 tf_ratio=0.737 -> TF=ON
Epoch 6/20
78/78 ━━━━━━━━━━━━━━━━━━━━ 0s 461ms/step - loss: 0.4427
Epoch 6: ReduceLROnPlateau reducing learning rate to 0.0002500000118743628.
78/78 ━━━━━━━━━━━━━━━━━━━━ 37s 474ms/step - loss: 0.4132 - val_loss: 1.5208 - learning_rate: 5.0000e-04
Epoch 6: early stopping
Restoring model weights from the end of the best epoch: 1.
Parameters used in the single-fit model:
input_dim: 1
no_tasks: 6
task_output_dim: 1
pred_len: 20
hidden_layers: 2
hidden_dim: 32
dropout: 0.00000000
knots: 8
spline_power: 5
sub_kan_configs: None
teacher_forcing: True
tf_ratio_start: 1.00000000
tf_ratio_end: 0.00000000
lr: 0.00050000
l2_weight: 0.00001000
epochs: 20
batch_size: 32
verbose: 1
patience: 5
min_delta: 0.00010000
validation_split: 0.00000000
seed: 42
metrics: ('mse',)
run_eagerly: False
min_epochs: 10
output_dim: 6
loss_type: mse
target_is_logvar: True
nll_eps: 0.00000000
clamp_logvar_min: -20.00000000
clamp_logvar_max: 20.00000000
student_df: 5.00000000
Results for regular train/test evaluation (without CV):
--- Task 1 ---
1 day(s) MAE : 0.64767237
1 day(s) RMSE : 2.86762199
1 day(s) R2 : -0.02555123
1 day(s) Pearson r : 0.62927649
1 day(s) QLIKE : 1.00042933
3 day(s) MAE : 0.64801158
3 day(s) RMSE : 2.86842303
3 day(s) R2 : -0.02607466
3 day(s) Pearson r : 0.10963418
3 day(s) QLIKE : 0.99935628
5 day(s) MAE : 0.64892921
5 day(s) RMSE : 2.87032285
5 day(s) R2 : -0.02737544
5 day(s) Pearson r : 0.04115390
5 day(s) QLIKE : 1.00263756
10 day(s) MAE : 0.65086309
10 day(s) RMSE : 2.87203464
10 day(s) R2 : -0.02846193
10 day(s) Pearson r : 0.02489798
10 day(s) QLIKE : 1.00405926
20 day(s) MAE : 0.65170702
20 day(s) RMSE : 2.87246624
20 day(s) R2 : -0.02864902
20 day(s) Pearson r : 0.01759992
20 day(s) QLIKE : 1.00202103
full horizon MAE : 0.65170702
full horizon RMSE : 2.87246624
full horizon R2 : -0.02864902
full horizon Pearson r : 0.01759992
full horizon QLIKE : 1.00202103
--- Task 2 ---
1 day(s) MAE : 0.04241849
1 day(s) RMSE : 0.06653976
1 day(s) R2 : -0.49966939
1 day(s) Pearson r : 0.54841295
1 day(s) QLIKE : 2.60336329
3 day(s) MAE : 0.05068332
3 day(s) RMSE : 0.07404670
3 day(s) R2 : -0.83217050
3 day(s) Pearson r : 0.00795080
3 day(s) QLIKE : 6.19467582
5 day(s) MAE : 0.05261835
5 day(s) RMSE : 0.07590526
5 day(s) R2 : -0.89870221
5 day(s) Pearson r : 0.00294387
5 day(s) QLIKE : 7.06057813
10 day(s) MAE : 0.05454973
10 day(s) RMSE : 0.07805465
10 day(s) R2 : -0.94376780
10 day(s) Pearson r : -0.00228189
10 day(s) QLIKE : 6.64302809
20 day(s) MAE : 0.05654097
20 day(s) RMSE : 0.08073530
20 day(s) R2 : -0.95859713
20 day(s) Pearson r : -0.00709750
20 day(s) QLIKE : 5.04555404
full horizon MAE : 0.05654097
full horizon RMSE : 0.08073530
full horizon R2 : -0.95859713
full horizon Pearson r : -0.00709750
full horizon QLIKE : 5.04555404
--- Task 3 ---
1 day(s) MAE : 0.41880436
1 day(s) RMSE : 0.42342587
1 day(s) R2 : -43.58917075
1 day(s) Pearson r : 0.30218690
1 day(s) QLIKE : 0.00241607
3 day(s) MAE : 0.49828233
3 day(s) RMSE : 0.50801501
3 day(s) R2 : -62.76030656
3 day(s) Pearson r : 0.03526802
3 day(s) QLIKE : 0.02609402
5 day(s) MAE : 0.61592835
5 day(s) RMSE : 0.63934119
5 day(s) R2 : -99.39358914
5 day(s) Pearson r : 0.01747071
5 day(s) QLIKE : 0.36339575
10 day(s) MAE : 0.75091246
10 day(s) RMSE : 0.77387645
10 day(s) R2 : -143.92968531
10 day(s) Pearson r : 0.01527902
10 day(s) QLIKE : 0.79670806
20 day(s) MAE : 0.82018370
20 day(s) RMSE : 0.83498519
20 day(s) R2 : -162.63246067
20 day(s) Pearson r : 0.01846285
20 day(s) QLIKE : 0.52762033
full horizon MAE : 0.82018370
full horizon RMSE : 0.83498519
full horizon R2 : -162.63246067
full horizon Pearson r : 0.01846285
full horizon QLIKE : 0.52762033
--- Task 4 ---
1 day(s) MAE : 0.02104220
1 day(s) RMSE : 0.02389576
1 day(s) R2 : -0.08992699
1 day(s) Pearson r : 0.23852544
1 day(s) QLIKE : 1.08955013
3 day(s) MAE : 0.02114438
3 day(s) RMSE : 0.02406950
3 day(s) R2 : -0.10128434
3 day(s) Pearson r : 0.01494281
3 day(s) QLIKE : 1.09256870
5 day(s) MAE : 0.02019343
5 day(s) RMSE : 0.02363673
5 day(s) R2 : -0.05771181
5 day(s) Pearson r : 0.02990537
5 day(s) QLIKE : 1.10165721
10 day(s) MAE : 0.02007690
10 day(s) RMSE : 0.02353760
10 day(s) R2 : -0.04062451
10 day(s) Pearson r : 0.00665258
10 day(s) QLIKE : 1.10060525
20 day(s) MAE : 0.02062980
20 day(s) RMSE : 0.02456458
20 day(s) R2 : -0.12318755
20 day(s) Pearson r : -0.06888730
20 day(s) QLIKE : 1.13319694
full horizon MAE : 0.02062980
full horizon RMSE : 0.02456458
full horizon R2 : -0.12318755
full horizon Pearson r : -0.06888730
full horizon QLIKE : 1.13319694
--- Task 5 ---
1 day(s) MAE : 0.02570228
1 day(s) RMSE : 0.03114222
1 day(s) R2 : -0.00036142
1 day(s) Pearson r : 0.05697330
1 day(s) QLIKE : 0.42942536
3 day(s) MAE : 0.02606636
3 day(s) RMSE : 0.03118846
3 day(s) R2 : -0.00865848
3 day(s) Pearson r : -0.00713684
3 day(s) QLIKE : 0.42699054
5 day(s) MAE : 0.02606495
5 day(s) RMSE : 0.03115202
5 day(s) R2 : -0.01194579
5 day(s) Pearson r : -0.02142971
5 day(s) QLIKE : 0.42093034
10 day(s) MAE : 0.02640165
10 day(s) RMSE : 0.03116828
10 day(s) R2 : -0.02825788
10 day(s) Pearson r : -0.01420625
10 day(s) QLIKE : 0.39965849
20 day(s) MAE : 0.02791691
20 day(s) RMSE : 0.03473274
20 day(s) R2 : -0.32122042
20 day(s) Pearson r : 0.01577365
20 day(s) QLIKE : 0.34276729
full horizon MAE : 0.02791691
full horizon RMSE : 0.03473274
full horizon R2 : -0.32122042
full horizon Pearson r : 0.01577365
full horizon QLIKE : 0.34276729
--- Task 6 ---
1 day(s) MAE : 2.13753614
1 day(s) RMSE : 2.64224300
1 day(s) R2 : -0.63649107
1 day(s) Pearson r : 0.06873530
1 day(s) QLIKE : 0.05753544
3 day(s) MAE : 2.05253475
3 day(s) RMSE : 2.53687454
3 day(s) R2 : -0.50461994
3 day(s) Pearson r : -0.00379009
3 day(s) QLIKE : 0.05828848
5 day(s) MAE : 1.95710473
5 day(s) RMSE : 2.43264542
5 day(s) R2 : -0.37942640
5 day(s) Pearson r : 0.00202893
5 day(s) QLIKE : 0.05972980
10 day(s) MAE : 1.91800748
10 day(s) RMSE : 2.38361609
10 day(s) R2 : -0.31490557
10 day(s) Pearson r : -0.00560522
10 day(s) QLIKE : 0.05980990
20 day(s) MAE : 1.82004543
20 day(s) RMSE : 2.28522796
20 day(s) R2 : -0.19231980
20 day(s) Pearson r : 0.03530988
20 day(s) QLIKE : 0.06126600
full horizon MAE : 1.82004543
full horizon RMSE : 2.28522796
full horizon R2 : -0.19231980
full horizon Pearson r : 0.03530988
full horizon QLIKE : 0.06126600
Best model (no-CV) saved to: /Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v8/Saved_objects/hierarchical_results_saved_object/SP500/Custom_KAN_LSTM_H20.pkl
Saved y_true min=0.00323949, max=53.2315
Saved y_pred min=0.333764, max=0.392152
In [836]:
hierarchical_frames = build_metric_frames(hierarchical_results_store, outer_horizon="full", pretty_print=True)
os.makedirs("results", exist_ok=True)
export_metrics(hierarchical_results_store, "results/hierarchical_task_all.csv", outer_horizon="full")
export_metrics(hierarchical_results_store, "results/hierarchical_task_all.txt", outer_horizon="full")
=== AAPL | Custom_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.318615 2.487262 2.555042 2.545927
Pearson r 0.370403 0.284447 0.208310 0.186543
QLIKE 0.368335 0.432096 0.459599 0.471674
R2 0.026075 -0.002711 -0.023170 -0.015726
RMSE 8.529161 8.951316 9.079594 9.065810
=== AAPL | Custom_KAN_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.439325 2.733695 2.576263 2.625173
Pearson r 0.451438 0.006517 -0.003502 -0.002359
QLIKE 0.535380 0.557882 0.587486 0.602212
R2 -0.018415 -0.007937 -0.040699 -0.050278
RMSE 8.721797 8.974611 9.157042 9.218717
=== MSFT | Custom_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.065696 2.146139 2.139506 2.173128
Pearson r 0.049339 0.002476 -0.019362 -0.014000
QLIKE 0.480419 0.528343 0.519135 0.509428
R2 -0.013146 -0.029190 -0.033569 -0.030169
RMSE 6.746685 6.800049 6.814750 7.008772
=== MSFT | Custom_KAN_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 1.969583 2.149781 2.345673 2.158551
Pearson r 0.262519 0.164579 0.006053 0.006947
QLIKE 0.379534 0.433327 0.474432 0.493606
R2 0.030454 0.004908 -0.003006 -0.022197
RMSE 6.599920 6.686456 6.713235 6.981600
=== GE | Custom_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.123006 4.326514 4.459059 4.492792
Pearson r 0.012156 0.001579 0.005824 -0.021266
QLIKE 0.691330 0.743176 0.762849 0.783562
R2 -0.010290 -0.015666 -0.015221 -0.023053
RMSE 27.566934 27.638597 27.634563 27.741693
=== GE | Custom_KAN_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.870943 4.523866 4.465643 4.318339
Pearson r 0.046869 -0.000122 -0.000272 -0.002192
QLIKE 0.684368 0.695262 0.683893 0.687734
R2 -0.001825 -0.005724 -0.006250 -0.011102
RMSE 27.451193 27.502984 27.512204 27.579184
=== BAC | Custom_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.218764 2.315760 2.377755 2.360622
Pearson r 0.360256 0.162822 -0.044596 -0.054259
QLIKE 0.339648 0.423011 0.433186 0.430008
R2 0.090281 0.009696 -0.010677 -0.011761
RMSE 7.403981 7.728616 7.808080 7.810978
=== BAC | Custom_KAN_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 1.984658 2.174726 2.300111 2.308275
Pearson r 0.437762 0.164894 -0.001345 0.000910
QLIKE 0.272968 0.391940 0.439506 0.422809
R2 0.160222 -0.008393 -0.022134 -0.012223
RMSE 7.113674 7.798882 7.852211 7.812762
=== C | Custom_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.978088 2.903246 2.887941 2.952660
Pearson r 0.483365 0.142331 0.083177 0.034534
QLIKE 0.274388 0.416982 0.429684 0.436819
R2 0.096491 -0.009518 -0.027654 -0.028733
RMSE 8.972150 9.522281 9.618667 9.627530
=== C | Custom_KAN_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 2.713545 2.853104 2.939848 2.934725
Pearson r 0.479963 0.179852 0.010335 0.004627
QLIKE 0.311682 0.405071 0.457465 0.432778
R2 0.179079 -0.039958 -0.053677 -0.024680
RMSE 8.552261 9.664776 9.739689 9.608544
=== BTCUSDT | Custom_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 3.996892 4.508294 4.674688 4.774879
Pearson r 0.378138 0.162538 0.044634 -0.003729
QLIKE 0.255808 0.367620 0.404387 0.419134
R2 0.065903 -0.027110 -0.075247 -0.080072
RMSE 8.878098 9.308816 9.525601 9.545265
=== BTCUSDT | Custom_KAN_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 4.456452 4.788267 5.306979 5.059873
Pearson r 0.311768 0.176070 0.111611 0.004598
QLIKE 0.323612 0.370649 0.419227 0.424784
R2 0.011846 -0.125630 -0.230204 -0.118786
RMSE 9.131376 9.745045 10.188888 9.714826
=== EURUSD | Custom_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.088158 0.092045 0.096391 0.099675
Pearson r 0.599808 0.509232 0.477334 0.424556
QLIKE 0.428952 0.406325 0.426089 0.474492
R2 0.343572 0.243549 0.198655 0.143635
RMSE 0.173094 0.184059 0.188544 0.193770
=== EURUSD | Custom_KAN_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.107669 0.119398 0.116493 0.140131
Pearson r 0.413050 0.369016 0.308140 0.005337
QLIKE 0.489902 0.522251 0.519447 0.596096
R2 0.104308 -0.031741 -0.048783 -0.326733
RMSE 0.202194 0.214957 0.215698 0.241184
=== GOLD | Custom_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.309377 0.311183 0.317904 0.346815
Pearson r 0.447552 0.478758 0.488464 0.401298
QLIKE 0.513822 0.488001 0.509680 0.560789
R2 0.173158 0.192401 0.208005 0.097801
RMSE 0.595884 0.589995 0.587553 0.631780
=== GOLD | Custom_KAN_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.408012 0.454040 0.450157 0.426175
Pearson r 0.357309 0.314968 0.065464 0.008281
QLIKE 0.499330 0.505073 0.536587 0.581421
R2 -0.082473 -0.240777 -0.283731 -0.078860
RMSE 0.681803 0.731303 0.748037 0.690872
=== SP500 | Custom_KAN | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.484774 0.650754 0.725642 0.725583
Pearson r 0.652686 0.344270 0.236617 0.166260
QLIKE 0.443646 0.669757 0.792334 0.843326
R2 0.370255 0.019755 -0.202000 -0.106890
RMSE 2.247116 2.803713 3.104901 2.979707
=== SP500 | Custom_KAN_LSTM | using all outer horizons [1, 5, 10, 20] | task=Task 1 | full-only ===
H1 H5 H10 H20
MAE 0.581925 0.642370 0.658522 0.651707
Pearson r 0.471433 0.235388 0.172967 0.017600
QLIKE 0.616883 0.798351 0.903159 1.002021
R2 0.081577 0.018913 0.009762 -0.028649
RMSE 2.713717 2.804916 2.818158 2.872466
Out[836]:
{'mode': 'text',
'path': '/Users/silviumatu/Desktop/Code/Python/Licenta/Licenta_INFO_v9/results/hierarchical_task_all.txt',
'sections': 18}
In [837]:
saved_hierarchical_plots = plot_metric_vs_horizon(
hierarchical_frames,
tickers=None,
models=None,
metrics=None,
include_full=True,
save_dir="plots/hierarchical",
show=True
)
print("Saved", len(saved), "charts to plots/hierarchical")
saved_hierarchical_y_plots = plot_h1_full_from_results(
hierarchical_results_store,
tickers=None,
models=None,
save_dir="plots/hierarchical/y_plots",
show=True,
verbose=True
)
Saved 10 charts to plots/hierarchical [AAPL H=1] Custom_KAN: blob_shape=(404, 1, 2), yt=ok, yp=ok [AAPL H=1] Custom_KAN_LSTM: blob_shape=(404, 1, 2), yt=ok, yp=ok [AAPL H=1] actual=YES [AAPL H=1] Aligned series head (first 50 of 404 rows): Actual Custom_KAN_pred Custom_KAN_LSTM_pred 0.934885 1.19949 2.41671 1.65221 1.1972 2.41671 3.00982 1.1994 2.41671 7.17364 1.21536 2.41671 2.32415 1.30248 2.41672 1.8112 1.2985 2.41671 2.83463 1.29008 2.41672 2.29244 1.30762 2.41672 1.60917 1.31178 2.41672 1.9016 1.28338 2.41672 2.02761 1.28975 2.41672 0.882809 1.29439 2.41672 1.75996 1.28112 2.41671 1.93843 1.27651 2.41671 1.30271 1.289 2.41671 1.66381 1.2949 2.41671 1.7326 1.29491 2.41671 1.90607 1.27515 2.41671 2.70629 1.27923 2.41671 1.98734 1.29793 2.41671 4.17835 1.28181 2.41671 2.23484 1.34233 2.41672 2.5908 1.34213 2.41672 1.38316 1.35036 2.41672 3.07923 1.33739 2.41672 2.16603 1.35053 2.41672 1.08108 1.35272 2.41672 1.05538 1.33887 2.41672 1.56663 1.32561 2.41671 1.20165 1.3273 2.41671 3.78232 1.28792 2.41671 1.6213 1.33214 2.41672 2.03959 1.3245 2.41671 2.6352 1.32788 2.41672 1.42274 1.33597 2.41672 2.873 1.33285 2.41671 1.51033 1.3382 2.41672 2.09779 1.31948 2.41671 4.03363 1.3201 2.41672 2.80041 1.38374 2.41672 2.38801 1.36705 2.41672 2.0338 1.35581 2.41672 2.19524 1.37625 2.41672 11.9895 1.36734 2.41672 3.66056 1.58912 2.41674 1.3156 1.57306 2.41672 0.963659 1.53643 2.41673 1.08602 1.51526 2.41672 1.29415 1.46083 2.41672 0.888571 1.43817 2.41672
[MSFT H=1] Custom_KAN: blob_shape=(404, 1, 2), yt=ok, yp=ok [MSFT H=1] Custom_KAN_LSTM: blob_shape=(404, 1, 2), yt=ok, yp=ok [MSFT H=1] actual=YES [MSFT H=1] Aligned series head (first 50 of 404 rows): Actual Custom_KAN_pred Custom_KAN_LSTM_pred 1.62646 2.22021 1.45818 1.38102 2.15964 1.44234 1.45815 2.12555 1.44359 1.91787 2.13263 1.43549 1.81463 2.15343 1.4279 1.90777 2.06604 1.41948 1.13204 2.13591 1.4157 2.16207 2.14262 1.38857 1.60526 2.11671 1.39645 1.36873 2.12241 1.39064 1.54924 2.05999 1.37638 1.38923 2.10711 1.37061 1.71635 2.11613 1.36279 7.14713 2.09352 1.36281 2.33716 2.11699 1.49179 2.91188 2.1708 1.51338 2.05814 2.11762 1.53022 2.23013 2.11176 1.52305 2.9262 2.15948 1.51272 2.44337 2.12708 1.53692 3.72187 2.19136 1.53591 2.39093 2.14672 1.59241 3.38264 2.0828 1.58948 1.96965 2.13461 1.61796 5.97013 2.05984 1.58917 3.19073 2.13934 1.98441 1.62044 2.22036 2.17582 1.86668 2.09107 1.7502 1.46844 2.12469 1.62203 1.78506 2.04927 1.5676 2.02385 2.1187 1.5204 2.15305 2.06661 1.49572 3.35866 2.04965 1.48094 4.75959 1.93655 1.51698 2.26205 1.9536 1.62801 2.5589 1.9319 1.61949 28.4576 1.90207 1.59337 5.29249 2.45396 5.59901 5.58633 2.58595 5.63937 2.95631 2.43606 5.32488 2.9314 2.3637 4.7406 2.15893 2.38768 4.35883 2.50397 2.44829 3.69189 2.31466 2.40853 3.42803 1.9771 2.40624 3.21562 1.16435 2.39158 2.89155 1.01889 2.15879 2.53758 1.35823 2.2645 1.85151 1.42899 2.19558 1.5984 1.02177 2.21435 1.52827
[GE H=1] Custom_KAN: blob_shape=(404, 1, 2), yt=ok, yp=ok [GE H=1] Custom_KAN_LSTM: blob_shape=(404, 1, 2), yt=ok, yp=ok [GE H=1] actual=YES [GE H=1] Aligned series head (first 50 of 404 rows): Actual Custom_KAN_pred Custom_KAN_LSTM_pred 1.5135 2.62958 4.74942 1.72313 2.6234 4.74942 1.67254 2.60952 4.74942 1.66078 2.62365 4.74942 0.769403 2.61986 4.74942 1.04217 2.61249 4.74942 3.65719 2.63235 4.74942 1.16201 2.63027 4.74942 4.30547 2.6139 4.74942 1.37445 2.58896 4.74942 1.38507 2.58454 4.74942 1.13075 2.5748 4.74942 1.22114 2.57874 4.74942 1.99537 2.56524 4.74942 2.85347 2.5721 4.74942 0.960334 2.58125 4.74942 2.10725 2.57416 4.74942 2.0909 2.58061 4.74942 1.68286 2.58339 4.74942 3.11206 2.58157 4.74942 2.59462 2.60752 4.74942 4.00565 2.57793 4.74942 2.58083 2.59204 4.74942 1.92091 2.58972 4.74942 3.17544 2.57879 4.74942 2.71117 2.59879 4.74942 1.39353 2.60273 4.74942 2.01672 2.59915 4.74942 2.69781 2.60441 4.74942 2.05652 2.60025 4.74942 2.32011 2.60447 4.74942 1.69183 2.61983 4.74942 3.70289 2.6081 4.74942 3.22004 2.57598 4.74942 4.1739 2.58382 4.74942 3.9362 2.58502 4.74943 20.9567 2.59216 4.74943 4.07076 2.67048 4.74944 7.35977 2.67369 4.74945 4.59998 2.68347 4.74945 3.23707 2.71246 4.74945 3.27846 2.68517 4.74945 7.43036 2.70505 4.74944 2.44681 2.70852 4.74944 1.00844 2.70774 4.74944 2.67284 2.68832 4.74944 1.85009 2.70225 4.74943 2.73625 2.68353 4.74943 1.99492 2.69937 4.74943 1.32531 2.69042 4.74943
[BAC H=1] Custom_KAN: blob_shape=(404, 1, 2), yt=ok, yp=ok [BAC H=1] Custom_KAN_LSTM: blob_shape=(404, 1, 2), yt=ok, yp=ok [BAC H=1] actual=YES [BAC H=1] Aligned series head (first 50 of 404 rows): Actual Custom_KAN_pred Custom_KAN_LSTM_pred 2.28991 1.79203 1.62009 2.58025 1.83137 1.65437 1.43685 1.81755 1.70708 1.40532 1.82192 1.66887 1.62484 1.73964 1.63696 2.44658 1.71432 1.63278 1.56274 1.76774 1.67399 2.04251 1.7558 1.65693 1.92693 1.81258 1.66462 1.4117 1.77126 1.67169 1.18859 1.8158 1.64326 1.43173 1.77945 1.61318 1.53413 1.76102 1.60624 2.43259 1.85296 1.60542 1.83194 1.81342 1.64349 1.89469 1.77622 1.64915 2.37737 1.75054 1.65103 2.50765 1.68169 1.68564 3.17295 1.76383 1.72964 2.17531 1.7835 1.84894 3.21212 1.78605 1.83289 2.57597 1.86529 1.92585 2.49883 1.85048 1.92135 2.39942 1.87684 1.90979 5.25542 1.87626 1.90109 5.78329 2.08839 2.42634 2.47379 2.2391 3.1094 2.97723 2.18929 2.65656 2.52012 2.15886 2.62751 10.1661 2.10366 2.42103 3.21457 2.60243 4.42903 8.54499 2.5696 3.2979 4.49535 2.99583 4.14051 4.60281 3.03314 3.79923 3.86461 2.92587 3.49156 3.30898 2.8859 3.39191 2.6722 3.02325 3.20113 2.11101 2.79741 2.96784 4.7242 2.70799 2.75853 3.81735 2.77711 3.37629 2.94351 2.79217 3.12513 2.42652 2.65585 2.86305 2.96869 2.55386 2.64965 2.88004 2.61336 2.66233 7.20205 2.52962 2.54334 3.87685 2.8543 3.76743 2.27628 2.89852 3.20411 2.08444 2.86025 2.78926 3.13418 2.51228 2.55478 3.92102 2.4845 2.66515
[C H=1] Custom_KAN: blob_shape=(404, 1, 2), yt=ok, yp=ok [C H=1] Custom_KAN_LSTM: blob_shape=(404, 1, 2), yt=ok, yp=ok [C H=1] actual=YES [C H=1] Aligned series head (first 50 of 404 rows): Actual Custom_KAN_pred Custom_KAN_LSTM_pred 1.4361 1.61315 1.55255 2.4089 1.72313 1.55103 1.70682 1.83608 1.5575 1.3802 1.73085 1.55482 1.42651 1.49696 1.55232 2.28623 1.39389 1.55051 3.09043 2.50494 1.55637 3.47712 2.0521 1.5676 2.10921 2.35139 1.5857 3.05691 1.67253 1.57905 1.32975 2.75387 1.59699 1.00699 1.53955 1.57376 1.56839 1.61568 1.56194 1.79976 1.36946 1.55885 3.40343 1.55363 1.55891 2.66085 1.60086 1.57622 3.50115 1.44726 1.57933 2.93494 1.81702 1.60817 3.77812 1.952 1.62002 2.79098 2.71382 1.68594 2.48685 2.30216 1.68393 1.80238 2.04674 1.66663 4.05301 2.24658 1.62126 2.247 2.37602 1.71181 3.71379 1.848 1.66661 4.11532 2.1277 1.73817 3.12293 2.75956 1.87834 2.66388 2.16317 1.87992 5.69437 2.17952 1.81883 9.77504 3.75105 2.18227 4.82663 5.36929 3.08052 5.00513 4.23115 3.0356 4.18801 4.56509 3.15222 2.87579 3.96287 3.11619 3.95956 3.24976 2.82096 2.62006 3.45699 2.87351 1.79606 2.8276 2.67658 2.26218 2.56078 2.5467 6.96561 2.44397 2.32597 3.21371 3.71038 2.91514 3.60813 2.63559 2.69264 2.14357 2.7847 2.66517 2.83844 2.43422 2.37193 2.47451 2.30596 2.26827 5.06039 2.2874 2.18536 2.61845 3.33734 2.29653 1.97985 2.94661 2.21067 2.24 2.61437 2.04176 5.28614 2.45761 1.90302 1.59823 3.55161 2.18998
[BTCUSDT H=1] Custom_KAN: blob_shape=(480, 1, 2), yt=ok, yp=ok [BTCUSDT H=1] Custom_KAN_LSTM: blob_shape=(480, 1, 2), yt=ok, yp=ok [BTCUSDT H=1] actual=YES [BTCUSDT H=1] Aligned series head (first 50 of 480 rows): Actual Custom_KAN_pred Custom_KAN_LSTM_pred 6.92748 3.48175 6.12203 1.26781 2.58393 6.40278 0.752504 1.99679 3.86965 10.0004 2.81132 3.38879 8.45084 6.45316 4.59524 5.32244 5.79103 4.34959 2.80991 4.04693 3.77076 5.32016 2.61173 3.52791 1.27538 1.79704 3.69467 3.3705 1.76765 2.91812 4.30039 2.74818 2.8623 4.5337 3.97239 2.84491 6.04033 3.00672 2.75938 4.38553 3.73992 2.93889 3.19392 2.86888 2.774 0.828641 2.52012 2.62536 1.72163 2.01967 2.44806 3.29458 2.98017 2.43211 2.52403 3.66452 2.49009 3.66699 3.8424 2.43723 3.61632 2.97193 2.48626 8.21189 2.92507 2.5068 2.21355 2.05444 3.01136 2.72723 2.25223 2.49819 6.91185 2.65374 2.47909 7.1909 4.13726 2.99248 6.48986 4.29747 3.14206 6.82806 4.16423 3.05829 4.04652 3.53064 3.35959 3.11243 2.03647 2.85748 2.5171 2.03228 2.6793 2.75687 2.65182 2.59871 6.87132 3.79279 2.5427 4.43353 4.42485 2.99567 4.92989 3.75903 2.72238 3.08011 2.4104 2.74078 1.10511 1.41326 2.61837 1.37997 1.66678 2.43203 6.66109 2.1173 2.41466 13.9372 4.2446 2.7803 26.0966 7.07464 4.92825 18.2381 10.0231 9.8953 8.3292 9.26931 7.90272 2.68908 4.6492 6.50934 3.12688 4.09763 5.69295 23.8375 6.02919 4.98481 50.1271 13.2263 11.6708 25.8546 20.0603 24.8581 9.41359 16.4818 18.5387 16.7212 11.4972 11.5373
[EURUSD H=1] Custom_KAN: blob_shape=(757, 1, 2), yt=ok, yp=ok
[EURUSD H=1] Custom_KAN_LSTM: blob_shape=(757, 1, 2), yt=ok, yp=ok
[EURUSD H=1] actual=YES
[EURUSD H=1] Aligned series head (first 50 of 757 rows):
Actual Custom_KAN_pred Custom_KAN_LSTM_pred
0.739859 0.607651 0.268178
0.743822 0.386594 0.270754
1.02363 0.348778 0.271516
0.0199331 0.0559565 0.272158
0.52303 0.389376 0.270718
0.355217 0.279801 0.27113
0.417086 0.728095 0.270513
0.266624 0.533505 0.270521
0.312901 0.433327 0.269751
0.00443731 0.00655468 0.269215
0.261753 0.665032 0.266524
0.788937 0.235256 0.263815
0.598342 0.725181 0.267831
0.723261 0.571858 0.270252
0.341589 0.427038 0.271354
0.0137579 0.0370461 0.270738
0.29405 0.328636 0.26881
0.513047 0.423259 0.267813
0.252839 0.359412 0.268583
0.305402 0.569082 0.268119
0.226156 0.236479 0.267583
0.00963577 0.038558 0.266194
0.0784947 0.199045 0.261211
0.199036 0.366473 0.247567
0.171632 0.373385 0.218563
0.201223 0.284305 0.186591
0.353565 0.216012 0.170578
0.0670925 0.0234985 0.157734
0.2403 0.307552 0.156828
0.443555 0.447809 0.158891
0.418858 0.396097 0.152236
0.378402 0.396133 0.167059
0.644091 0.372788 0.189981
0.0152947 0.0342811 0.243805
0.272258 0.213232 0.256396
0.23458 0.262449 0.25859
0.249941 0.342523 0.256754
0.705648 0.290315 0.253734
0.293732 0.267186 0.261782
0.00988965 0.0121514 0.265145
0.164063 0.121439 0.262569
0.414405 0.45376 0.256583
0.477121 0.366059 0.253374
0.264156 0.387199 0.258174
0.210154 0.219699 0.261001
0.0136265 0.0190497 0.260359
0.265973 0.214395 0.252331
0.234507 0.450987 0.238072
0.284101 0.380328 0.220388
0.226239 0.263445 0.207918
[GOLD H=1] Custom_KAN: blob_shape=(1107, 1, 2), yt=ok, yp=ok [GOLD H=1] Custom_KAN_LSTM: blob_shape=(1107, 1, 2), yt=ok, yp=ok [GOLD H=1] actual=YES [GOLD H=1] Aligned series head (first 50 of 1107 rows): Actual Custom_KAN_pred Custom_KAN_LSTM_pred 0.983207 0.701758 0.273914 0.0725466 0.0823518 0.274401 0.365228 0.481668 0.273761 0.675363 0.720237 0.273848 1.19577 0.618304 0.273905 0.396614 0.384839 0.274294 0.569428 0.38176 0.273966 0.0394832 0.734734 0.274082 0.540289 0.624824 0.273594 0.645375 0.658961 0.273806 0.596069 0.745082 0.273801 0.496734 0.599063 0.273755 0.986594 0.583062 0.273703 0.0170706 0.0170588 0.273946 0.388559 0.656164 0.273456 0.616087 0.67779 0.273773 0.603893 0.69176 0.273831 0.739671 0.602324 0.273768 0.832053 0.626191 0.27371 0.0110286 0.0288489 0.273781 0.506412 0.585677 0.273382 0.392005 0.649655 0.27367 0.80787 0.665953 0.273761 0.535457 0.655078 0.273809 0.837414 0.648757 0.273604 0.0220968 0.0186792 0.273725 0.498523 0.622294 0.273425 0.472977 0.547215 0.273758 1.61389 0.676914 0.273823 0.641823 0.612712 0.274545 0.600193 0.551905 0.275096 0.0226182 0.0473576 0.276448 0.411008 0.669144 0.275287 0.743739 0.528199 0.274043 0.369762 0.724526 0.274035 0.57285 0.725252 0.273748 1.20635 0.538979 0.273812 0.0659951 0.0290663 0.274545 1.33651 0.58866 0.273808 0.970258 0.660859 0.275569 0.553171 0.573039 0.279112 0.193903 0.509898 0.282449 1.26963 0.638561 0.281787 0.222961 0.0525852 0.284728 0.483934 0.638752 0.286731 1.3403 0.674858 0.283263 1.06602 0.643749 0.287496 0.692459 0.412385 0.302146 0.757949 0.467479 0.338778 0.0295473 0.118015 0.376959
[SP500 H=1] Custom_KAN: blob_shape=(694, 1, 2), yt=ok, yp=ok [SP500 H=1] Custom_KAN_LSTM: blob_shape=(694, 1, 2), yt=ok, yp=ok [SP500 H=1] actual=YES [SP500 H=1] Aligned series head (first 50 of 694 rows): Actual Custom_KAN_pred Custom_KAN_LSTM_pred 1.0351 1.58101 0.87748 1.03491 1.63305 0.833625 0.818223 1.19753 0.803877 0.0217497 0.274999 0.77482 0.502587 0.502349 0.618308 2.13502 0.584809 0.608759 0.903485 1.3048 0.802115 0.898743 1.37717 0.762571 0.904151 0.859937 0.737796 0.0405714 0.212866 0.732258 0.113815 0.571309 0.592389 0.60793 0.851078 0.574063 0.92855 0.811166 0.552216 0.794209 0.83568 0.593118 0.910428 0.587703 0.595495 0.0104435 0.139828 0.612359 0.610551 0.32853 0.503915 0.532918 1.02085 0.472238 0.832645 0.996561 0.468645 0.902649 0.934651 0.541841 0.540703 0.87642 0.586514 0.0320152 0.0433334 0.569987 0.44739 0.366324 0.399505 1.17379 0.790062 0.364112 0.784725 0.951741 0.552952 1.19858 0.999232 0.584269 3.1834 0.636494 0.632736 1.95774 0.28704 0.993249 5.83359 1.2685 1.04276 1.91438 2.50608 1.26875 2.82295 2.34888 1.206 1.97836 2.17837 1.29349 1.67848 2.12045 1.27739 0.231827 0.890928 1.22371 1.99306 1.98042 1.05096 0.595525 1.56959 1.04029 2.0958 1.39722 0.932883 1.44715 1.4858 1.00623 1.8779 1.39319 0.987773 0.0836623 0.390448 1.00878 0.723351 1.3374 0.887578 0.463055 0.803606 0.835048 0.331959 1.16216 0.765568 0.355102 1.03631 0.660686 0.359515 0.73861 0.606783 0.0698865 0.0532842 0.592606 0.418851 0.588901 0.436196 0.479569 0.540078 0.377433 0.468942 0.475543 0.364782 0.278331 0.642221 0.353861
